Update run_linter.sh

Fix spelling error `notebooked` → `notebooks`

.github/workflows/linter/run_linter.sh

@@ -58,7 +58,7 @@ done
 # Only check notebooks in test folders modified in this pull request.
 # Note: Use process substitution to persist the data in the array
 if [ ${#notebooks[@]} -eq 0 ]; then
-    echo "Checking for changed notebooked using git"
+    echo "Checking for changed notebooks using git"
     while read -r file || [ -n "$line" ]; do
         notebooks+=("$file")
     done < <(git diff --name-only main... | grep '\.ipynb$')

community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==1.13.1
+torch==2.2.0
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/vertex_model_garden/benchmarking_reports/pytorch_openllama_7b_finetune_benchmark_report.md

@@ -1,227 +0,0 @@
-# Benchmark report on fine tuning the OpenLLaMA 7B model on Google Cloud Vertex Model Garden
-
-Gary Wei, Software Engineer, Google Cloud
-Dustin Luong, Software Engineer, Google Cloud
-Changyu Zhu, Software Engineer, Google Cloud
-Genquan Duan, Software Engineer, Google Cloud
-
-## Introduction
-
-Fine-tuning of LLMs can be non-trivial to find an optimal configuration of
-machine types, training parameters, and other hyperparameters that achieves a
-good balance between cost efficiency and model performance. To facilitate users
-in conducting tuning experiments, this report benchmarks OpenLLaMA 7B
-fine-tuning on Google Cloud Vertex Model Garden, demonstrating both efficiency
-and effectiveness. The observations are general and can be applied to other LLM
-models.
-
-We benchmarked fine tuning algorithms [LoRA](https://arxiv.org/abs/2106.09685)
-and [QLoRA](https://arxiv.org/abs/2305.14314) supported by
-[huggingface PEFT libraries](https://github.com/huggingface/peft). LoRA, short
-for Low-Rank Adaptation of Large Language Models, is an improved fine tuning
-method where instead of fine tuning all the weights that constitute the weight
-matrix of the pre-trained large language model, two smaller matrices that
-approximate this larger matrix are fine-tuned. QLoRA is an even more
-memory-efficient version of LoRA, where the pretrained model is loaded to GPU
-memory as quantized 4-bit weights, while preserving similar effectiveness to
-LoRA. We also provide simple scripts and parameter settings to reproduce the
-results reported in this report.
-
-In general, there are many factors that affect the performance of fine-tuning
-experiments, such as hardware settings, parameters, cost, and accuracy. It is
-impractical to obtain benchmarks for all possible combinations of these factors.
-Instead, we focus on tuning a subset of related parameters and evaluating their
-impact on a set of chosen metrics. The evaluation metrics are GPU memory usage,
-percentage of parameters tuned, tuning speed, cost, and accuracy. The tuning
-parameters are batch size, lora rank, maximum sequence length, and maximum
-training steps.
-
-## Key takeaways
-
--   **Use QLoRA to minimize the peak GPU requirements**: The QLoRA can
-    significantly reduce the peak GPU memory usage by ~75% compared to LoRA. For
-    OpenLLaMA7b, the peak memory is ~28G for LoRA and ~7G for QLoRA.
--   **Use LoRA to maximize the tuning speed and minimize the tuning cost**: LoRA
-    is ~66% faster than QLoRA in fine tuning speed. LoRA/QLoRA tuning cost is
-    low generally, while LoRA is even ~40% cheaper than QLoRA with the same
-    parameters. Suggest to use QLoRA for limited GPU memories, and LoRA for
-    limited training budgets. For OpenLLaMA7b, the tuning speed for LoRA/QLoRA
-    ~5 samples / 3 samples per second, and the tuning cost for LoRA/QLoRA in 500
-    steps is ~$1/$1.7 on `a2-highgpu-1g` with 1 A100 40G GPU. The tuning cost
-    for QLoRA in 500 steps is $6.75 on n1-standard-8 with 1 V100 GPU, while LoRA
-    could not run because of OOM.
--   **Use QLoRA to tune models with large sequence lengths**. For OpenLLaMA7b,
-    the max sequence length for QLoRA can be 2048 when consuming 16.3G GPU,
-    while the max sequence length for LoRA is 512 when consuming 28.2G GPU, and
-    encounter OOM when max sequence length is 1024.
--   **Both LoRA and QLoRA give similar accuracy improvement after fine tuning.**
-    For OpenLLaMA7b, both LoRA/QLoRA can improve the average accuracy by ~4%
-    evaluating on 3 typical tasks (ARC challenge, HellaSwag and TruthfulQA),
-    after training 1875 steps on dataset
-    [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
--   **Use a big batch size if GPU memory is not a constraint**. For OpenLLaMA7b
-    with other default parameters, we suggest using a batch size as 24 for
-    QLoRA, but 2 for LoRA when tuning with 1 A100 40G. We also suggest using a
-    batch size as 8 for QLoRA when tuning with 1 V100. Tuning with LoRA and
-    batch size as 1 got OOM and we don't recommend tuning LoRA with 1 V100.
-
-## Benchmark Details
-
-### Experiment Setup
-
-The benchmark dataset is
-[timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
-The training dataset is directly downloaded from hugging face to the VM, before
-every experiment.
-
-The default tuning parameters during benchmark are:
-
--   Host VM: a2-highgpu-1g
--   Accelerator type: 1 A100 40G
--   batch size: 2
--   lora_rank: 16
--   max_seq_length: 512
--   precision_mode: float16
--   max_train_steps: 500
-
-For simplicity, we set the precision mode to `float16` when tuning LoRA models,
-and set the precision to `4bit` for QLoRA.
-
-Sample script to start fine tuning dockers in a VM on GCP.
-
-```shell
-IMAGE_TAG=us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:latest
-docker run  --runtime=nvidia  -e NVIDIA_VISIBLE_DEVICES=0 \
---rm --name "test_gpu"  -it --pull=always ${IMAGE_TAG} \
---task=instruct-lora \
---pretrained_model_id=openlm-research/open_llama_7b \
---dataset_name="timdettmers/openassistant-guanaco"  \
---instruct_column_in_dataset="text" \
---precision_mode="float16" \
---output_dir=<OUTPUT DIR>  \
---lora_rank=2 \
---max_sequence_length=512 \
---learning_rate=2e-4 \
---max_steps=50
-```
-
-### GPU Memory
-
-In this benchmark, we investigated the impact of batch size, lora rank, and
-maximum sequence length on GPU memory, and then made recommendations on the
-maximum batch size for different GPUs.
-
-#### Peak GPU memory by batch size (GB)
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-peak-gpu-vs-batch-size.png" width="600">
-
--   The QLoRA can significantly reduce the peak GPU memory usage by ~75%
-    compared to LoRA. The peak GPU memory is ~28G for LoRA and ~7G for QLoRA
-    when batch size is 2.
--   QLoRA can support much larger batch sizes than LoRA
-    -   We can use a batch size as 32 for QLoRA, but only 2 for LoRA on 1 A100
-        40G.
-    -   We can use a batch size of 8 for QLoRA on 1 V100 GPU. LoRA will fail
-        with OOM even with a batch size of 1.
-
-#### Peak GPU memory by LoRA rank (GB)
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-peak-gpu-vs-lora-rank.png" width="600">
-
--   Peak GPU memories are quite similar for different LoRA ranks for both
-    LoRA/QLoRA.
--   The peak GPU memory increasing percentages are very small generally when
-    LoRA rank increases.
-    -   The peak GPU memory increases from 28G with LoRA rank 4 to 29.09G with
-        LoRA rank 64, and the increasing percentage is only ~3.9%.
-
-#### Peak GPU memory by max sequence length for LoRA/QLoRA (GB)
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-peak-gpu-vs-max-seq-length.png" width="600">
-
--   The peak GPU increases quickly when max sequence length increases for both
-    LoRA/QLoRA, and the increasing rate of LoRA is much faster than QLoRA.
-    -   For LoRA tuning, the GPU memory increased from 20.5G (max sequence
-        length=256) to 28.2G (max sequence length=512), an increase of ~37%.
-    -   For QLoRA tuning, the GPU memory increased from 6.94G (max sequence
-        length=256) to 7.57G (max sequence length=512), an increase of ~9%.
--   The max sequence length for QLoRA can be 2048 when consuming 16.3G GPU,
-    while the max sequence length for LoRA is 512 when consuming 28.2G GPU, and
-    encounter OOM when max sequence length is 1024.
-
-### Fine Tuning Parameters
-
-This section shows the number/percentage of trainable parameters, and the sizes
-of the fine tuned models. LoRA and QLoRA differ only in how they represent the
-precision of their parameters. The total number of parameters and the number of
-trainable parameters are the same for both methods.
-
-| LoRA Rank | Finetuned parameters | Total parameters | Trainable Parameter Percentage | Fine tuned model size (MB) |
-| --------- | -------------------- | ---------------- | ------------------------------ | -------------------------- |
-| 8         | 2.00E+07             | 6.76E+09         | 0.3%                           | 76.4                       |
-| 16        | 4.00E+07             | 6.78E+09         | 0.6%                           | 152.65                     |
-| 32        | 8.00E+07             | 6.82E+09         | 1.2%                           | 305.15                     |
-| 64        | 1.60E+08             | 6.90E+09         | 2.3%                           | 610.15                     |
-
-
-LoRA/QLoRA tunes quite a small fraction (only 0.3% with LoRA rank=8) of all
-parameters, and the tuned models are very small (only 76.4MB with LoRA rank=8).
-
-### Fine Tuning Speed And Costs
-
-The fine-tuning speed and cost are affected by various factors, such as the
-GPUs, LoRA ranks, and max sequence lengths.
-
--   LoRA is ~66% faster than QLoRA in fine tuning speed. The tuning speed for
-    LoRA/QLoRA ~5 samples / 3 samples per second on 1 A100 40G GPU
--   Higher LoRA ranks, slower tuning speed for both LoRA/QLoRA.
-    -   LoRA tuning speed reduces from ~5 samples per second with LoRA rank as 8
-        to ~4 samples per second with LoRA rank as 64, slowed down by 20%.
-    -   QLoRA tuning speed reduces from ~3 samples per second with LoRA rank as
-        8 to ~2.5 samples per second with LoRA rank as 64, slowed down by 17%.
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-tune-speed-vs-lora-rank.png" width="600">
-
--   Longer sequence lengths, slower tuning speed.
-    -   LoRA tuning speed reduces from ~5.56 samples per second with max
-        sequence length as 256 to ~4.84 samples per second with max sequence
-        length as 512 slowed down by 13%.
-    -   LoRA tuning speed reduces from ~2.95 samples per second with max
-        sequence length as 256 to ~2.88 samples per second with max sequence
-        length as 512 slowed down by ~2.4%.
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-tune-speed-lora-qlora.png" width="600">
-
--   LoRA/QLoRA tuning cost is low generally, while LoRA is even ~40% cheaper
-    than QLoRA with the same parameters.
-    -   The LoRA/QLoRA fine tuning cost for 500 steps is ~$1/$1.7 on 1 A100 40G.
-    -   The tuning cost for QLoRA in 500 steps is $6.75 on n1-standard-8 with 1
-        V100 GPU, while LoRA could not run because of OOM.
-
-<img src="images/openllama_7b_fine_tune_benchmark_report/openllama-7b-tune-cost-lora-qlora.png" width="600">
-
-### Accuracy
-
-We fine tuned Open Llama 7B model with
-[timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco),
-and report accuracy similar to the
-[HuggingFace leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-using
-[Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness).
-[HuggingFace leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-mainly compares models on ARC, HellaSwag, MMLU, and TruthfulQA. The authors did
-not publish OpenLLaMA 7B on MMLU
-([link](https://huggingface.co/openlm-research/open_llama_7b)). Therefore, we
-only benchmark accuracies on ARC, HellaSwag, and TruthfulQA.
-
-|                                                              | Mean | ARC  | HellaSwag | TruthfulQA | Tuning Parameters                                            |
-| ------------------------------------------------------------ | ---- | ---- | --------- | ---------- | ------------------------------------------------------------ |
-| OpenLLaMA7B ([Original Report](https://huggingface.co/openlm-research/open_llama_7b)) | 0.49 | 0.41 | 0.73      | 0.34       | n/a                                                          |
-| OpenLLaMA7B ([Re-run with lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)) | 0.51 | 0.47 | 0.72      | 0.35       | n/a                                                          |
-| OpenLLaMA7B+LoRA                                             | 0.56 | 0.48 | 0.74      | 0.45       | LoRA Rank=16; Max Sequence Length=512;Learning Rate=1e-4; Train steps=1875 |
-| OpenLLaMA7B+QLoRA                                            | 0.53 | 0.45 | 0.73      | 0.42       | LoRA Rank=16; Max Sequence Length=512; Learning Rate=1e-4; Train steps=1875 |
-
--   The base OpenLLaMA7B model gets better performance (2%) when using the
-    [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness).
--   LoRA/QLoRA can improve the performance by ~2-4% when trained for 1875 steps
-    with learning rate 1e-4.

community-content/vertex_model_garden/model_oss/notebook_util/common_util.py

@@ -231,6 +231,22 @@ def download_image(url: str) -> str:
   return Image.open(io.BytesIO(response.content))
 
 
+def resize_image(image: Any, new_width: int = 1000) -> Any:
+    """Resizes an image to a certain width.
+
+    Args:
+      image: The image which has to be resized.
+      new_width: New width of the image.
+
+    Returns:
+      New resized image.
+    """
+    width, height = image.size
+    new_height = int(height * new_width / width)
+    new_img = image.resize((new_width, new_height))
+    return new_img
+
+
 def load_img(path: str) -> Any:
   """Reads image from path and return PIL.Image instance.
 

notebooks/community/CODEOWNERS

@@ -116,6 +116,7 @@
 /notebooks/community/model_garden/model_garden_pytorch_biomedclip.ipynb @KCFindstr
 /notebooks/community/model_garden/model_garden_pytorch_imagebind.ipynb @kathyyu-google
 /notebooks/community/persistent_resource/00_persistent_resource_getting_started_cli.ipynb @jbrache
+/notebooks/community/persistent_resource/00_persistent_resource_getting_started_sdk.ipynb @jbrache
 /notebooks/community/model_garden/model_garden_pytorch_llama2_deployment.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_pytorch_llama2_quantization.ipynb @dstnluong-google
@@ -124,6 +125,8 @@
 /notebooks/community/model_garden/model_garden_pytorch_llama2_rlhf_tuning.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_pytorch_llama3_deployment.ipynb @kathyyu-google
 /notebooks/community/model_garden/model_garden_pytorch_llama3_finetuning.ipynb @kathyyu-google
+/notebooks/community/model_garden/model_garden_pytorch_llama3_1_deployment.ipynb @xiangxu-google
+/notebooks/community/model_garden/model_garden_pytorch_llama3_1_finetuning.ipynb @wrzhao-work
 /notebooks/community/model_garden/model_garden_pytorch_wizard_coder.ipynb @KCFindstr
 /notebooks/community/model_registry/get_started_with_vertex_ai_deployer.ipynb angelmontero@ @inardini
 /notebooks/community/model_garden/model_garden_pytorch_wizard_lm.ipynb @KCFindstr
@@ -144,3 +147,8 @@
 /notebooks/community/model_garden/model_garden_pytorch_sd_xl_finetuning_dreambooth_lora.ipynb @weigary
 /notebooks/community/model_garden/model_garden_pytorch_sd_2_1_local_finetuning_dreambooth.ipynb @weigary
 /notebooks/community/model_garden/model_garden_timesfm_deployment_on_vertex.ipynb @siriuz42
+/notebooks/community/model_garden/model_garden_llama_guard_deployment.ipynb @kathyyu-google
+/notebooks/community/model_garden/model_garden_rag.ipynb @kathyyu-google
+/notebooks/community/model_garden/synthetic_data_generation_using_llama3_1.ipynb @xiangxu-google
+/notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb @inardini
+/notebooks/community/model_garden/model_garden_openai_api_llama3_1.ipynb @inardini

notebooks/community/exploratory_data_analysis/eda_with_r_and_bigquery.ipynb

@@ -31,9 +31,9 @@
    "source": [
     "# Exploratory Data Analysis with R and BigQuery\n",
     "\n",
-    "**Authors**:  [Alok Pattani](https://github.com/alokpattani), [Khalid Salama](https://github.com/ksalama)\n",
+    "**Author**:  [Alok Pattani](https://github.com/alokpattani)\n",
     "\n",
-    "**Last Updated**:  February 2024\n",
+    "**Last Updated**:  July 2024\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -79,7 +79,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "version"
@@ -135,7 +137,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "bq_auth(use_oob = TRUE)"
@@ -151,7 +155,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Set the project ID\n",
@@ -168,13 +174,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Set your Cloud Storage bucket name\n",
     "BUCKET_NAME <- \"[YOUR-BUCKET-NAME]\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Set default height/width for plots generated\n",
+    "options(repr.plot.height = 9, repr.plot.width = 16)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -192,31 +212,64 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "sql_query_template <- \"\n",
     "    SELECT\n",
-    "      ROUND(weight_pounds, 2) AS weight_pounds,\n",
-    "      is_male,\n",
-    "      mother_age,\n",
-    "      plurality,\n",
-    "      gestation_weeks,\n",
-    "      cigarette_use,\n",
-    "      alcohol_use,\n",
-    "      CAST(ABS(FARM_FINGERPRINT(CONCAT(\n",
-    "        CAST(YEAR AS STRING), CAST(month AS STRING), \n",
-    "        CAST(weight_pounds AS STRING)))\n",
-    "        ) AS STRING) AS key\n",
+    "      TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, MINUTE) AS trip_time_minutes,  \n",
+    "\n",
+    "      passenger_count,\n",
+    "\n",
+    "      ROUND(trip_distance, 1) AS trip_distance_miles,\n",
+    "\n",
+    "      rate_code,\n",
+    "      /* Mapping from rate code to type from description column in BQ table schema */\n",
+    "      (CASE \n",
+    "        WHEN rate_code = '1.0'\n",
+    "          THEN 'Standard rate'\n",
+    "        WHEN rate_code = '2.0'\n",
+    "          THEN 'JFK'\n",
+    "        WHEN rate_code = '3.0'\n",
+    "          THEN 'Newark'\n",
+    "        WHEN rate_code = '4.0'\n",
+    "          THEN 'Nassau or Westchester'\n",
+    "        WHEN rate_code = '5.0'\n",
+    "          THEN 'Negotiated fare'\n",
+    "        WHEN rate_code = '6.0'\n",
+    "          THEN 'Group ride'\n",
+    "        /* Several NULL AND some '99.0' values go here */\n",
+    "        ELSE 'Unknown'\n",
+    "        END)\n",
+    "        AS rate_type,\n",
+    "\n",
+    "      fare_amount,\n",
+    "\n",
+    "      CAST(ABS(FARM_FINGERPRINT(\n",
+    "        CONCAT(\n",
+    "          CAST(trip_distance AS STRING), \n",
+    "          CAST(fare_amount AS STRING)\n",
+    "          )\n",
+    "        ))\n",
+    "        AS STRING)\n",
+    "        AS key\n",
+    "\n",
     "    FROM\n",
-    "        publicdata.samples.natality\n",
-    "    WHERE \n",
-    "      year > 2000\n",
-    "      AND weight_pounds > 0\n",
-    "      AND mother_age > 0\n",
-    "      AND plurality > 0\n",
-    "      AND gestation_weeks > 0\n",
-    "      AND month > 0\n",
+    "      `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2022`\n",
+    "\n",
+    "    /* Filter out some outlier or hard to understand values */\n",
+    "    WHERE\n",
+    "      (TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, MINUTE)\n",
+    "        BETWEEN 0.01 AND 120)\n",
+    "      AND\n",
+    "      (passenger_count BETWEEN 1 AND 10)\n",
+    "      AND\n",
+    "      (trip_distance BETWEEN 0.01 AND 100)\n",
+    "      AND\n",
+    "      (fare_amount BETWEEN 0.01 AND 250)\n",
+    "\n",
     "    LIMIT %s\n",
     "\""
    ]
@@ -232,14 +285,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "sample_size <- 10000\n",
     "\n",
     "sql_query <- sprintf(sql_query_template, sample_size)\n",
     "\n",
-    "natality_data <- bq_table_download(\n",
+    "taxi_trip_data <- bq_table_download(\n",
     "    bq_project_query(\n",
     "        PROJECT_ID, \n",
     "        query = sql_query\n",
@@ -257,31 +312,37 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# View the query result\n",
-    "head(natality_data)"
+    "head(taxi_trip_data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Show # of rows and data types of each column\n",
-    "str(natality_data)"
+    "str(taxi_trip_data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# View the results summary\n",
-    "summary(natality_data)"
+    "summary(taxi_trip_data)"
    ]
   },
   {
@@ -294,27 +355,31 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Display the distribution of baby weights using a histogram\n",
+    "# Display the distribution of fare amounts using a histogram\n",
     "ggplot(\n",
-    "    data = natality_data, \n",
-    "    aes(x = weight_pounds)\n",
+    "    data = taxi_trip_data, \n",
+    "    aes(x = fare_amount)\n",
     "    ) + \n",
-    "geom_histogram(bins = 200)"
+    "geom_histogram(bins = 100)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Display the relationship between gestation weeks and baby weights \n",
+    "# Display the relationship between trip distance and fare amount\n",
     "ggplot(\n",
-    "    data = natality_data, \n",
-    "    aes(x = gestation_weeks, y = weight_pounds)\n",
+    "    data = taxi_trip_data, \n",
+    "    aes(x = trip_distance_miles, y = fare_amount)\n",
     "    ) + \n",
     "geom_point() + \n",
     "geom_smooth(method = \"lm\")"
@@ -325,23 +390,41 @@
    "metadata": {},
    "source": [
     "### Performing the processing in BigQuery\n",
-    "Create a function that finds the number of records and the average weight for each value of the chosen column."
+    "Create a function that finds the number of trips and the average fare amount for each value of the chosen column."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "get_distinct_values <- function(column_name) {\n",
+    "get_distinct_value_aggregates <- function(column) {\n",
     "    query <- paste0(\n",
-    "        'SELECT ', column_name, ', \n",
-    "            COUNT(1) AS num_babies,\n",
-    "            AVG(weight_pounds) AS avg_wt\n",
-    "        FROM publicdata.samples.natality\n",
-    "        WHERE year > 2000\n",
-    "        GROUP BY ', column_name)\n",
+    "        'SELECT ', \n",
+    "          column, \n",
+    "          ', \n",
+    "          COUNT(1) AS num_trips,\n",
+    "          AVG(fare_amount) AS avg_fare_amount\n",
+    "        \n",
+    "        FROM\n",
+    "          `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2022`\n",
+    "        \n",
+    "        WHERE\n",
+    "          (TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, MINUTE) \n",
+    "            BETWEEN 0.01 AND 120)\n",
+    "          AND\n",
+    "          (passenger_count BETWEEN 1 AND 10)\n",
+    "          AND\n",
+    "          (trip_distance BETWEEN 0.01 AND 100)\n",
+    "          AND\n",
+    "          (fare_amount BETWEEN 0.01 AND 250)\n",
+    "        \n",
+    "        GROUP BY 1\n",
+    "        '\n",
+    "        )\n",
     "    \n",
     "    bq_table_download(\n",
     "        bq_project_query(\n",
@@ -362,20 +445,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "df <- get_distinct_values('mother_age')\n",
+    "df <- get_distinct_value_aggregates(\n",
+    "  'TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, MINUTE) AS trip_time_minutes')\n",
     "\n",
     "ggplot(\n",
     "    data = df, \n",
-    "    aes(x = mother_age, y = num_babies)\n",
+    "    aes(x = trip_time_minutes, y = num_trips)\n",
     "    ) + \n",
     "geom_line()\n",
     "\n",
     "ggplot(\n",
-    "    data = df, \n",
-    "    aes(x = mother_age, y = avg_wt)\n",
+    "    data = df,\n",
+    "    aes(x = trip_time_minutes, y = avg_fare_amount)\n",
     "    ) + \n",
     "geom_line()"
    ]
@@ -383,64 +469,88 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "df <- get_distinct_values('is_male')\n",
+    "df <- get_distinct_value_aggregates('passenger_count')\n",
     "\n",
     "ggplot(\n",
     "    data = df, \n",
-    "    aes(x = is_male, y = num_babies)\n",
+    "    aes(x = passenger_count, y = num_trips)\n",
+    "    ) + \n",
+    "geom_col() +\n",
+    "scale_x_continuous(breaks = 1:10)\n",
+    "\n",
+    "ggplot(\n",
+    "    data = df, \n",
+    "    aes(x = passenger_count, y = avg_fare_amount)\n",
+    "    ) + \n",
+    "geom_col() +\n",
+    "scale_x_continuous(breaks = 1:10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df <- get_distinct_value_aggregates('ROUND(trip_distance, 0) AS trip_distance_miles')\n",
+    "\n",
+    "ggplot(\n",
+    "    data = df, \n",
+    "    aes(x = trip_distance_miles, y = num_trips)\n",
+    "    ) + \n",
+    "geom_line()\n",
+    "\n",
+    "ggplot(\n",
+    "    data = df,\n",
+    "    aes(x = trip_distance_miles, y = avg_fare_amount)\n",
+    "    ) + \n",
+    "geom_line()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df <- get_distinct_value_aggregates(\"\n",
+    "  (CASE \n",
+    "    WHEN rate_code = '1.0'\n",
+    "      THEN 'Standard rate'\n",
+    "    WHEN rate_code = '2.0'\n",
+    "      THEN 'JFK'\n",
+    "    WHEN rate_code = '3.0'\n",
+    "      THEN 'Newark'\n",
+    "    WHEN rate_code = '4.0'\n",
+    "      THEN 'Nassau or Westchester'\n",
+    "    WHEN rate_code = '5.0'\n",
+    "      THEN 'Negotiated fare'\n",
+    "    WHEN rate_code = '6.0'\n",
+    "      THEN 'Group ride'\n",
+    "    /* Several NULL AND some '99.0' values go here */\n",
+    "    ELSE 'Unknown'\n",
+    "    END)\n",
+    "    AS rate_type\n",
+    "  \")\n",
+    "\n",
+    "ggplot(\n",
+    "    data = df,\n",
+    "    aes(x = rate_type, y = num_trips)\n",
     "    ) + \n",
     "geom_col()\n",
     "\n",
     "ggplot(\n",
-    "    data = df, \n",
-    "    aes(x = is_male, y = avg_wt)\n",
-    "    ) + \n",
-    "geom_col()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df <- get_distinct_values('plurality')\n",
-    "\n",
-    "ggplot(\n",
-    "    data = df, \n",
-    "    aes(x = plurality, y = num_babies)\n",
-    "    ) + \n",
-    "geom_col() + \n",
-    "scale_y_log10()\n",
-    "\n",
-    "ggplot(\n",
     "    data = df,\n",
-    "    aes(x = plurality, y = avg_wt)\n",
-    "    ) + \n",
-    "geom_col()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df <- get_distinct_values('gestation_weeks')\n",
-    "\n",
-    "ggplot(\n",
-    "    data = df,\n",
-    "    aes(x = gestation_weeks, y = num_babies)\n",
-    "    ) + \n",
-    "geom_col() + \n",
-    "scale_y_log10()\n",
-    "\n",
-    "ggplot(\n",
-    "    data = df,\n",
-    "    aes(x = gestation_weeks, y = avg_wt)\n",
+    "    aes(x = rate_type, y = avg_fare_amount)\n",
     "    ) + \n",
     "geom_col()"
    ]
@@ -455,7 +565,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Prepare training and evaluation data from BigQuery\n",
@@ -489,7 +601,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "print(paste0(\"Training instances count: \", nrow(train_data)))\n",
@@ -500,23 +614,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Write data frames to local CSV files, without headers or row names\n",
+    "# Write data frames to local CSV files, with headers\n",
     "dir.create(file.path('data'), showWarnings = FALSE)\n",
     "\n",
     "write.table(train_data, \"data/train_data.csv\", \n",
-    "   row.names = FALSE, col.names = FALSE, sep = \",\")\n",
+    "   row.names = FALSE, col.names = TRUE, sep = \",\")\n",
     "\n",
     "write.table(eval_data, \"data/eval_data.csv\", \n",
-    "   row.names = FALSE, col.names = FALSE, sep = \",\")"
+    "   row.names = FALSE, col.names = TRUE, sep = \",\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Upload CSV data to Cloud Storage by passing gsutil commands to system\n",
@@ -541,9 +659,9 @@
  "metadata": {
   "environment": {
    "kernel": "conda-env-r-r",
-   "name": "workbench-notebooks.m115",
+   "name": "workbench-notebooks.m123",
    "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/workbench-notebooks:m115"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m123"
   },
   "kernelspec": {
    "display_name": "R (Local)",

notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb

@@ -0,0 +1,741 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur8xi4C7S06n"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAPoU8Sm5E6e"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Evaluate Llama 3.1 models using Vertex AI AutoSxS\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_autosxs_evaluation_llama3_1.ipynb\"\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demostrates how to use the Vertex AI automatic side-by-side (AutoSxS) tool to evaluate Llama 3.1 models for a question-answering task.\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Choose the Llama 3.1 models you want to compare.\n",
+        "\n",
+        "- Create an evaluation dataset with question-answer data.\n",
+        "\n",
+        "- Create and run a Vertex AI AutoSxS pipeline that generates judgments and a set of AutoSxS metrics using the generated judgments.\n",
+        "\n",
+        "- Print the judgments and AutoSxS metrics.\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61RBz8LLbxCR"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tFy3H3aPgx12"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install --upgrade --user --quiet google-cloud-aiplatform google-cloud-pipeline-components\n",
+        "! pip3 install --upgrade --user --quiet openai gcsfs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5Xep4W9lq-Z"
+      },
+      "source": [
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XRvKdaPDTznN"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SbmM4z7FOBpM"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"<your-project-id>\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the region of the instance\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zgPO1eR3CYjk"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "\n",
+        "Create a storage bucket to store tutorial artifacts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MzGDU7TWdts_"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_NAME = \"<your-bucket-name>\"  # @param {type:\"string\"}\n",
+        "\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-EcIXiGsCePi"
+      },
+      "source": [
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NIq7R4HZCfIc"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Wn8ZkcV86KR"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B8DawN9D9NLU"
+      },
+      "outputs": [],
+      "source": [
+        "import vertexai\n",
+        "\n",
+        "vertexai.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jVYoyDl165EE"
+      },
+      "source": [
+        "### Import libraries\n",
+        "\n",
+        "Import libraries to use in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c1tEW-U968h8"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import string\n",
+        "\n",
+        "import openai\n",
+        "import pandas as pd\n",
+        "from google.auth import default, transport\n",
+        "from google.cloud import aiplatform\n",
+        "from google_cloud_pipeline_components.v1 import model_evaluation\n",
+        "from kfp import compiler"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZXnx1_CtEV5L"
+      },
+      "source": [
+        "### Set variables\n",
+        "\n",
+        "Before starting, you must decide how to access Llama 3.1 models. You can access Llama 3.1 models in just a few clicks using Model-as-a-Service (MaaS) without any setup or infrastructure hassles. You can also access Llama models for self-service in Vertex AI Model Garden, allowing you to choose your preferred infrastructure.\n",
+        "\n",
+        "This tutorial assumes that you deploy a self-managed instance of the Llama 3.1 model and compare it with Llama 3 405b using Model-as-a-Service (MaaS). Notice, only `us-central1` is supported region for Llama 3.1 models using Model-as-a-Service (MaaS).\n",
+        "\n",
+        "[Check out Llama 3 model card](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3?_ga=2.31261500.2048242469.1721714335-1107467625.1721655511) to learn how to deploy a Llama 3.1 models on Vertex AI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XASp0SPNEX10"
+      },
+      "outputs": [],
+      "source": [
+        "SELF_DEPLOYED_ENDPOINT_REGION = \"<your-endpoint-region>\"  # @param {type:\"string\"}\n",
+        "SELF_DEPLOYED_ENDPOINT_ID = \"<your-endpoint-id>\"  # @param {type:\"string\"}\n",
+        "MODEL_LOCATION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_sdmrDed2aHd"
+      },
+      "source": [
+        "### Helpers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pACHdEUf2bfq"
+      },
+      "outputs": [],
+      "source": [
+        "def self_model_generate(\n",
+        "    question,\n",
+        "    context,\n",
+        "    endpoint_id=SELF_DEPLOYED_ENDPOINT_ID,\n",
+        "    endpoint_location=SELF_DEPLOYED_ENDPOINT_REGION,\n",
+        "    **model_kwargs,\n",
+        "):\n",
+        "    \"\"\"Generate a response from a self-managed Llama 3.1 model.\"\"\"\n",
+        "\n",
+        "    aiplatform.init(project=PROJECT_ID, location=endpoint_location)\n",
+        "\n",
+        "    prompt = \"\"\"You are an AI assistant. Your goal is to answer questions using the pieces of context. \"\"\"\n",
+        "    prompt += f\"\"\"Question: {question}.\"\"\"\n",
+        "    prompt += f\"\"\"Context: {context}.\"\"\"\n",
+        "    prompt += \"\"\"Answer:\"\"\"\n",
+        "\n",
+        "    instance = {\"prompt\": prompt}\n",
+        "    instance.update(model_kwargs)\n",
+        "    instances = [instance]\n",
+        "\n",
+        "    endpoint = aiplatform.Endpoint(endpoint_id)\n",
+        "    response = endpoint.predict(instances=instances)\n",
+        "    return response.predictions[0][len(prompt) + 1 :]\n",
+        "\n",
+        "\n",
+        "def maas_generate(\n",
+        "    question,\n",
+        "    context,\n",
+        "    model=\"meta/llama3-405b-instruct-maas\",\n",
+        "    model_location=MODEL_LOCATION,\n",
+        "    **model_kwargs,\n",
+        "):\n",
+        "    \"\"\"Generate a response from a MaaS Llama 3.1 model.\"\"\"\n",
+        "\n",
+        "    creds, _ = default()\n",
+        "    auth_req = transport.requests.Request()\n",
+        "    creds.refresh(auth_req)\n",
+        "    if model_kwargs is None:\n",
+        "        model_kwargs = {}\n",
+        "\n",
+        "    client = openai.OpenAI(\n",
+        "        base_url=f\"https://{model_location}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{model_location}/endpoints/openapi/chat/completions?\",\n",
+        "        api_key=creds.token,\n",
+        "    )\n",
+        "\n",
+        "    response = client.chat.completions.create(\n",
+        "        model=model,\n",
+        "        messages=[\n",
+        "            {\n",
+        "                \"role\": \"system\",\n",
+        "                \"content\": \"\"\"You are an AI assistant. Your goal is to answer questions using the pieces of context. If you don't know the answer, say that you don't know.\"\"\",\n",
+        "            },\n",
+        "            {\"role\": \"user\", \"content\": question},\n",
+        "            {\"role\": \"assistant\", \"content\": context},\n",
+        "        ],\n",
+        "        **model_kwargs,\n",
+        "    )\n",
+        "\n",
+        "    return response.choices[0].message.content\n",
+        "\n",
+        "\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    \"\"\"Generate a uuid of a specified length (default=8).\"\"\"\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eha2l9nkNxZs"
+      },
+      "source": [
+        "### Generate evaluation dataset for AutoSxS\n",
+        "\n",
+        "Below you create your evaluation dataset, you specify a set of prompts to evaluate on.\n",
+        "\n",
+        "In this notebook, you:\n",
+        "\n",
+        "- Use 10 examples from the original dataset to create an evaluation dataset for AutoSxS.\n",
+        "  - Data in the `contexts` column will be treated as model context.\n",
+        "  - Data in the `questions` column will be treated as model instruction.\n",
+        "  - Data in the `response_a` column will be treated as responses for model A.\n",
+        "  - Data in the `response_b` will be treated as responses for model B.\n",
+        "\n",
+        "- Store the data in a JSON file in Google sCloud Storage.\n",
+        "\n",
+        "#### **Note: For the best results we recommend using at least 100 examples. There are diminishing returns when using more than 400 examples.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kuVd8Y7GHbp8"
+      },
+      "source": [
+        "#### Provide context and question"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j_OuH0yh_PMe"
+      },
+      "outputs": [],
+      "source": [
+        "contexts = [\n",
+        "    \"Beginning in the late 1910s and early 1920s, Whitehead gradually turned his attention from mathematics to philosophy of science, and finally to metaphysics. He developed a comprehensive metaphysical system which radically departed from most of western philosophy. Whitehead argued that reality consists of processes rather than material objects, and that processes are best defined by their relations with other processes, thus rejecting the theory that reality is fundamentally constructed by bits of matter that exist independently of one another. Today Whitehead's philosophical works – particularly Process and Reality – are regarded as the foundational texts of process philosophy.\",\n",
+        "    \"The gills have an adnate attachment to the cap, are narrow to moderately broad, closely spaced, and eventually separate from the stem. Young gills are cinnamon-brown in color, with lighter edges, but darken in maturity because they become covered with the dark spores. The stem is 6 to 8 cm (2+3⁄8 to 3+1⁄8 in) long by 1.5 to 2 mm (1⁄16 to 3⁄32 in) thick, and roughly equal in width throughout except for a slightly enlarged base. The lower region of the stem is brownish in color and has silky 'hairs' pressed against the stem; the upper region is grayish and pruinose (lightly dusted with powdery white granules). The flesh turns slightly bluish or greenish where it has been injured. The application of a drop of dilute potassium hydroxide solution on the cap or flesh will cause a color change to pale to dark yellowish to reddish brown; a drop on the stem produces a less intense or no color change.\",\n",
+        "    \"Go to Device Support. Choose your device. Scroll to Getting started and select Hardware & phone details. Choose Insert or remove SIM card and follow the steps. Review the Account Summary page for details. Image 13 Activate online Go to att.com/activateprepaid ((att.com/activarprepaid for Spanish)) and follow the prompts. Activate over the phone Call us at 877.426.0525 for automated instructions. You will need to know your SIM/eSIM ICCID & IMEI number for activation. Note: Look for your SIM (( ICCID )) number on your box or SIM card Now youre ready to activate your phone 1. Start with your new device powered off. 2. To activate a new line of service or a replacement device, please go to the AT&T Activation site or call 866.895.1099. You download the eSIM to your device over Wi-Fi®. The eSIM connects your device to our wireless network. How do I activate my phone with an eSIM? Turn your phone on, connect to Wi-Fi, and follow the prompts. Swap active SIM cards AT&T Wireless SM SIM Card Turn your device off. Remove the old SIM card. Insert the new one. Turn on your device.\",\n",
+        "    \"According to chief astronaut Deke Slayton's autobiography, he chose Bassett for Gemini 9 because he was 'strong enough to carry' both himself and See. Slayton had also assigned Bassett as command module pilot for the second backup Apollo crew, alongside Frank Borman and William Anders.\",\n",
+        "    \"Adaptation of the endosymbiont to the host's lifestyle leads to many changes in the endosymbiont–the foremost being drastic reduction in its genome size. This is due to many genes being lost during the process of metabolism, and DNA repair and recombination. While important genes participating in the DNA to RNA transcription, protein translation and DNA/RNA replication are retained. That is, a decrease in genome size is due to loss of protein coding genes and not due to lessening of inter-genic regions or open reading frame (ORF) size. Thus, species that are naturally evolving and contain reduced sizes of genes can be accounted for an increased number of noticeable differences between them, thereby leading to changes in their evolutionary rates. As the endosymbiotic bacteria related with these insects are passed on to the offspring strictly via vertical genetic transmission, intracellular bacteria goes through many hurdles during the process, resulting in the decrease in effective population sizes when compared to the free living bacteria. This incapability of the endosymbiotic bacteria to reinstate its wild type phenotype via a recombination process is called as Muller's ratchet phenomenon. Muller's ratchet phenomenon together with less effective population sizes has led to an accretion of deleterious mutations in the non-essential genes of the intracellular bacteria. This could have been due to lack of selection mechanisms prevailing in the rich environment of the host.\",\n",
+        "    \"The National Archives Building in downtown Washington holds record collections such as all existing federal census records, ships' passenger lists, military unit records from the American Revolution to the Philippine–American War, records of the Confederate government, the Freedmen's Bureau records, and pension and land records.\",\n",
+        "    \"Standard 35mm photographic film used for cinema projection has a much higher image resolution than HDTV systems, and is exposed and projected at a rate of 24 frames per second (frame/s). To be shown on standard television, in PAL-system countries, cinema film is scanned at the TV rate of 25 frame/s, causing a speedup of 4.1 percent, which is generally considered acceptable. In NTSC-system countries, the TV scan rate of 30 frame/s would cause a perceptible speedup if the same were attempted, and the necessary correction is performed by a technique called 3:2 Pulldown: Over each successive pair of film frames, one is held for three video fields (1/20 of a second) and the next is held for two video fields (1/30 of a second), giving a total time for the two frames of 1/12 of a second and thus achieving the correct average film frame rate.\",\n",
+        "    \"Maria Deraismes was initiated into Freemasonry in 1882, then resigned to allow her lodge to rejoin their Grand Lodge. Having failed to achieve acceptance from any masonic governing body, she and Georges Martin started a mixed masonic lodge that actually worked masonic ritual. Annie Besant spread the phenomenon to the English speaking world. Disagreements over ritual led to the formation of exclusively female bodies of Freemasons in England, which spread to other countries. Meanwhile, the French had re-invented Adoption as an all-female lodge in 1901, only to cast it aside again in 1935. The lodges, however, continued to meet, which gave rise, in 1959, to a body of women practising continental Freemasonry.\",\n",
+        "    \"Excavation of the foundations began in November 1906, with an average of 275 workers during the day shift and 100 workers during the night shift. The excavation was required to be completed in 120 days. To remove the spoils from the foundation, three temporary wooden platforms were constructed to street level. Hoisting engines were installed to place the beams for the foundation, while the piers were sunk into the ground under their own weight. Because of the lack of space in the area, the contractors' offices were housed beneath the temporary platforms. During the process of excavation, the Gilsey Building's foundations were underpinned or shored up, because that building had relatively shallow foundations descending only 18 feet (5.5 m) below Broadway.\",\n",
+        "    \"Dopamine consumed in food cannot act on the brain, because it cannot cross the blood–brain barrier. However, there are also a variety of plants that contain L-DOPA, the metabolic precursor of dopamine. The highest concentrations are found in the leaves and bean pods of plants of the genus Mucuna, especially in Mucuna pruriens (velvet beans), which have been used as a source for L-DOPA as a drug. Another plant containing substantial amounts of L-DOPA is Vicia faba, the plant that produces fava beans (also known as 'broad beans'). The level of L-DOPA in the beans, however, is much lower than in the pod shells and other parts of the plant. The seeds of Cassia and Bauhinia trees also contain substantial amounts of L-DOPA.\",\n",
+        "]\n",
+        "\n",
+        "questions = [\n",
+        "    \"What was the predominant theory of reality that Whitehead opposed?\",\n",
+        "    \"Why do the gills on the Psilocybe pelliculosa mushroom darken as they mature?\",\n",
+        "    \"user: How do I provision my AT&T SIM card?\",\n",
+        "    \"Why did chief astronaut Deke Slayton choose Charles Bassett for Gemini 9, according to Slayton's autobiography?\",\n",
+        "    \"What is the main alteration in an endosymbiont when it adapts to a host?\",\n",
+        "    \"What's the earliest war The National Archives Building has military unit records for\",\n",
+        "    \"To be shown on SDTV in PAL-system countries, at what rate is cinema film scanned?\",\n",
+        "    \"What year was the all-female masonic lodge cast aside?\",\n",
+        "    \"Why did the Gilsey Building have underpinned and shored up foundations?\",\n",
+        "    \"Why can dopamine consumed in food not act on the brain?\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Oiwr677h_cSk"
+      },
+      "outputs": [],
+      "source": [
+        "examples = pd.DataFrame(\n",
+        "    {\n",
+        "        \"questions\": questions,\n",
+        "        \"context\": contexts,\n",
+        "    }\n",
+        ")\n",
+        "examples.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2je-Rs8e_65p"
+      },
+      "outputs": [],
+      "source": [
+        "examples[\"response_a\"] = examples.apply(\n",
+        "    lambda x: self_model_generate(\n",
+        "        x[\"questions\"], x[\"context\"], max_tokens=2500, temperature=0.5\n",
+        "    ),\n",
+        "    axis=1,\n",
+        ")\n",
+        "examples.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VGIpgBk9Br_G"
+      },
+      "outputs": [],
+      "source": [
+        "examples[\"response_b\"] = examples.apply(\n",
+        "    lambda x: maas_generate(\n",
+        "        x[\"questions\"], x[\"context\"], max_tokens=2500, temperature=0.5\n",
+        "    ),\n",
+        "    axis=1,\n",
+        ")\n",
+        "examples.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NY1Jsj4aOCe1"
+      },
+      "source": [
+        "#### Upload your dataset to Cloud Storage\n",
+        "\n",
+        "Finally, we upload our evaluation dataset to Cloud Storage to be used as input for AutoSxS."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vykmkhp-ODKg"
+      },
+      "outputs": [],
+      "source": [
+        "examples.to_json(f\"{BUCKET_URI}/evaluation_dataset.json\", orient=\"records\", lines=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qgdk_qNIOFik"
+      },
+      "source": [
+        "### Create and run AutoSxS job\n",
+        "\n",
+        "In order to run AutoSxS, we need to define a `autosxs_pipeline` job with the following parameters.\n",
+        "\n",
+        "More details of the AutoSxS pipeline configuration can be found [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.9.0/api/preview/model_evaluation.html#preview.model_evaluation.autosxs_pipeline)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "veq26QZ7OMoC"
+      },
+      "source": [
+        "First, compile the AutoSxS pipeline locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C2NGZzOMOJPV"
+      },
+      "outputs": [],
+      "source": [
+        "template_uri = \"pipeline.yaml\"\n",
+        "compiler.Compiler().compile(\n",
+        "    pipeline_func=model_evaluation.autosxs_pipeline,\n",
+        "    package_path=template_uri,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I0aMBhoqOTXF"
+      },
+      "source": [
+        "The following code starts a Vertex Pipeline job, viewable from the Vertex UI. This pipeline job will take ~15 mins. This pipeline is made for batch prediction at a much larger scale than this example, so the time won't scale up linearly if there were thousands of Q&A pairs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tRdA3ovUOV6j"
+      },
+      "outputs": [],
+      "source": [
+        "display_name = f\"autosxs-qa-{generate_uuid()}\"\n",
+        "context_column = \"context\"\n",
+        "question_column = \"questions\"\n",
+        "response_column_a = \"response_a\"\n",
+        "response_column_b = \"response_b\"\n",
+        "\n",
+        "parameters = {\n",
+        "    \"evaluation_dataset\": BUCKET_URI + \"/evaluation_dataset.json\",\n",
+        "    \"id_columns\": [question_column],\n",
+        "    \"autorater_prompt_parameters\": {\n",
+        "        \"inference_context\": {\"column\": context_column},\n",
+        "        \"inference_instruction\": {\"column\": question_column},\n",
+        "    },\n",
+        "    \"task\": \"question_answering\",\n",
+        "    \"response_column_a\": response_column_a,\n",
+        "    \"response_column_b\": response_column_b,\n",
+        "}\n",
+        "\n",
+        "job = aiplatform.PipelineJob(\n",
+        "    job_id=display_name,\n",
+        "    display_name=display_name,\n",
+        "    pipeline_root=BUCKET_URI + \"/pipeline\",\n",
+        "    template_path=template_uri,\n",
+        "    parameter_values=parameters,\n",
+        "    enable_caching=False,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=LOCATION,\n",
+        ")\n",
+        "job.run()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EinPbr3XOYPQ"
+      },
+      "source": [
+        "### Get the judgments and AutoSxS metrics\n",
+        "Next, you can review judgments from the completed AutoSxS job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V_9yMfhrOZDk"
+      },
+      "outputs": [],
+      "source": [
+        "for details in job.task_details:\n",
+        "    if details.task_name == \"online-evaluation-pairwise\":\n",
+        "        break\n",
+        "\n",
+        "judgments_uri = details.outputs[\"judgments\"].artifacts[0].uri\n",
+        "judgments_df = pd.read_json(judgments_uri, lines=True)\n",
+        "judgments_df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BlKXu5Ze4tD3"
+      },
+      "source": [
+        "You can also review AutoSxS metrics computed from the judgments.\n",
+        "\n",
+        "You can find more details of AutoSxS metrics [here](https://cloud.google.com/vertex-ai/generative-ai/docs/models/side-by-side-eval#aggregate-metrics)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "G7meI2Eq4muT"
+      },
+      "outputs": [],
+      "source": [
+        "for details in job.task_details:\n",
+        "    if details.task_name == \"model-evaluation-text-generation-pairwise\":\n",
+        "        break\n",
+        "pd.DataFrame([details.outputs[\"autosxs_metrics\"].artifacts[0].metadata])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TpV-iwP9qw9c"
+      },
+      "source": [
+        "## Cleaning up\n",
+        "\n",
+        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+        "\n",
+        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
+        "\n",
+        "Set `delete_bucket` to **True** to delete the Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sx_vKniMq9ZX"
+      },
+      "outputs": [],
+      "source": [
+        "delete_pipeline_job = False  # @param {type:\"boolean\"}\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "if delete_pipeline_job:\n",
+        "    job.delete()\n",
+        "\n",
+        "if delete_bucket:\n",
+        "    ! gsutil rm -r gs://{BUCKET_NAME}"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_autosxs_evaluation_llama3_1.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/model_garden_camp_zipnerf_gradio.ipynb

@@ -221,7 +221,7 @@
       "source": [
         "# @title BigQuery Setup for CamP ZipNeRF Experiment Tracking\n",
         "\n",
-        "# @markdown The app leverages [BigQuery](https://pantheon.corp.google.com/bigquery) to create interactive dataframes that persistently tracks the lifecycle of NeRF experiments. This ensures that even if the runtime is stopped or lost, the app continues to work with the same information.\n",
+        "# @markdown The app leverages [BigQuery](https://cloud.google.com/bigquery) to create interactive dataframes that persistently tracks the lifecycle of NeRF experiments. This ensures that even if the runtime is stopped or lost, the app continues to work with the same information.\n",
         "\n",
         "# @markdown Each user is assigned a unique database name in BigQuery, generated based on their bucket name.\n",
         "\n",

notebooks/community/model_garden/model_garden_gemma_deployment_on_gke.ipynb

@@ -31,19 +31,18 @@
       "source": [
         "# Gemma deployment to GKE using TGI on GPU\n",
         "\n",
-        "\u003ctable align=\"left\"\u003e\u003ctbody\u003e\u003ctr\u003e\n",
-        "  \u003ctd\u003e\n",
-        "    \u003ca href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_gemma_deployment_on_gke.ipynb\"\u003e\n",
-        "      \u003cimg alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"\u003e\u003cbr\u003e Run in Colab Enterprise\n",
-        "    \u003c/a\u003e\n",
-        "  \u003c/td\u003e\n",
-        "  \u003ctd\u003e\n",
-        "    \u003ca href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_deployment_on_gke.ipynb\"\u003e\n",
-        "      \u003cimg src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"\u003e\u003cbr\u003e\n",
-        "      View on GitHub\n",
-        "    \u003c/a\u003e\n",
-        "  \u003c/td\u003e\n",
-        "\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_gemma_deployment_on_gke.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_deployment_on_gke.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
       ]
     },
     {
@@ -157,7 +156,7 @@
         "# Create Kubernetes secret for Hugging Face credentials\n",
         "! kubectl create secret generic hf-secret \\\n",
         "    --from-literal=hf_api_token={HF_TOKEN} \\\n",
-        "    --dry-run=client -o yaml \u003e hf-secret.yaml\n",
+        "    --dry-run=client -o yaml > hf-secret.yaml\n",
         "\n",
         "! kubectl apply -f hf-secret.yaml"
       ]
@@ -340,13 +339,22 @@
         "command = f\"\"\"kubectl exec -t $( kubectl get pod -l app=gemma-server -o jsonpath=\"{{.items[0].metadata.name}}\" ) -c inference-server -- curl -X POST http://localhost:8000/generate \\\n",
         "   -H \"Content-Type: application/json\" \\\n",
         "   -d '{json.dumps(request)}' \\\n",
-        "   2\u003e /dev/null\"\"\"\n",
+        "   2> /dev/null\"\"\"\n",
         "\n",
         "output = !{command}\n",
         "print(\"Output:\")\n",
         "print(json.loads(output[0])[\"generated_text\"])"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wbRmgoOZF6es"
+      },
+      "source": [
+        "## Clean up resources"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -356,8 +364,6 @@
       },
       "outputs": [],
       "source": [
-        "# @title Clean up resources\n",
-        "\n",
         "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
         "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
         "\n",
@@ -377,7 +383,6 @@
   "metadata": {
     "colab": {
       "name": "model_garden_gemma_deployment_on_gke.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

notebooks/community/model_garden/model_garden_gemma_deployment_on_vertex.ipynb

@@ -82,20 +82,6 @@
         "## Before you begin"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "C_wC61dhpWXj"
-      },
-      "outputs": [],
-      "source": [
-        "# @title Request for TPU quota\n",
-        "\n",
-        "# @markdown By default, the quota for TPU deployment `Custom model serving TPU v5e cores per region` is 4. TPU quota is only available in `us-west1`. You can request for higher TPU quota following the instructions at [\"Request a higher quota\"](https://cloud.google.com/docs/quota/view-manage#requesting_higher_quota)."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -109,28 +95,44 @@
         "\n",
         "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
         "\n",
-        "# @markdown **[Optional]** Set the GCS BUCKET_URI to store the experiment artifacts, if you want to use your own bucket. **If not set, a unique GCS bucket will be created automatically on your behalf**.\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
         "\n",
-        "import json\n",
+        "# @markdown 3. By default, the quota for TPU deployment `Custom model serving TPU v5e cores per region` is 4. TPU quota is only available in `us-west1`. You can request for higher TPU quota following the instructions at [\"Request a higher quota\"](https://cloud.google.com/docs/quota/view-manage#requesting_higher_quota).\n",
+        "\n",
+        "# Import the necessary packages\n",
+        "\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
         "import os\n",
         "from datetime import datetime\n",
         "from typing import Tuple\n",
         "\n",
         "from google.cloud import aiplatform\n",
         "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
         "# Get the default cloud project id.\n",
         "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
         "\n",
         "# Get the default region for launching jobs.\n",
         "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
         "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
         "# Cloud Storage bucket for storing the experiment artifacts.\n",
         "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
-        "# prefer using your own GCS bucket, please change the value yourself below.\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
         "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
         "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
+        "\n",
         "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
-        "    # Create a unique GCS bucket for this notebook if not specified\n",
         "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
         "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
         "else:\n",
@@ -147,6 +149,10 @@
         "\n",
         "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
         "MODEL_BUCKET = os.path.join(BUCKET_URI, \"gemma\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
         "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
         "\n",
         "# Gets the default SERVICE_ACCOUNT.\n",
@@ -155,13 +161,12 @@
         "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
         "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
         "\n",
+        "\n",
         "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
         "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
         "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
         "\n",
-        "# Enable Vertex AI and Cloud Compute APIs.\n",
         "! gcloud config set project $PROJECT_ID\n",
-        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
         "\n",
         "# @markdown ## Access Gemma Models\n",
         "# @markdown Choose between accessing Gemma models on [Hugging Face](https://huggingface.co/)\n",
@@ -171,7 +176,9 @@
         "# @markdown Alternatively, you can also load the original Gemma models for serving from Vertex AI after accepting the agreement.\n",
         "\n",
         "# @markdown **Please only select and fill one of the two following sections.**\n",
-        "LOAD_MODEL_FROM = \"Hugging Face\"  # @param [\"Hugging Face\", \"Google Cloud\"] {isTemplate:true}\n",
+        "LOAD_MODEL_FROM = (\n",
+        "    \"Hugging Face\"  # @param [\"Hugging Face\", \"Google Cloud\"] {isTemplate:true}\n",
+        ")\n",
         "\n",
         "# @markdown ---\n",
         "\n",
@@ -219,20 +226,14 @@
         "HEXLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/hex-llm-serve:deploy\"\n",
         "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240508_0916_RC02\"\n",
         "\n",
-        "SERVICE_ENDPOINT = \"aiplatform.googleapis.com\"\n",
-        "\n",
-        "\n",
-        "def get_job_name_with_datetime(prefix: str) -> str:\n",
-        "    \"\"\"Gets the job name with date time when triggering deployment jobs.\"\"\"\n",
-        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
-        "\n",
         "\n",
         "def deploy_model_hexllm(\n",
         "    model_name: str,\n",
         "    model_id: str,\n",
         "    service_account: str,\n",
-        "    machine_type: str = \"ct5lp-hightpu-1t\",\n",
+        "    base_model_id: str = None,\n",
         "    tensor_parallel_size: int = 1,\n",
+        "    machine_type: str = \"ct5lp-hightpu-1t\",\n",
         "    hbm_utilization_factor: float = 0.6,\n",
         "    max_running_seqs: int = 256,\n",
         "    endpoint_id: str = \"\",\n",
@@ -246,28 +247,42 @@
         "        )\n",
         "        endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
         "    else:\n",
-        "        endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "        endpoint = aiplatform.Endpoint.create(\n",
+        "            display_name=f\"{model_name}-endpoint\",\n",
+        "            location=TPU_DEPLOYMENT_REGION,\n",
+        "        )\n",
+        "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
+        "    if not tensor_parallel_size:\n",
+        "        tensor_parallel_size = int(machine_type[-2])\n",
         "\n",
         "    hexllm_args = [\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
         "        \"--log_level=INFO\",\n",
-        "        \"--enable_jit\",\n",
         "        f\"--model={model_id}\",\n",
-        "        \"--load_format=auto\",\n",
         "        f\"--tensor_parallel_size={tensor_parallel_size}\",\n",
+        "        \"--enable_jit\",\n",
+        "        \"--load_format=auto\",\n",
         "        f\"--hbm_utilization_factor={hbm_utilization_factor}\",\n",
         "        f\"--max_running_seqs={max_running_seqs}\",\n",
         "    ]\n",
-        "    hexllm_envs = {\n",
+        "\n",
+        "    env_vars = {\n",
+        "        \"MODEL_ID\": base_model_id,\n",
         "        \"PJRT_DEVICE\": \"TPU\",\n",
         "        \"RAY_DEDUP_LOGS\": \"0\",\n",
         "        \"RAY_USAGE_STATS_ENABLED\": \"0\",\n",
-        "        \"MODEL_ID\": model_id,\n",
         "        \"DEPLOY_SOURCE\": \"notebook\",\n",
         "    }\n",
-        "    if HF_TOKEN:\n",
-        "        hexllm_envs.update({\"HF_TOKEN\": HF_TOKEN})\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars.update({\"HF_TOKEN\": HF_TOKEN})\n",
+        "    except:\n",
+        "        pass\n",
         "\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
@@ -277,9 +292,10 @@
         "        serving_container_ports=[7080],\n",
         "        serving_container_predict_route=\"/generate\",\n",
         "        serving_container_health_route=\"/ping\",\n",
-        "        serving_container_environment_variables=hexllm_envs,\n",
+        "        serving_container_environment_variables=env_vars,\n",
         "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
         "        serving_container_deployment_timeout=7200,\n",
+        "        location=TPU_DEPLOYMENT_REGION,\n",
         "    )\n",
         "\n",
         "    model.deploy(\n",
@@ -297,33 +313,42 @@
         "    model_name: str,\n",
         "    model_id: str,\n",
         "    service_account: str,\n",
-        "    machine_type: str = \"g2-standard-12\",\n",
+        "    base_model_id: str = None,\n",
+        "    machine_type: str = \"g2-standard-8\",\n",
         "    accelerator_type: str = \"NVIDIA_L4\",\n",
         "    accelerator_count: int = 1,\n",
-        "    max_model_len: int = 8192,\n",
-        "    dtype: str = \"bfloat16\",\n",
+        "    gpu_memory_utilization: float = 0.9,\n",
+        "    max_model_len: int = 4096,\n",
+        "    dtype: str = \"auto\",\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
-        "    \"\"\"Deploys models with vLLM on GPU in Vertex AI.\"\"\"\n",
+        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
         "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
         "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
         "    vllm_args = [\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
         "        f\"--model={model_id}\",\n",
         "        f\"--tensor-parallel-size={accelerator_count}\",\n",
         "        \"--swap-space=16\",\n",
-        "        \"--gpu-memory-utilization=0.9\",\n",
+        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
         "        f\"--max-model-len={max_model_len}\",\n",
         "        f\"--dtype={dtype}\",\n",
         "        \"--disable-log-stats\",\n",
         "    ]\n",
         "\n",
         "    env_vars = {\n",
-        "        \"MODEL_ID\": model_id,\n",
+        "        \"MODEL_ID\": base_model_id,\n",
         "        \"DEPLOY_SOURCE\": \"notebook\",\n",
         "    }\n",
-        "    if HF_TOKEN:\n",
-        "        env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "    except:\n",
+        "        pass\n",
         "\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
@@ -337,7 +362,9 @@
         "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
         "        serving_container_deployment_timeout=7200,\n",
         "    )\n",
-        "\n",
+        "    print(\n",
+        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
+        "    )\n",
         "    model.deploy(\n",
         "        endpoint=endpoint,\n",
         "        machine_type=machine_type,\n",
@@ -346,103 +373,9 @@
         "        deploy_request_timeout=1800,\n",
         "        service_account=service_account,\n",
         "    )\n",
-        "    return model, endpoint\n",
+        "    print(\"endpoint_name:\", endpoint.name)\n",
         "\n",
-        "\n",
-        "def get_quota(project_id: str, region: str, resource_id: str) -> int:\n",
-        "    \"\"\"Returns the quota for a resource in a region. Returns -1 if can not figure out the quota.\"\"\"\n",
-        "    quota_list_output = !gcloud alpha services quota list --service=$SERVICE_ENDPOINT  --consumer=projects/$project_id --filter=\"$SERVICE_ENDPOINT/$resource_id\" --format=json\n",
-        "    # Use '.s' on the command output because it is an SList type.\n",
-        "    quota_data = json.loads(quota_list_output.s)\n",
-        "    if len(quota_data) == 0 or \"consumerQuotaLimits\" not in quota_data[0]:\n",
-        "        return -1\n",
-        "    if (\n",
-        "        len(quota_data[0][\"consumerQuotaLimits\"]) == 0\n",
-        "        or \"quotaBuckets\" not in quota_data[0][\"consumerQuotaLimits\"][0]\n",
-        "    ):\n",
-        "        return -1\n",
-        "    all_regions_data = quota_data[0][\"consumerQuotaLimits\"][0][\"quotaBuckets\"]\n",
-        "    for region_data in all_regions_data:\n",
-        "        if (\n",
-        "            region_data.get(\"dimensions\")\n",
-        "            and region_data[\"dimensions\"][\"region\"] == region\n",
-        "        ):\n",
-        "            if \"effectiveLimit\" in region_data:\n",
-        "                return int(region_data[\"effectiveLimit\"])\n",
-        "            else:\n",
-        "                return 0\n",
-        "    return -1\n",
-        "\n",
-        "\n",
-        "def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:\n",
-        "    \"\"\"Returns the resource id for a given accelerator type and the use case.\n",
-        "    Args:\n",
-        "      accelerator_type: The accelerator type.\n",
-        "      is_for_training: Whether the resource is used for training. Set false\n",
-        "      for serving use case.\n",
-        "    Returns:\n",
-        "      The resource id.\n",
-        "    \"\"\"\n",
-        "    training_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_training_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_training_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_training_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_training_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_training_tpu_v5e\",\n",
-        "        \"TPU_V3\": \"custom_model_training_tpu_v3\",\n",
-        "    }\n",
-        "    serving_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_serving_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_serving_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_serving_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_serving_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_serving_tpu_v5e\",\n",
-        "    }\n",
-        "    if is_for_training:\n",
-        "        if accelerator_type in training_accelerator_map:\n",
-        "            return training_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for training.\"\n",
-        "            )\n",
-        "    else:\n",
-        "        if accelerator_type in serving_accelerator_map:\n",
-        "            return serving_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for serving.\"\n",
-        "            )\n",
-        "\n",
-        "\n",
-        "def check_quota(\n",
-        "    project_id: str,\n",
-        "    region: str,\n",
-        "    accelerator_type: str,\n",
-        "    accelerator_count: int,\n",
-        "    is_for_training: bool,\n",
-        "):\n",
-        "    \"\"\"Checks if the project and the region has the required quota.\"\"\"\n",
-        "    resource_id = get_resource_id(accelerator_type, is_for_training)\n",
-        "    quota = get_quota(project_id, region, resource_id)\n",
-        "    quota_request_instruction = (\n",
-        "        \"Either use \"\n",
-        "        \"a different region or request additional quota. Follow \"\n",
-        "        \"instructions here \"\n",
-        "        \"https://cloud.google.com/docs/quotas/view-manage#requesting_higher_quota\"\n",
-        "        \" to check quota in a region or request additional quota for \"\n",
-        "        \"your project.\"\n",
-        "    )\n",
-        "    if quota == -1:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not found for: {resource_id} in {region}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )\n",
-        "    if quota < accelerator_count:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not enough for {resource_id} in {region}:\n",
-        "            {quota} < {accelerator_count}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )"
+        "    return model, endpoint"
       ]
     },
     {
@@ -472,6 +405,7 @@
         "\n",
         "# @markdown Select one of the six model variations.\n",
         "MODEL_ID = \"gemma-1.1-2b-it\"  # @param [\"gemma-2b\", \"gemma-2b-it\", \"gemma-7b\", \"gemma-7b-it\", \"gemma-1.1-2b-it\", \"gemma-1.1-7b-it\"] {allow-input: true, isTemplate: true}\n",
+        "TPU_DEPLOYMENT_REGION = \"us-west1\"  # @param [\"us-west1\"] {isTemplate:true}\n",
         "model_id = os.path.join(model_path_prefix, MODEL_ID)\n",
         "\n",
         "# @markdown Find Vertex AI prediction TPUv5e machine types in\n",
@@ -489,7 +423,7 @@
         "    # Note: 1 TPU V5 chip has only one core.\n",
         "    accelerator_count = 4\n",
         "\n",
-        "check_quota(\n",
+        "common_util.check_quota(\n",
         "    project_id=PROJECT_ID,\n",
         "    region=REGION,\n",
         "    accelerator_type=accelerator_type,\n",
@@ -498,20 +432,18 @@
         ")\n",
         "\n",
         "# Server parameters.\n",
-        "tensor_parallel_size = accelerator_count\n",
-        "hbm_utilization_factor = 0.6  # Fraction of HBM memory allocated for KV cache after model loading. A larger value improves throughput but gives higher risk of TPU out-of-memory errors with long prompts.\n",
-        "max_running_seqs = 256  # Maximum number of running sequences in a continuous batch.\n",
+        "hbm_utilization_factor = 0.6  # A larger value improves throughput but gives higher risk of TPU out-of-memory errors with long prompts.\n",
+        "max_running_seqs = 256\n",
         "\n",
         "# Endpoint configurations.\n",
         "min_replica_count = 1\n",
         "max_replica_count = 1\n",
         "\n",
-        "model_hexllm, endpoint_hexllm = deploy_model_hexllm(\n",
-        "    model_name=get_job_name_with_datetime(prefix=MODEL_ID),\n",
+        "models[\"hexllm_tpu\"], endpoints[\"hexllm_tpu\"] = deploy_model_hexllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=MODEL_ID),\n",
         "    model_id=model_id,\n",
         "    service_account=SERVICE_ACCOUNT,\n",
         "    machine_type=machine_type,\n",
-        "    tensor_parallel_size=tensor_parallel_size,\n",
         "    hbm_utilization_factor=hbm_utilization_factor,\n",
         "    max_running_seqs=max_running_seqs,\n",
         "    min_replica_count=min_replica_count,\n",
@@ -530,7 +462,9 @@
       "source": [
         "# @title Predict\n",
         "\n",
-        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. The first few requests may have high latency. This is because the server needs to warm up with the initial requests. The following requests should not have the same delay.\n",
+        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts based on your `template`. Note that the first few prompts will take longer to execute.\n",
+        "\n",
+        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
         "\n",
         "# @markdown Example:\n",
         "\n",
@@ -542,8 +476,8 @@
         "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
         "\n",
         "# Loads an existing endpoint instance using the endpoint name:\n",
-        "# - Using `endpoint_name = endpoint_hexllm.name` allows us to get the endpoint\n",
-        "#   name of the endpoint `endpoint_hexllm` created in the cell above.\n",
+        "# - Using `endpoint_name = endpoint.name` allows us to get the endpoint\n",
+        "#   name of the endpoint `endpoint` created in the cell above.\n",
         "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
         "#   an existing endpoint with the ID 1234567890123456789.\n",
         "# You may uncomment the code below to load an existing endpoint:\n",
@@ -552,7 +486,7 @@
         "# aip_endpoint_name = (\n",
         "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
         "# )\n",
-        "# endpoint_hexllm = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
         "\n",
         "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
         "max_tokens = 50  # @param {type: \"integer\"}\n",
@@ -568,10 +502,10 @@
         "        \"top_k\": top_k,\n",
         "    },\n",
         "]\n",
-        "response = endpoint_hexllm.predict(instances=instances)\n",
+        "response = endpoints[\"hexllm_tpu\"].predict(instances=instances)\n",
         "\n",
-        "prediction = response.predictions[0]\n",
-        "print(prediction)"
+        "for prediction in response.predictions:\n",
+        "    print(prediction)"
       ]
     },
     {
@@ -639,7 +573,7 @@
         "        \"top_k\": 1,\n",
         "    },\n",
         "]\n",
-        "response = endpoint_hexllm.predict(instances=instances)\n",
+        "response = endpoints[\"hexllm_tpu\"].predict(instances=instances)\n",
         "\n",
         "prediction = response.predictions[0]\n",
         "print(prediction)"
@@ -726,7 +660,7 @@
         "        % accelerator_type\n",
         "    )\n",
         "\n",
-        "check_quota(\n",
+        "common_util.check_quota(\n",
         "    project_id=PROJECT_ID,\n",
         "    region=REGION,\n",
         "    accelerator_type=accelerator_type,\n",
@@ -737,8 +671,8 @@
         "# Note that a larger max_model_len will require more GPU memory.\n",
         "max_model_len = 2048\n",
         "\n",
-        "model_vllm, endpoint_vllm = deploy_model_vllm(\n",
-        "    model_name=get_job_name_with_datetime(prefix=\"gemma-serve-vllm\"),\n",
+        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=\"gemma-serve-vllm\"),\n",
         "    model_id=model_id,\n",
         "    service_account=SERVICE_ACCOUNT,\n",
         "    machine_type=machine_type,\n",
@@ -769,26 +703,37 @@
       "source": [
         "# @title Predict\n",
         "\n",
-        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. Note that the first few prompts will take longer to execute.\n",
+        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. Sampling parameters supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/dev/sampling_params.html).\n",
+        "\n",
+        "# @markdown Example:\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown Human: What is a car?\n",
+        "# @markdown Assistant:  A car, or a motor car, is a road-connected human-transportation system used to move people or goods from one place to another. The term also encompasses a wide range of vehicles, including motorboats, trains, and aircrafts. Cars typically have four wheels, a cabin for passengers, and an engine or motor. They have been around since the early 19th century and are now one of the most popular forms of transportation, used for daily commuting, shopping, and other purposes.\n",
+        "# @markdown ```\n",
+        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
         "\n",
         "# Loads an existing endpoint instance using the endpoint name:\n",
-        "# - Using `endpoint_name = endpoint_vllm.name` allows us to get the endpoint\n",
-        "#   name of the endpoint `endpoint_vllm` created in the cell above.\n",
+        "# - Using `endpoint_name = endpoint.name` allows us to get the\n",
+        "#   endpoint name of the endpoint `endpoint` created in the cell\n",
+        "#   above.\n",
         "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
         "#   an existing endpoint with the ID 1234567890123456789.\n",
-        "# You may uncomment the code below to load an existing endpoint:\n",
-        "# endpoint_name = endpoint_vllm.name\n",
-        "# # endpoint_name = \"\"  # @param {type:\"string\"}\n",
+        "# You may uncomment the code below to load an existing endpoint.\n",
+        "\n",
+        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
         "# aip_endpoint_name = (\n",
         "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
         "# )\n",
-        "# endpoint_vllm = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
         "\n",
         "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
-        "max_tokens = 50  # @param {type: \"integer\"}\n",
-        "temperature = 1.0  # @param {type: \"number\"}\n",
-        "top_p = 1.0  # @param {type: \"number\"}\n",
-        "top_k = 10  # @param {type: \"integer\"}\n",
+        "max_tokens = 50  # @param {type:\"integer\"}\n",
+        "temperature = 1.0  # @param {type:\"number\"}\n",
+        "top_p = 1.0  # @param {type:\"number\"}\n",
+        "top_k = 1  # @param {type:\"integer\"}\n",
+        "raw_response = False  # @param {type:\"boolean\"}\n",
+        "\n",
         "instances = [\n",
         "    {\n",
         "        \"prompt\": prompt,\n",
@@ -796,12 +741,13 @@
         "        \"temperature\": temperature,\n",
         "        \"top_p\": top_p,\n",
         "        \"top_k\": top_k,\n",
+        "        \"raw_response\": raw_response,\n",
         "    },\n",
         "]\n",
-        "response = endpoint_vllm.predict(instances=instances)\n",
+        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
         "\n",
-        "prediction = response.predictions[0]\n",
-        "print(prediction)"
+        "for prediction in response.predictions:\n",
+        "    print(prediction)"
       ]
     },
     {
@@ -833,20 +779,18 @@
       },
       "outputs": [],
       "source": [
-        "# @title  Delete the models and endpoints\n",
-        "\n",
         "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
         "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
-        "# Undeploy models and delete endpoints.\n",
-        "endpoint_hexllm.delete(force=True)\n",
-        "endpoint_vllm.delete(force=True)\n",
+        "\n",
+        "# Undeploy model and delete endpoint.\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
         "\n",
         "# Delete models.\n",
-        "model_hexllm.delete()\n",
-        "model_vllm.delete()\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
         "\n",
-        "# Delete Cloud Storage objects.\n",
-        "delete_bucket = False  # @param {type:\"boolean\", isTemplate: true}\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
         "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]

notebooks/community/model_garden/model_garden_gemma_evaluation.ipynb

@@ -3,6 +3,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "id": "B8S-yo8qTIcO"
       },
@@ -91,6 +92,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "cellView": "form",
         "id": "81CC3tL1T_TL"
@@ -98,18 +100,26 @@
       "outputs": [],
       "source": [
         "# @title Setup Google Cloud project\n",
-        "\n",
         "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
         "\n",
         "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
         "\n",
         "# Import the necessary packages\n",
+        "\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
         "import os\n",
-        "import json\n",
         "from datetime import datetime\n",
         "\n",
         "from google.cloud import aiplatform\n",
         "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
         "# Get the default cloud project id.\n",
         "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
         "\n",
@@ -122,16 +132,16 @@
         "\n",
         "# Cloud Storage bucket for storing the experiment artifacts.\n",
         "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
-        "# prefer using your own GCS bucket, please change the value yourself below.\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
         "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
         "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
-        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
-        "assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "\n",
         "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
-        "    # Create a unique GCS bucket for this notebook, if not specified by the user\n",
         "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
         "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
         "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
         "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
         "    bucket_region = shell_output[0].strip().lower()\n",
         "    if bucket_region != REGION:\n",
@@ -139,127 +149,37 @@
         "            \"Bucket region %s is different from notebook region %s\"\n",
         "            % (bucket_region, REGION)\n",
         "        )\n",
-        "\n",
         "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
         "\n",
-        "# Gets the default BUCKET_URI and SERVICE_ACCOUNT if they were not specified by the user.\n",
-        "SERVICE_ACCOUNT = None\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"gemma\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "\n",
+        "# Gets the default SERVICE_ACCOUNT.\n",
         "shell_output = ! gcloud projects describe $PROJECT_ID\n",
         "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
         "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
         "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
         "\n",
-        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
         "\n",
-        "# Initialize Vertex AI API.\n",
-        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"staging\")\n",
-        "MODEL_BUCKET = os.path.join(STAGING_BUCKET, \"model\")\n",
-        "print(\"Initializing Vertex AI API.\")\n",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
+        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
         "\n",
         "! gcloud config set project $PROJECT_ID\n",
         "\n",
         "# The evaluation docker image.\n",
-        "EVAL_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-lm-evaluation-harness:20240320_0655_RC00\"\n",
-        "\n",
-        "# Define common functions\n",
-        "\n",
-        "\n",
-        "def get_job_name_with_datetime(prefix: str) -> str:\n",
-        "    \"\"\"Gets the job name with date time when triggering training or deployment\n",
-        "    jobs in Vertex AI.\n",
-        "    \"\"\"\n",
-        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
-        "\n",
-        "\n",
-        "def get_quota(project_id: str, region: str, resource_id: str) -> int:\n",
-        "  \"\"\"Returns the quota for a resource in a region. Returns -1 if can not figure out the quota.\"\"\"\n",
-        "  service_endpoint = \"aiplatform.googleapis.com\"\n",
-        "  quota_list_output = !gcloud alpha services quota list --service=$service_endpoint  --consumer=projects/$project_id --filter=\"$service_endpoint/$resource_id\" --format=json\n",
-        "  # Use '.s' on the command output because it is an SList type.\n",
-        "  quota_data = json.loads(quota_list_output.s)\n",
-        "  if len(quota_data) == 0 or \"consumerQuotaLimits\" not in quota_data[0]:\n",
-        "    return -1\n",
-        "  if len(quota_data[0][\"consumerQuotaLimits\"]) == 0 or \"quotaBuckets\" not in quota_data[0][\"consumerQuotaLimits\"][0]:\n",
-        "    return -1\n",
-        "  all_regions_data = quota_data[0][\"consumerQuotaLimits\"][0][\"quotaBuckets\"]\n",
-        "  for region_data in all_regions_data:\n",
-        "    if region_data.get('dimensions') and region_data['dimensions']['region'] == region:\n",
-        "      if 'effectiveLimit' in region_data:\n",
-        "        return int(region_data['effectiveLimit'])\n",
-        "      else:\n",
-        "        return 0\n",
-        "  return -1\n",
-        "\n",
-        "\n",
-        "def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:\n",
-        "  \"\"\"Returns the resource id for a given accelerator type and the use case.\n",
-        "  Args:\n",
-        "    accelerator_type: The accelerator type.\n",
-        "    is_for_training: Whether the resource is used for training. Set false\n",
-        "    for serving use case.\n",
-        "  Returns:\n",
-        "    The resource id.\n",
-        "  \"\"\"\n",
-        "  training_accelerator_map = {\n",
-        "      \"NVIDIA_TESLA_V100\": \"custom_model_training_nvidia_v100_gpus\",\n",
-        "      \"NVIDIA_L4\": \"custom_model_training_nvidia_l4_gpus\",\n",
-        "      \"NVIDIA_TESLA_A100\": \"custom_model_training_nvidia_a100_gpus\",\n",
-        "      \"NVIDIA_TESLA_T4\": \"custom_model_training_nvidia_t4_gpus\",\n",
-        "      \"TPU_V5e\": \"custom_model_training_tpu_v5e\",\n",
-        "      \"TPU_V3\": \"custom_model_training_tpu_v3\",\n",
-        "  }\n",
-        "  serving_accelerator_map = {\n",
-        "      \"NVIDIA_TESLA_V100\": \"custom_model_serving_nvidia_v100_gpus\",\n",
-        "      \"NVIDIA_L4\": \"custom_model_serving_nvidia_l4_gpus\",\n",
-        "      \"NVIDIA_TESLA_A100\": \"custom_model_serving_nvidia_a100_gpus\",\n",
-        "      \"NVIDIA_TESLA_T4\": \"custom_model_serving_nvidia_t4_gpus\",\n",
-        "      \"TPU_V5e\": \"custom_model_serving_tpu_v5e\",\n",
-        "  }\n",
-        "  if is_for_training:\n",
-        "    if accelerator_type in training_accelerator_map:\n",
-        "      return training_accelerator_map[accelerator_type]\n",
-        "    else:\n",
-        "      raise ValueError(\n",
-        "          f\"Could not find accelerator type: {accelerator_type} for training.\"\n",
-        "      )\n",
-        "  else:\n",
-        "    if accelerator_type in serving_accelerator_map:\n",
-        "      return serving_accelerator_map[accelerator_type]\n",
-        "    else:\n",
-        "      raise ValueError(\n",
-        "          f\"Could not find accelerator type: {accelerator_type} for serving.\"\n",
-        "      )\n",
-        "\n",
-        "\n",
-        "def check_quota(project_id:str, region: str, accelerator_type: str,\n",
-        "                accelerator_count: int, is_for_training: bool):\n",
-        "  \"\"\"Checks if the project and the region has the required quota.\"\"\"\n",
-        "  resource_id = get_resource_id(accelerator_type, is_for_training)\n",
-        "  quota = get_quota(project_id, region, resource_id)\n",
-        "  quota_request_instruction = (\"Either use \"\n",
-        "            \"a different region or request additional quota. Follow \"\n",
-        "            \"instructions here \"\n",
-        "            \"https://cloud.google.com/docs/quotas/view-manage#requesting_higher_quota\"\n",
-        "            \" to check quota in a region or request additional quota for \"\n",
-        "            \"your project.\")\n",
-        "  if quota == -1:\n",
-        "    raise ValueError(\n",
-        "            f\"\"\"Quota not found for: {resource_id} in {region}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )\n",
-        "  if quota < accelerator_count:\n",
-        "    raise ValueError(\n",
-        "            f\"\"\"Quota not enough for {resource_id} in {region}:\n",
-        "            {quota} < {accelerator_count}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )"
+        "EVAL_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-lm-evaluation-harness:20240320_0655_RC00\""
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "cellView": "form",
         "id": "pNHMbjr0UjrK"
@@ -285,12 +205,14 @@
         "# Setup evaluation job.\n",
         "# @markdown Set the base model id.\n",
         "base_model_id = \"google/gemma-1.1-2b-it\"  # @param[\"google/gemma-2b\", \"google/gemma-2b-it\", \"google/gemma-7b\", \"google/gemma-7b-it\", \"google/gemma-1.1-2b-it\", \"google/gemma-1.1-7b-it\"] {isTemplate:true}\n",
-        "job_name = get_job_name_with_datetime(prefix=\"gemma-eval\")\n",
+        "job_name = common_util.get_job_name_with_datetime(prefix=\"gemma-eval\")\n",
         "eval_output_dir = os.path.join(MODEL_BUCKET, job_name)\n",
         "eval_output_dir_gcsfuse = eval_output_dir.replace(\"gs://\", \"/gcs/\")\n",
         "\n",
         "# @markdown Set the accelerator type.\n",
-        "accelerator_type = \"NVIDIA_L4\"  # @param[\"NVIDIA_TESLA_V100\", \"NVIDIA_L4\", \"NVIDIA_TESLA_A100\"]\n",
+        "accelerator_type = (\n",
+        "    \"NVIDIA_L4\"  # @param[\"NVIDIA_TESLA_V100\", \"NVIDIA_L4\", \"NVIDIA_TESLA_A100\"]\n",
+        ")\n",
         "\n",
         "# @markdown To evaluate a PEFT-finetuned model, enter the PEFT output directory to the LoRA adapter below.\n",
         "# @markdown Otherwise, leave it empty.\n",
@@ -313,11 +235,13 @@
         "\n",
         "replica_count = 1\n",
         "\n",
-        "check_quota(project_id=PROJECT_ID,\n",
-        "            region=REGION,\n",
-        "            accelerator_type=accelerator_type,\n",
-        "            accelerator_count=accelerator_count,\n",
-        "            is_for_training=True)\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=True,\n",
+        ")\n",
         "\n",
         "# Prepare evaluation command that runs the evaluation harness.\n",
         "# Set `trust_remote_code = True` because evaluating the model requires\n",
@@ -409,6 +333,17 @@
         "print(f\"Evaluation result:\\n{result_formatted}\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "execution_count": null,
+      "metadata": {
+        "id": "unjukbcjEBOd"
+      },
+      "outputs": [],
+      "source": [
+        "## Clean up resources"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -418,7 +353,6 @@
       },
       "outputs": [],
       "source": [
-        "# @title Clean up resources\n",
         "# Delete evaluation job.\n",
         "\n",
         "delete_bucket = False  # @param {type:\"boolean\"}\n",

notebooks/community/model_garden/model_garden_gemma_finetuning_on_vertex.ipynb

@@ -3,6 +3,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "id": "7d9bbf86da5e"
       },
@@ -25,6 +26,7 @@
     },
     {
       "cell_type": "markdown",
+      "language": "markdown",
       "metadata": {
         "id": "99c1c3fc2ca5"
       },
@@ -87,6 +89,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "cellView": "form",
         "id": "855d6b96f291"
@@ -94,51 +97,76 @@
       "outputs": [],
       "source": [
         "# @title Setup Google Cloud project\n",
-        "\n",
         "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
         "\n",
-        "# @markdown **[Optional]** Set the GCS BUCKET_URI to store the experiment artifacts, if you want to use your own bucket. **If not set, a unique GCS bucket will be created automatically on your behalf**.\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
         "\n",
-        "import json\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
         "import os\n",
-        "import uuid\n",
         "from datetime import datetime\n",
         "from typing import Tuple\n",
         "\n",
         "from google.cloud import aiplatform\n",
         "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
         "# Get the default cloud project id.\n",
         "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
         "\n",
         "# Get the default region for launching jobs.\n",
         "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
         "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
         "# Cloud Storage bucket for storing the experiment artifacts.\n",
         "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
-        "# prefer using your own GCS bucket, please change the value yourself below.\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
         "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
         "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
-        "if not BUCKET_URI.strip() or BUCKET_URI == \"gs://\":\n",
-        "    # Create a unique GCS bucket for this notebook if not specified\n",
-        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
         "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
-        "assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"gemma\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
         "\n",
         "# Gets the default SERVICE_ACCOUNT.\n",
         "shell_output = ! gcloud projects describe $PROJECT_ID\n",
         "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
         "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
-        "\n",
         "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
-        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
         "\n",
         "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
         "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
         "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
         "\n",
-        "# Enable Vertex AI and Cloud Compute APIs.\n",
         "! gcloud config set project $PROJECT_ID\n",
-        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
         "\n",
         "# @markdown ## Access Gemma Models\n",
         "# @markdown For GPU based finetuning and serving, choose between accessing Gemma models on [Hugging Face](https://huggingface.co/)\n",
@@ -174,10 +202,6 @@
         "\n",
         "VERTEX_AI_MODEL_GARDEN_GEMMA = \"\"  # @param {type:\"string\", isTemplate:true}\n",
         "\n",
-        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
-        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"gemma\")\n",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
-        "\n",
         "if LOAD_MODEL_FROM == \"Google Cloud\":\n",
         "    assert (\n",
         "        VERTEX_AI_MODEL_GARDEN_GEMMA\n",
@@ -204,7 +228,7 @@
         "# and Hex-LLM serving.\n",
         "KERAS_TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/jax-keras-train-tpu:20240422_0939_RC00\"\n",
         "KERAS_MODEL_CONVERSION_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/jax-keras-model-conversion:20240422_0949_RC00\"\n",
-        "HEXLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/hex-llm-serve:20240220_0936_RC01\"\n",
+        "HEXLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/hex-llm-serve:deploy\"\n",
         "conversion_job = None\n",
         "\n",
         "# @markdown *--- Or ---*\n",
@@ -221,34 +245,31 @@
         "# @markdown ---\n",
         "\n",
         "\n",
-        "def get_job_name_with_datetime(prefix: str) -> str:\n",
-        "    \"\"\"Gets the job name with date time when triggering training or deployment\n",
-        "    jobs in Vertex AI.\n",
-        "    \"\"\"\n",
-        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
-        "\n",
-        "\n",
         "def deploy_model_vllm(\n",
         "    model_name: str,\n",
-        "    base_model_id: str,\n",
         "    model_id: str,\n",
         "    service_account: str,\n",
-        "    machine_type: str = \"g2-standard-12\",\n",
+        "    base_model_id: str = None,\n",
+        "    machine_type: str = \"g2-standard-8\",\n",
         "    accelerator_type: str = \"NVIDIA_L4\",\n",
         "    accelerator_count: int = 1,\n",
-        "    max_model_len: int = 8192,\n",
-        "    dtype: str = \"bfloat16\",\n",
+        "    gpu_memory_utilization: float = 0.9,\n",
+        "    max_model_len: int = 4096,\n",
+        "    dtype: str = \"auto\",\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
-        "    \"\"\"Deploys models with vLLM on GPU in Vertex AI.\"\"\"\n",
+        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
         "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
         "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
         "    vllm_args = [\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
         "        f\"--model={model_id}\",\n",
         "        f\"--tensor-parallel-size={accelerator_count}\",\n",
         "        \"--swap-space=16\",\n",
-        "        \"--gpu-memory-utilization=0.95\",\n",
+        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
         "        f\"--max-model-len={max_model_len}\",\n",
         "        f\"--dtype={dtype}\",\n",
         "        \"--disable-log-stats\",\n",
@@ -258,8 +279,12 @@
         "        \"MODEL_ID\": base_model_id,\n",
         "        \"DEPLOY_SOURCE\": \"notebook\",\n",
         "    }\n",
-        "    if HF_TOKEN:\n",
-        "        env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "    except:\n",
+        "        pass\n",
         "\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
@@ -273,7 +298,9 @@
         "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
         "        serving_container_deployment_timeout=7200,\n",
         "    )\n",
-        "\n",
+        "    print(\n",
+        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
+        "    )\n",
         "    model.deploy(\n",
         "        endpoint=endpoint,\n",
         "        machine_type=machine_type,\n",
@@ -282,38 +309,52 @@
         "        deploy_request_timeout=1800,\n",
         "        service_account=service_account,\n",
         "    )\n",
+        "    print(\"endpoint_name:\", endpoint.name)\n",
+        "\n",
         "    return model, endpoint\n",
         "\n",
         "\n",
         "def deploy_model_hexllm(\n",
         "    model_name: str,\n",
-        "    base_model_id: str,\n",
         "    model_id: str,\n",
         "    service_account: str,\n",
+        "    base_model_id: str = None,\n",
+        "    tensor_parallel_size: int = 1,\n",
         "    machine_type: str = \"ct5lp-hightpu-1t\",\n",
-        "    max_num_batched_tokens: int = 11264,\n",
-        "    tokens_pad_multiple: int = 1024,\n",
-        "    seqs_pad_multiple: int = 32,\n",
+        "    hbm_utilization_factor: float = 0.6,\n",
+        "    max_running_seqs: int = 256,\n",
+        "    endpoint_id: str = \"\",\n",
+        "    min_replica_count: int = 1,\n",
+        "    max_replica_count: int = 1,\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
         "    \"\"\"Deploys models with Hex-LLM on TPU in Vertex AI.\"\"\"\n",
-        "    endpoint = aiplatform.Endpoint.create(\n",
-        "        display_name=f\"{model_name}-endpoint\",\n",
-        "        location=TPU_DEPLOYMENT_REGION,\n",
-        "    )\n",
+        "    if endpoint_id:\n",
+        "        aip_endpoint_name = (\n",
+        "            f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_id}\"\n",
+        "        )\n",
+        "        endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "    else:\n",
+        "        endpoint = aiplatform.Endpoint.create(\n",
+        "            display_name=f\"{model_name}-endpoint\",\n",
+        "            location=TPU_DEPLOYMENT_REGION,\n",
+        "        )\n",
+        "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
+        "    if not tensor_parallel_size:\n",
+        "        tensor_parallel_size = int(machine_type[-2])\n",
         "\n",
-        "    num_tpu_chips = int(machine_type[-2])\n",
         "    hexllm_args = [\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
         "        \"--log_level=INFO\",\n",
         "        f\"--model={model_id}\",\n",
-        "        f\"--tensor_parallel_size={num_tpu_chips}\",\n",
-        "        \"--num_nodes=1\",\n",
-        "        \"--use_ray\",\n",
-        "        \"--batch_mode=continuous\",\n",
-        "        f\"--max_num_batched_tokens={max_num_batched_tokens}\",\n",
-        "        f\"--tokens_pad_multiple={tokens_pad_multiple}\",\n",
-        "        f\"--seqs_pad_multiple={seqs_pad_multiple}\",\n",
+        "        f\"--tensor_parallel_size={tensor_parallel_size}\",\n",
+        "        \"--enable_jit\",\n",
+        "        \"--load_format=auto\",\n",
+        "        f\"--hbm_utilization_factor={hbm_utilization_factor}\",\n",
+        "        f\"--max_running_seqs={max_running_seqs}\",\n",
         "    ]\n",
         "\n",
         "    env_vars = {\n",
@@ -323,14 +364,17 @@
         "        \"RAY_USAGE_STATS_ENABLED\": \"0\",\n",
         "        \"DEPLOY_SOURCE\": \"notebook\",\n",
         "    }\n",
-        "    if KAGGLE_USERNAME and KAGGLE_KEY:\n",
-        "        env_vars[\"KAGGLE_USERNAME\"] = KAGGLE_USERNAME\n",
-        "        env_vars[\"KAGGLE_KEY\"] = KAGGLE_KEY\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars.update({\"HF_TOKEN\": HF_TOKEN})\n",
+        "    except:\n",
+        "        pass\n",
         "\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=HEXLLM_DOCKER_URI,\n",
-        "        serving_container_command=[\"python\", \"-m\", \"hex_llm.entrypoints.api_server\"],\n",
+        "        serving_container_command=[\"python\", \"-m\", \"hex_llm.server.api_server\"],\n",
         "        serving_container_args=hexllm_args,\n",
         "        serving_container_ports=[7080],\n",
         "        serving_container_predict_route=\"/generate\",\n",
@@ -346,105 +390,10 @@
         "        machine_type=machine_type,\n",
         "        deploy_request_timeout=1800,\n",
         "        service_account=service_account,\n",
+        "        min_replica_count=min_replica_count,\n",
+        "        max_replica_count=max_replica_count,\n",
         "    )\n",
-        "    return model, endpoint\n",
-        "\n",
-        "\n",
-        "def get_quota(project_id: str, region: str, resource_id: str) -> int:\n",
-        "    \"\"\"Returns the quota for a resource in a region. Returns -1 if can not figure out the quota.\"\"\"\n",
-        "    service_endpoint = \"aiplatform.googleapis.com\"  # noqa: F841\n",
-        "    quota_list_output = !gcloud alpha services quota list --service=$service_endpoint  --consumer=projects/$project_id --filter=\"$service_endpoint/$resource_id\" --format=json\n",
-        "    # Use '.s' on the command output because it is an SList type.\n",
-        "    quota_data = json.loads(quota_list_output.s)\n",
-        "    if len(quota_data) == 0 or \"consumerQuotaLimits\" not in quota_data[0]:\n",
-        "        return -1\n",
-        "    if (\n",
-        "        len(quota_data[0][\"consumerQuotaLimits\"]) == 0\n",
-        "        or \"quotaBuckets\" not in quota_data[0][\"consumerQuotaLimits\"][0]\n",
-        "    ):\n",
-        "        return -1\n",
-        "    all_regions_data = quota_data[0][\"consumerQuotaLimits\"][0][\"quotaBuckets\"]\n",
-        "    for region_data in all_regions_data:\n",
-        "        if (\n",
-        "            region_data.get(\"dimensions\")\n",
-        "            and region_data[\"dimensions\"][\"region\"] == region\n",
-        "        ):\n",
-        "            if \"effectiveLimit\" in region_data:\n",
-        "                return int(region_data[\"effectiveLimit\"])\n",
-        "            else:\n",
-        "                return 0\n",
-        "    return -1\n",
-        "\n",
-        "\n",
-        "def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:\n",
-        "    \"\"\"Returns the resource id for a given accelerator type and the use case.\n",
-        "    Args:\n",
-        "      accelerator_type: The accelerator type.\n",
-        "      is_for_training: Whether the resource is used for training. Set false\n",
-        "      for serving use case.\n",
-        "    Returns:\n",
-        "      The resource id.\n",
-        "    \"\"\"\n",
-        "    training_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_training_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_training_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_training_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_training_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_training_tpu_v5e\",\n",
-        "        \"TPU_V3\": \"custom_model_training_tpu_v3\",\n",
-        "    }\n",
-        "    serving_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_serving_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_serving_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_serving_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_serving_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_serving_tpu_v5e\",\n",
-        "    }\n",
-        "    if is_for_training:\n",
-        "        if accelerator_type in training_accelerator_map:\n",
-        "            return training_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for training.\"\n",
-        "            )\n",
-        "    else:\n",
-        "        if accelerator_type in serving_accelerator_map:\n",
-        "            return serving_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for serving.\"\n",
-        "            )\n",
-        "\n",
-        "\n",
-        "def check_quota(\n",
-        "    project_id: str,\n",
-        "    region: str,\n",
-        "    accelerator_type: str,\n",
-        "    accelerator_count: int,\n",
-        "    is_for_training: bool,\n",
-        "):\n",
-        "    \"\"\"Checks if the project and the region has the required quota.\"\"\"\n",
-        "    resource_id = get_resource_id(accelerator_type, is_for_training)\n",
-        "    quota = get_quota(project_id, region, resource_id)\n",
-        "    quota_request_instruction = (\n",
-        "        \"Either use \"\n",
-        "        \"a different region or request additional quota. Follow \"\n",
-        "        \"instructions here \"\n",
-        "        \"https://cloud.google.com/docs/quotas/view-manage#requesting_higher_quota\"\n",
-        "        \" to check quota in a region or request additional quota for \"\n",
-        "        \"your project.\"\n",
-        "    )\n",
-        "    if quota == -1:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not found for: {resource_id} in {region}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )\n",
-        "    if quota < accelerator_count:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not enough for {resource_id} in {region}:\n",
-        "            {quota} < {accelerator_count}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )"
+        "    return model, endpoint"
       ]
     },
     {
@@ -582,7 +531,7 @@
         "\n",
         "    replica_count = 1\n",
         "\n",
-        "    check_quota(\n",
+        "    common_util.check_quota(\n",
         "        project_id=PROJECT_ID,\n",
         "        region=REGION,\n",
         "        accelerator_type=ACCELERATOR_TYPE,\n",
@@ -591,7 +540,7 @@
         "    )\n",
         "\n",
         "    # Setup training job.\n",
-        "    job_name = get_job_name_with_datetime(\"gemma-lora-train\")\n",
+        "    job_name = common_util.get_job_name_with_datetime(\"gemma-lora-train\")\n",
         "\n",
         "    # Pass training arguments and launch job.\n",
         "    train_job = aiplatform.CustomContainerTrainingJob(\n",
@@ -600,12 +549,12 @@
         "    )\n",
         "\n",
         "    # Create a GCS folder to store the LORA adapter.\n",
-        "    lora_adapter_dir = get_job_name_with_datetime(\"gemma-lora-adapter\")\n",
+        "    lora_adapter_dir = common_util.get_job_name_with_datetime(\"gemma-lora-adapter\")\n",
         "    lora_output_dir = os.path.join(STAGING_BUCKET, lora_adapter_dir)\n",
         "\n",
         "    # Create a GCS folder to store the merged model with the base model and the\n",
         "    # finetuned LORA adapter.\n",
-        "    merged_model_dir = get_job_name_with_datetime(\"gemma-merged-model\")\n",
+        "    merged_model_dir = common_util.get_job_name_with_datetime(\"gemma-merged-model\")\n",
         "    merged_model_output_dir = os.path.join(STAGING_BUCKET, merged_model_dir)\n",
         "\n",
         "    train_job.run(\n",
@@ -666,7 +615,7 @@
         "    accelerator_type = \"NVIDIA_L4\"\n",
         "    accelerator_count = 1\n",
         "\n",
-        "    check_quota(\n",
+        "    common_util.check_quota(\n",
         "        project_id=PROJECT_ID,\n",
         "        region=REGION,\n",
         "        accelerator_type=accelerator_type,\n",
@@ -678,7 +627,7 @@
         "    max_model_len = 2048\n",
         "\n",
         "    model, endpoint = deploy_model_vllm(\n",
-        "        model_name=get_job_name_with_datetime(prefix=\"gemma-vllm-serve\"),\n",
+        "        model_name=common_util.get_job_name_with_datetime(prefix=\"gemma-vllm-serve\"),\n",
         "        base_model_id=f\"google/{MODEL_ID}\",\n",
         "        model_id=merged_model_output_dir,\n",
         "        service_account=SERVICE_ACCOUNT,\n",
@@ -700,6 +649,8 @@
       "outputs": [],
       "source": [
         "# @title Predict\n",
+        "\n",
+        "\n",
         "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts.\n",
         "\n",
         "# @markdown Here we use an example from the [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) to show the finetuning outcome:\n",
@@ -905,7 +856,7 @@
         "\n",
         "    replica_count = 1\n",
         "\n",
-        "    check_quota(\n",
+        "    common_util.check_quota(\n",
         "        project_id=PROJECT_ID,\n",
         "        region=REGION,\n",
         "        accelerator_type=accelerator_type,\n",
@@ -914,7 +865,7 @@
         "    )\n",
         "\n",
         "    # Setup training job.\n",
-        "    job_name = get_job_name_with_datetime(\"gemma-keras-lora-train\")\n",
+        "    job_name = common_util.get_job_name_with_datetime(\"gemma-keras-lora-train\")\n",
         "\n",
         "    # Pass training arguments and launch job.\n",
         "    train_job = aiplatform.CustomContainerTrainingJob(\n",
@@ -1004,7 +955,7 @@
         "    replica_count = 1\n",
         "\n",
         "    # Setup training job.\n",
-        "    job_name = get_job_name_with_datetime(\"gemma-keras-model-conversion\")\n",
+        "    job_name = common_util.get_job_name_with_datetime(\"gemma-keras-model-conversion\")\n",
         "\n",
         "    # Pass training arguments and launch job.\n",
         "    conversion_job = aiplatform.CustomContainerTrainingJob(\n",
@@ -1065,7 +1016,7 @@
         "        # Note: 1 TPU-V5e chip has only 1 core.\n",
         "        accelerator_count = 4\n",
         "\n",
-        "    check_quota(\n",
+        "    common_util.check_quota(\n",
         "        project_id=PROJECT_ID,\n",
         "        region=REGION,\n",
         "        accelerator_type=accelerator_type,\n",
@@ -1084,7 +1035,7 @@
         "\n",
         "    print(\"Using model from: \", output_folder)\n",
         "    model, endpoint = deploy_model_hexllm(\n",
-        "        model_name=get_job_name_with_datetime(prefix=\"gemma-serve-hexllm\"),\n",
+        "        model_name=common_util.get_job_name_with_datetime(prefix=\"gemma-serve-hexllm\"),\n",
         "        base_model_id=f\"google/{KAGGLE_MODEL_ID}\",\n",
         "        model_id=output_folder,\n",
         "        service_account=SERVICE_ACCOUNT,\n",
@@ -1099,6 +1050,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "cellView": "form",
         "id": "lgJPw3e9yL5a"
@@ -1183,14 +1135,19 @@
         "if conversion_job:\n",
         "    conversion_job.delete()\n",
         "\n",
+        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
+        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
+        "\n",
         "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
         "\n",
         "# Delete models.\n",
-        "model.delete()\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
         "\n",
-        "# Delete Cloud Storage bucket that was created.\n",
-        "if BUCKET_URI == f\"gs://{PROJECT_ID}-tmp-{now}\":\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/community/model_garden/model_garden_llama_guard_deployment.ipynb

@@ -0,0 +1,503 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7d9bbf86da5e"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "99c1c3fc2ca5"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Llama Guard\n",
+        "\n",
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_llama_guard_deployment.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_llama_guard_deployment.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3de7470326a2"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates downloading and deploying prebuilt [Llama Guard models](https://huggingface.co/meta-llama) with [vLLM](https://github.com/vllm-project/vllm) on GPU, and demonstrates using the Llama Guard model to safeguard LLM inputs and outputs with the Vertex Llama 3.1 API service.\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Download and deploy prebuilt Llama Guard models with [vLLM](https://github.com/vllm-project/vllm) on GPU\n",
+        "- Use the Llama Guard models to safeguard LLM inputs and outputs with the Vertex Llama 3.1 API service\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "264c07757582"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YXFGIp1l-qtT"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Setup Google Cloud project\n",
+        "\n",
+        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
+        "\n",
+        "# Import the necessary packages\n",
+        "\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
+        "import os\n",
+        "import re\n",
+        "from datetime import datetime\n",
+        "from typing import Tuple\n",
+        "\n",
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
+        "# Get the default cloud project id.\n",
+        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
+        "\n",
+        "# Get the default region for launching jobs.\n",
+        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
+        "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
+        "# Cloud Storage bucket for storing the experiment artifacts.\n",
+        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
+        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
+        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = BUCKET_URI\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "\n",
+        "# Gets the default SERVICE_ACCOUNT.\n",
+        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
+        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
+        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
+        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
+        "\n",
+        "\n",
+        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
+        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
+        "\n",
+        "! gcloud config set project $PROJECT_ID\n",
+        "\n",
+        "# @markdown # Access Llama Guard models on Vertex AI\n",
+        "# @markdown The original models from Meta are converted into the Hugging Face format for serving in Vertex AI.\n",
+        "# @markdown Accept the model agreement to access the models:\n",
+        "# @markdown 1. Open the [Llama Guard model card](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama-guard) from [Vertex AI Model Garden](https://cloud.google.com/model-garden).\n",
+        "# @markdown 2. Review and accept the agreement in the pop-up window on the model card page. If you have previously accepted the model agreement, there will not be a pop-up window on the model card page and this step is not needed.\n",
+        "# @markdown 3. After accepting the agreement, a `gs://` URI containing Llama Guard pretrained and finetuned models will be shared.\n",
+        "# @markdown 4. Paste the URI in the `VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD` field below.\n",
+        "# @markdown 5. The Llama Guard models will be copied into `BUCKET_URI`.\n",
+        "\n",
+        "\n",
+        "VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD = \"\"  # @param {type:\"string\", isTemplate:true}\n",
+        "assert (\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD\n",
+        "), \"Please click the agreement in Vertex AI Model Garden at https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama-guard, and get the GCS path of Llama Guard model artifacts.\"\n",
+        "parsed_gcs_url = re.search(\"gs://.*?(?=[ ]|$)\", VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD)\n",
+        "if parsed_gcs_url:\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD = parsed_gcs_url.group()\n",
+        "assert VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD.startswith(\n",
+        "    \"gs://\"\n",
+        "), \"VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD is expected to be a GCS URI and must start with `gs://`.\"\n",
+        "print(\n",
+        "    \"Copying Llama Guard model artifacts from\",\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD,\n",
+        "    \"to \",\n",
+        "    MODEL_BUCKET,\n",
+        ")\n",
+        "\n",
+        "! gsutil -m cp -R $VERTEX_AI_MODEL_GARDEN_LLAMA_GUARD $MODEL_BUCKET\n",
+        "\n",
+        "# The pre-built serving docker images.\n",
+        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240726_1329_RC00\"\n",
+        "\n",
+        "\n",
+        "def deploy_model_vllm(\n",
+        "    model_name: str,\n",
+        "    model_id: str,\n",
+        "    service_account: str,\n",
+        "    base_model_id: str = None,\n",
+        "    machine_type: str = \"g2-standard-8\",\n",
+        "    accelerator_type: str = \"NVIDIA_L4\",\n",
+        "    accelerator_count: int = 1,\n",
+        "    gpu_memory_utilization: float = 0.9,\n",
+        "    max_model_len: int = 4096,\n",
+        "    dtype: str = \"auto\",\n",
+        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
+        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
+        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
+        "    vllm_args = [\n",
+        "        \"python\",\n",
+        "        \"-m\",\n",
+        "        \"vllm.entrypoints.api_server\",\n",
+        "        \"--host=0.0.0.0\",\n",
+        "        \"--port=7080\",\n",
+        "        f\"--model={model_id}\",\n",
+        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
+        "        \"--swap-space=16\",\n",
+        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
+        "        f\"--max-model-len={max_model_len}\",\n",
+        "        f\"--dtype={dtype}\",\n",
+        "        \"--disable-log-stats\",\n",
+        "        \"--enforce-eager\",\n",
+        "        \"--disable-custom-all-reduce\",\n",
+        "        \"--enable-chunked-prefill\",\n",
+        "        \"--max-num-seqs=12\",\n",
+        "    ]\n",
+        "\n",
+        "    env_vars = {\n",
+        "        \"MODEL_ID\": base_model_id,\n",
+        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
+        "    }\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "    except:\n",
+        "        pass\n",
+        "\n",
+        "    model = aiplatform.Model.upload(\n",
+        "        display_name=model_name,\n",
+        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
+        "        serving_container_args=vllm_args,\n",
+        "        serving_container_ports=[7080],\n",
+        "        serving_container_predict_route=\"/generate\",\n",
+        "        serving_container_health_route=\"/ping\",\n",
+        "        serving_container_environment_variables=env_vars,\n",
+        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
+        "        serving_container_deployment_timeout=7200,\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
+        "    )\n",
+        "    model.deploy(\n",
+        "        endpoint=endpoint,\n",
+        "        machine_type=machine_type,\n",
+        "        accelerator_type=accelerator_type,\n",
+        "        accelerator_count=accelerator_count,\n",
+        "        deploy_request_timeout=1800,\n",
+        "        service_account=service_account,\n",
+        "    )\n",
+        "    print(\"endpoint_name:\", endpoint.name)\n",
+        "\n",
+        "    return model, endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z-XybZjtgF9M"
+      },
+      "source": [
+        "## Deploy Llama Guard with vLLM on GPU"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E8OiHHNNE_wj"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Deploy\n",
+        "\n",
+        "# @markdown This section uploads prebuilt Llama Guard models to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model.\n",
+        "\n",
+        "# @markdown NVIDIA_L4 GPUs are used for demonstration. The serving efficiency of L4 GPUs is inferior to that of A100 GPUs, but L4 GPUs are nevertheless good serving solutions if you do not have A100 quota.\n",
+        "\n",
+        "# @markdown Set the model to deploy.\n",
+        "\n",
+        "MODEL_ID = \"Llama-Guard-3-8B\"  # @param [\"Llama-Guard-3-8B\"] {allow-input: true, isTemplate: true}\n",
+        "model_id = os.path.join(MODEL_BUCKET, MODEL_ID)\n",
+        "\n",
+        "# @markdown Find Vertex AI prediction supported accelerators and regions at https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
+        "accelerator_type = \"NVIDIA_L4\"  # @param [\"NVIDIA_L4\", \"NVIDIA_TESLA_A100\"]\n",
+        "\n",
+        "if accelerator_type == \"NVIDIA_L4\":\n",
+        "    machine_type = \"g2-standard-12\"\n",
+        "    accelerator_count = 1\n",
+        "elif accelerator_type == \"NVIDIA_TESLA_A100\":\n",
+        "    machine_type = \"a2-highgpu-1g\"\n",
+        "    accelerator_count = 1\n",
+        "else:\n",
+        "    raise ValueError(\n",
+        "        f\"Recommended GPU setting not found for: {accelerator_type} and {MODEL_ID}.\"\n",
+        "    )\n",
+        "\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=False,\n",
+        ")\n",
+        "\n",
+        "gpu_memory_utilization = 0.9\n",
+        "max_model_len = 32768  # Maximum context length.\n",
+        "\n",
+        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=MODEL_ID),\n",
+        "    model_id=model_id,\n",
+        "    service_account=SERVICE_ACCOUNT,\n",
+        "    machine_type=machine_type,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    gpu_memory_utilization=gpu_memory_utilization,\n",
+        "    max_model_len=max_model_len,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "192a021iB_DE"
+      },
+      "source": [
+        "## Use the Llama Guard models to safeguard LLM inputs and outputs with the Vertex Llama 3.1 API service\n",
+        "\n",
+        "We use [meta-llama/Llama-Guard-3-8B](https://huggingface.co/meta-llama/Llama-Guard-3-8B) to safeguard input and output conversations with the [Llama 3.1 405B Instruct model API service on Vertex](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3-405b-instruct-maas).\n",
+        "\n",
+        "Llama Guard 3 builds on the capabilities introduced with Llama Guard 2, adding three new categories, Defamation, Elections and Code Interpreter Abuse. Additionally this model is multilingual and a new prompt format is introduced, making Llama Guard 3’s prompt format consistent with Llama 3+ Instruct models.\n",
+        "\n",
+        "This section references [LlamaGuard.ipynb](https://colab.research.google.com/drive/16s0tlCSEDtczjPzdIK3jq0Le5LlnSYGf?usp=sharing) from [https://huggingface.co/meta-llama/LlamaGuard-7b](https://huggingface.co/meta-llama/LlamaGuard-7b)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fHC7INgjB_DF"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --upgrade --quiet openai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ajjcGNzhB_DF"
+      },
+      "outputs": [],
+      "source": [
+        "import google.auth\n",
+        "import openai\n",
+        "\n",
+        "# @markdown Set up the Llama 3.1 405B Instruct model API service.\n",
+        "\n",
+        "# Programmatically get an access token\n",
+        "creds, _ = google.auth.default(\n",
+        "    scopes=[\"https://www.googleapis.com/auth/cloud-platform\"]\n",
+        ")\n",
+        "auth_req = google.auth.transport.requests.Request()\n",
+        "creds.refresh(auth_req)\n",
+        "# Note: the credential lives for 1 hour by default (https://cloud.google.com/docs/authentication/token-types#at-lifetime); after expiration, it must be refreshed.\n",
+        "\n",
+        "client = openai.OpenAI(\n",
+        "    base_url=f\"https://us-central1-aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/openapi\",\n",
+        "    api_key=creds.token,\n",
+        ")\n",
+        "LLAMA3_405B_INSTRUCT = \"meta/llama3-405b-instruct-maas\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NvSfBcUUB_DF"
+      },
+      "outputs": [],
+      "source": [
+        "# @markdown Define input message in conversation and get output message from model.\n",
+        "\n",
+        "message_role = \"user\"  # @param {type: \"string\"}\n",
+        "message_content = \"What is a car?\"  # @param {type: \"string\"}\n",
+        "\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": message_role,\n",
+        "        \"content\": message_content,\n",
+        "    }\n",
+        "]\n",
+        "print(\"Conversation [turn 1]:\", messages)\n",
+        "\n",
+        "response = client.chat.completions.create(\n",
+        "    model=LLAMA3_405B_INSTRUCT,\n",
+        "    messages=messages,\n",
+        ")\n",
+        "print(\"Response:\", response)\n",
+        "\n",
+        "messages.append(\n",
+        "    {\n",
+        "        \"role\": response.choices[0].message.role,\n",
+        "        \"content\": response.choices[0].message.content,\n",
+        "    }\n",
+        ")\n",
+        "print(\"Conversation [turn 2]:\", messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y7-ym3GlB_DG"
+      },
+      "outputs": [],
+      "source": [
+        "# @markdown Use Llama Guard to classify the conversation: safe versus unsafe.\n",
+        "# @markdown Classification is performed on the last turn of the conversation.\n",
+        "# @markdown If the content is safe, the model will return `safe`. If the content is unsafe, the model will return `unsafe` and additionally the list of offending categories as a comma-separated list in a new line.\n",
+        "# @markdown Set `\"@requestFormat\": \"chatCompletions\"` to use the OpenAI chat completions format.\n",
+        "\n",
+        "instances = [\n",
+        "    {\n",
+        "        \"messages\": messages,\n",
+        "        \"@requestFormat\": \"chatCompletions\",\n",
+        "    },\n",
+        "]\n",
+        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
+        "\n",
+        "prediction = response.predictions[0]\n",
+        "print(prediction)\n",
+        "print(\"Llama Guard prediction:\", prediction[\"choices\"][0][\"message\"][\"content\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "956x4r7rsrza"
+      },
+      "source": [
+        "## Clean up resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "911406c1561e"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Delete the models and endpoints\n",
+        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
+        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
+        "\n",
+        "# Undeploy model and delete endpoint.\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
+        "\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_llama_guard_deployment.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/model_garden_pytorch_gemma_peft_finetuning_hf.ipynb

@@ -3,6 +3,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "language": "python",
       "metadata": {
         "id": "7d9bbf86da5e"
       },
@@ -94,55 +95,77 @@
       "outputs": [],
       "source": [
         "# @title Setup Google Cloud project\n",
-        "\n",
         "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
         "\n",
-        "# @markdown **[Optional]** Set the GCS BUCKET_URI to store the experiment artifacts, if you want to use your own bucket. **If not set, a unique GCS bucket will be created automatically on your behalf**.\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
         "\n",
-        "import json\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
         "import os\n",
         "import sys\n",
-        "import uuid\n",
         "from datetime import datetime\n",
         "from typing import Tuple\n",
         "\n",
         "from google.cloud import aiplatform, language\n",
         "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
         "# Get the default cloud project id.\n",
         "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
         "\n",
         "# Get the default region for launching jobs.\n",
         "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
         "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
         "# Cloud Storage bucket for storing the experiment artifacts.\n",
         "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
-        "# prefer using your own GCS bucket, please change the value yourself below.\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
         "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
         "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
-        "if not BUCKET_URI.strip() or BUCKET_URI == \"gs://\":\n",
-        "    # Create a unique GCS bucket for this notebook if not specified\n",
-        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
         "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
-        "assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"gemma\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
         "\n",
         "# Gets the default SERVICE_ACCOUNT.\n",
         "shell_output = ! gcloud projects describe $PROJECT_ID\n",
         "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
         "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
-        "\n",
         "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
-        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
         "\n",
         "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
         "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
         "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
         "\n",
-        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
-        "\n",
-        "# Enable Vertex AI, Cloud Compute, and Cloud Language APIs.\n",
         "! gcloud config set project $PROJECT_ID\n",
-        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com language.googleapis.com\n",
         "\n",
         "# @markdown ## Access Gemma Models\n",
         "\n",
@@ -161,13 +184,6 @@
         "    auth.authenticate_user(project_id=PROJECT_ID)\n",
         "\n",
         "\n",
-        "def get_job_name_with_datetime(prefix: str) -> str:\n",
-        "    \"\"\"Gets the job name with date time when triggering training or deployment\n",
-        "    jobs in Vertex AI.\n",
-        "    \"\"\"\n",
-        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
-        "\n",
-        "\n",
         "def moderate_text(text: str) -> language.ModerateTextResponse:\n",
         "    \"\"\"Calls Vertex AI APIs to analyze text moderations.\"\"\"\n",
         "    client = language.LanguageServiceClient()\n",
@@ -236,104 +252,7 @@
         "        deploy_request_timeout=1800,\n",
         "        service_account=service_account,\n",
         "    )\n",
-        "    return model, endpoint\n",
-        "\n",
-        "\n",
-        "def get_quota(project_id: str, region: str, resource_id: str) -> int:\n",
-        "    \"\"\"Returns the quota for a resource in a region. Returns -1 if can not figure out the quota.\"\"\"\n",
-        "    service_endpoint = \"aiplatform.googleapis.com\"  # noqa: F841\n",
-        "    quota_list_output = !gcloud alpha services quota list --service=$service_endpoint  --consumer=projects/$project_id --filter=\"$service_endpoint/$resource_id\" --format=json\n",
-        "    # Use '.s' on the command output because it is an SList type.\n",
-        "    quota_data = json.loads(quota_list_output.s)\n",
-        "    if len(quota_data) == 0 or \"consumerQuotaLimits\" not in quota_data[0]:\n",
-        "        return -1\n",
-        "    if (\n",
-        "        len(quota_data[0][\"consumerQuotaLimits\"]) == 0\n",
-        "        or \"quotaBuckets\" not in quota_data[0][\"consumerQuotaLimits\"][0]\n",
-        "    ):\n",
-        "        return -1\n",
-        "    all_regions_data = quota_data[0][\"consumerQuotaLimits\"][0][\"quotaBuckets\"]\n",
-        "    for region_data in all_regions_data:\n",
-        "        if (\n",
-        "            region_data.get(\"dimensions\")\n",
-        "            and region_data[\"dimensions\"][\"region\"] == region\n",
-        "        ):\n",
-        "            if \"effectiveLimit\" in region_data:\n",
-        "                return int(region_data[\"effectiveLimit\"])\n",
-        "            else:\n",
-        "                return 0\n",
-        "    return -1\n",
-        "\n",
-        "\n",
-        "def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:\n",
-        "    \"\"\"Returns the resource id for a given accelerator type and the use case.\n",
-        "    Args:\n",
-        "      accelerator_type: The accelerator type.\n",
-        "      is_for_training: Whether the resource is used for training. Set false\n",
-        "      for serving use case.\n",
-        "    Returns:\n",
-        "      The resource id.\n",
-        "    \"\"\"\n",
-        "    training_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_training_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_training_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_training_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_training_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_training_tpu_v5e\",\n",
-        "        \"TPU_V3\": \"custom_model_training_tpu_v3\",\n",
-        "    }\n",
-        "    serving_accelerator_map = {\n",
-        "        \"NVIDIA_TESLA_V100\": \"custom_model_serving_nvidia_v100_gpus\",\n",
-        "        \"NVIDIA_L4\": \"custom_model_serving_nvidia_l4_gpus\",\n",
-        "        \"NVIDIA_TESLA_A100\": \"custom_model_serving_nvidia_a100_gpus\",\n",
-        "        \"NVIDIA_TESLA_T4\": \"custom_model_serving_nvidia_t4_gpus\",\n",
-        "        \"TPU_V5e\": \"custom_model_serving_tpu_v5e\",\n",
-        "    }\n",
-        "    if is_for_training:\n",
-        "        if accelerator_type in training_accelerator_map:\n",
-        "            return training_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for training.\"\n",
-        "            )\n",
-        "    else:\n",
-        "        if accelerator_type in serving_accelerator_map:\n",
-        "            return serving_accelerator_map[accelerator_type]\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not find accelerator type: {accelerator_type} for serving.\"\n",
-        "            )\n",
-        "\n",
-        "\n",
-        "def check_quota(\n",
-        "    project_id: str,\n",
-        "    region: str,\n",
-        "    accelerator_type: str,\n",
-        "    accelerator_count: int,\n",
-        "    is_for_training: bool,\n",
-        "):\n",
-        "    \"\"\"Checks if the project and the region has the required quota.\"\"\"\n",
-        "    resource_id = get_resource_id(accelerator_type, is_for_training)\n",
-        "    quota = get_quota(project_id, region, resource_id)\n",
-        "    quota_request_instruction = (\n",
-        "        \"Either use \"\n",
-        "        \"a different region or request additional quota. Follow \"\n",
-        "        \"instructions here \"\n",
-        "        \"https://cloud.google.com/docs/quotas/view-manage#requesting_higher_quota\"\n",
-        "        \" to check quota in a region or request additional quota for \"\n",
-        "        \"your project.\"\n",
-        "    )\n",
-        "    if quota == -1:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not found for: {resource_id} in {region}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )\n",
-        "    if quota < accelerator_count:\n",
-        "        raise ValueError(\n",
-        "            f\"\"\"Quota not enough for {resource_id} in {region}:\n",
-        "            {quota} < {accelerator_count}.\n",
-        "            {quota_request_instruction}\"\"\"\n",
-        "        )"
+        "    return model, endpoint"
       ]
     },
     {
@@ -480,7 +399,7 @@
         "\n",
         "replica_count = 1\n",
         "\n",
-        "check_quota(\n",
+        "common_util.check_quota(\n",
         "    project_id=PROJECT_ID,\n",
         "    region=REGION,\n",
         "    accelerator_type=ACCELERATOR_TYPE,\n",
@@ -489,7 +408,7 @@
         ")\n",
         "\n",
         "# Setup training job.\n",
-        "job_name = get_job_name_with_datetime(\"gemma-lora-train\")\n",
+        "job_name = common_util.get_job_name_with_datetime(\"gemma-lora-train\")\n",
         "\n",
         "# Pass training arguments and launch job.\n",
         "train_job = aiplatform.CustomContainerTrainingJob(\n",
@@ -498,12 +417,12 @@
         ")\n",
         "\n",
         "# Create a GCS folder to store the LORA adapter.\n",
-        "lora_adapter_dir = get_job_name_with_datetime(\"gemma-lora-adapter\")\n",
+        "lora_adapter_dir = common_util.get_job_name_with_datetime(\"gemma-lora-adapter\")\n",
         "lora_output_dir = os.path.join(STAGING_BUCKET, lora_adapter_dir)\n",
         "\n",
         "# Create a GCS folder to store the merged model with the base model and the\n",
         "# finetuned LORA adapter.\n",
-        "merged_model_dir = get_job_name_with_datetime(\"gemma-merged-model\")\n",
+        "merged_model_dir = common_util.get_job_name_with_datetime(\"gemma-merged-model\")\n",
         "merged_model_output_dir = os.path.join(STAGING_BUCKET, merged_model_dir)\n",
         "\n",
         "train_job.run(\n",
@@ -560,7 +479,7 @@
         "accelerator_type = \"NVIDIA_L4\"\n",
         "accelerator_count = 1\n",
         "\n",
-        "check_quota(\n",
+        "common_util.check_quota(\n",
         "    project_id=PROJECT_ID,\n",
         "    region=REGION,\n",
         "    accelerator_type=accelerator_type,\n",
@@ -573,8 +492,8 @@
         "max_total_tokens = 1024\n",
         "max_batch_prefill_tokens = 2048\n",
         "\n",
-        "model, endpoint = deploy_model_tgi(\n",
-        "    model_name=get_job_name_with_datetime(prefix=\"gemma-tgi-serve\"),\n",
+        "models[\"tgi\"], endpoints[\"tgi\"] = deploy_model_tgi(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=\"gemma-tgi-serve\"),\n",
         "    model_id=merged_model_output_dir,\n",
         "    service_account=SERVICE_ACCOUNT,\n",
         "    machine_type=machine_type,\n",
@@ -583,8 +502,7 @@
         "    max_input_length=max_input_length,\n",
         "    max_total_tokens=max_total_tokens,\n",
         "    max_batch_prefill_tokens=max_batch_prefill_tokens,\n",
-        ")\n",
-        "print(\"endpoint_name:\", endpoint.name)"
+        ")"
       ]
     },
     {
@@ -641,7 +559,7 @@
         "        },\n",
         "    },\n",
         "]\n",
-        "response = endpoint.predict(instances=instances)\n",
+        "response = endpoints[\"tgi\"].predict(instances=instances)\n",
         "\n",
         "for prediction in response.predictions:\n",
         "    print(prediction)"
@@ -686,15 +604,20 @@
         "# Delete the train job.\n",
         "train_job.delete()\n",
         "\n",
+        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
+        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
+        "\n",
         "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
         "\n",
         "# Delete models.\n",
-        "model.delete()\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
         "\n",
-        "# Delete Cloud Storage bucket that was created.\n",
-        "if BUCKET_URI == f\"gs://{PROJECT_ID}-tmp-{now}\":\n",
-        "    ! gsutil -m rm -r $STAGING_BUCKET"
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/community/model_garden/model_garden_pytorch_llama3_1_deployment.ipynb

@@ -0,0 +1,692 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7d9bbf86da5e"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "99c1c3fc2ca5"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Llama 3.1 (Deployment)\n",
+        "\n",
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_llama3_1_deployment.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama3_1_deployment.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3de7470326a2"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates downloading, deploying, and serving prebuilt Llama 3.1 models with [Hex-LLM](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-hex-llm) or [vLLM](https://github.com/vllm-project/vllm).\n",
+        "\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Deploy Llama 3.1 8B with Hex-LLM on TPU.\n",
+        "- Deploy Llama 3.1 70B and 405B with vLLM on GPU, optionally with dynamic LoRA adapters.\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "264c07757582"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6fe2644d854f"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Accept the model agreement to access the models\n",
+        "\n",
+        "# @markdown 1. Open the [Llama 3.1 model card](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3_1) from [Vertex AI Model Garden](https://cloud.google.com/model-garden).\n",
+        "\n",
+        "# @markdown 2. Review and accept the agreement in the pop-up window on the model card page. If you have previously accepted the model agreement, there will not be a pop-up window on the model card page and this step is not needed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "ax7zWynUDcjk"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Request for quota\n",
+        "\n",
+        "# @markdown By default, the quota for TPU deployment `Custom model serving TPU v5e cores per region` is 4. TPU quota is only available in `us-west1`. You can request for higher TPU quota following the instructions at [\"Request a higher quota\"](https://cloud.google.com/docs/quota/view-manage#requesting_higher_quota).\n",
+        "\n",
+        "# @markdown By default, the quota for H100 deployment `Custom model serving per region` is 0. You need to request for H100 quota following the instructions at [\"Request a higher quota\"](https://cloud.google.com/docs/quota/view-manage#requesting_higher_quota)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YXFGIp1l-qtT"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Setup Google Cloud project\n",
+        "# Import the necessary packages\n",
+        "\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
+        "import os\n",
+        "import re\n",
+        "from datetime import datetime\n",
+        "from typing import Tuple\n",
+        "\n",
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
+        "\n",
+        "# Get the default cloud project id.\n",
+        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
+        "\n",
+        "# Get the default region for launching jobs.\n",
+        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
+        "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
+        "# Cloud Storage bucket for storing the experiment artifacts.\n",
+        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
+        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
+        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"llama_3_1\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "\n",
+        "# Gets the default SERVICE_ACCOUNT.\n",
+        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
+        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
+        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
+        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
+        "\n",
+        "\n",
+        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
+        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
+        "\n",
+        "! gcloud config set project $PROJECT_ID\n",
+        "\n",
+        "# @markdown # Access Llama 3.1 models on Vertex AI for serving\n",
+        "# @markdown The original models from Meta are converted into the Hugging Face format for serving in Vertex AI.\n",
+        "# @markdown Accept the model agreement to access the models:\n",
+        "# @markdown 1. Open the [Llama 3.1 model card](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3_1) from [Vertex AI Model Garden](https://cloud.google.com/model-garden).\n",
+        "# @markdown 2. Review and accept the agreement in the pop-up window on the model card page. If you have previously accepted the model agreement, there will not be a pop-up window on the model card page and this step is not needed.\n",
+        "# @markdown 3. After accepting the agreement of Llama 3.1, a `gs://` URI containing Llama 3.1 pretrained and finetuned models will be shared.\n",
+        "# @markdown 4. Paste the URI in the `VERTEX_AI_MODEL_GARDEN_LLAMA_3_1` field below.\n",
+        "# @markdown 5. The Llama 3.1 models will be copied into `BUCKET_URI`.\n",
+        "\n",
+        "\n",
+        "VERTEX_AI_MODEL_GARDEN_LLAMA_3_1 = \"\"  # @param {type:\"string\", isTemplate:true}\n",
+        "assert (\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_3_1\n",
+        "), \"Please click the agreement of Llama 3.1 in Vertex AI Model Garden, and get the GCS path of Llama 3.1 model artifacts.\"\n",
+        "parsed_gcs_url = re.search(\"gs://.*?(?=[ ]|$)\", VERTEX_AI_MODEL_GARDEN_LLAMA_3_1)\n",
+        "if parsed_gcs_url:\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_3_1 = parsed_gcs_url.group()\n",
+        "assert VERTEX_AI_MODEL_GARDEN_LLAMA_3_1.startswith(\n",
+        "    \"gs://\"\n",
+        "), \"VERTEX_AI_MODEL_GARDEN_LLAMA_3_1 is expected to be a GCS URI and must start with `gs://`.\"\n",
+        "print(\n",
+        "    \"Copying LLaMA3 model artifacts from\",\n",
+        "    VERTEX_AI_MODEL_GARDEN_LLAMA_3_1,\n",
+        "    \"to \",\n",
+        "    MODEL_BUCKET,\n",
+        ")\n",
+        "\n",
+        "! gsutil -m cp -R $VERTEX_AI_MODEL_GARDEN_LLAMA_3_1/* $MODEL_BUCKET\n",
+        "\n",
+        "# The pre-built serving docker images.\n",
+        "HEXLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/hex-llm-serve:llama3.1\"\n",
+        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240726_1329_RC00\"\n",
+        "\n",
+        "SERVICE_ENDPOINT = \"aiplatform.googleapis.com\"\n",
+        "\n",
+        "\n",
+        "def deploy_model_hexllm(\n",
+        "    model_name: str,\n",
+        "    model_id: str,\n",
+        "    service_account: str,\n",
+        "    machine_type: str = \"ct5lp-hightpu-4t\",\n",
+        "    tensor_parallel_size: int = 4,\n",
+        "    hbm_utilization_factor: float = 0.8,\n",
+        "    max_running_seqs: int = 256,\n",
+        "    max_model_len: int = 8192,\n",
+        "    endpoint_id: str = \"\",\n",
+        "    min_replica_count: int = 1,\n",
+        "    max_replica_count: int = 1,\n",
+        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
+        "    \"\"\"Deploys models with Hex-LLM on TPU in Vertex AI.\"\"\"\n",
+        "    if endpoint_id:\n",
+        "        aip_endpoint_name = (\n",
+        "            f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_id}\"\n",
+        "        )\n",
+        "        endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "    else:\n",
+        "        endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "\n",
+        "    hexllm_args = [\n",
+        "        \"--host=0.0.0.0\",\n",
+        "        \"--port=7080\",\n",
+        "        \"--log_level=INFO\",\n",
+        "        \"--enable_jit\",\n",
+        "        f\"--model={model_id}\",\n",
+        "        \"--load_format=auto\",\n",
+        "        f\"--tensor_parallel_size={tensor_parallel_size}\",\n",
+        "        f\"--hbm_utilization_factor={hbm_utilization_factor}\",\n",
+        "        f\"--max_running_seqs={max_running_seqs}\",\n",
+        "        f\"--max_model_len={max_model_len}\",\n",
+        "        \"--max-num-seqs=12\",\n",
+        "    ]\n",
+        "    hexllm_envs = {\n",
+        "        \"PJRT_DEVICE\": \"TPU\",\n",
+        "        \"RAY_DEDUP_LOGS\": \"0\",\n",
+        "        \"RAY_USAGE_STATS_ENABLED\": \"0\",\n",
+        "        \"MODEL_ID\": model_id,\n",
+        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
+        "    }\n",
+        "\n",
+        "    model = aiplatform.Model.upload(\n",
+        "        display_name=model_name,\n",
+        "        serving_container_image_uri=HEXLLM_DOCKER_URI,\n",
+        "        serving_container_command=[\"python\", \"-m\", \"hex_llm.server.api_server\"],\n",
+        "        serving_container_args=hexllm_args,\n",
+        "        serving_container_ports=[7080],\n",
+        "        serving_container_predict_route=\"/generate\",\n",
+        "        serving_container_health_route=\"/ping\",\n",
+        "        serving_container_environment_variables=hexllm_envs,\n",
+        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
+        "        serving_container_deployment_timeout=7200,\n",
+        "    )\n",
+        "\n",
+        "    model.deploy(\n",
+        "        endpoint=endpoint,\n",
+        "        machine_type=machine_type,\n",
+        "        deploy_request_timeout=1800,\n",
+        "        service_account=service_account,\n",
+        "        min_replica_count=min_replica_count,\n",
+        "        max_replica_count=max_replica_count,\n",
+        "    )\n",
+        "    return model, endpoint\n",
+        "\n",
+        "\n",
+        "def deploy_model_vllm(\n",
+        "    model_name: str,\n",
+        "    model_id: str,\n",
+        "    service_account: str,\n",
+        "    machine_type: str = \"g2-standard-8\",\n",
+        "    accelerator_type: str = \"NVIDIA_L4\",\n",
+        "    accelerator_count: int = 1,\n",
+        "    gpu_memory_utilization: float = 0.9,\n",
+        "    max_model_len: int = 8192,\n",
+        "    max_loras: int = 1,\n",
+        "    max_cpu_loras: int = 16,\n",
+        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
+        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
+        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "\n",
+        "    vllm_args = [\n",
+        "        \"python\",\n",
+        "        \"-m\",\n",
+        "        \"vllm.entrypoints.api_server\",\n",
+        "        \"--host=0.0.0.0\",\n",
+        "        \"--port=7080\",\n",
+        "        f\"--model={model_id}\",\n",
+        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
+        "        \"--swap-space=16\",\n",
+        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
+        "        f\"--max-model-len={max_model_len}\",\n",
+        "        \"--enable-lora\",\n",
+        "        \"--disable-custom-all-reduce\",\n",
+        "        f\"--max-loras={max_loras}\",\n",
+        "        f\"--max-cpu-loras={max_cpu_loras}\",\n",
+        "        \"--disable-log-stats\",\n",
+        "    ]\n",
+        "\n",
+        "    env_vars = {\"MODEL_ID\": model_id, \"DEPLOY_SOURCE\": \"notebook\"}\n",
+        "    model = aiplatform.Model.upload(\n",
+        "        display_name=model_name,\n",
+        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
+        "        serving_container_args=vllm_args,\n",
+        "        serving_container_ports=[7080],\n",
+        "        serving_container_predict_route=\"/generate\",\n",
+        "        serving_container_health_route=\"/ping\",\n",
+        "        serving_container_environment_variables=env_vars,\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
+        "    )\n",
+        "    model.deploy(\n",
+        "        endpoint=endpoint,\n",
+        "        machine_type=machine_type,\n",
+        "        accelerator_type=accelerator_type,\n",
+        "        accelerator_count=accelerator_count,\n",
+        "        deploy_request_timeout=1800,\n",
+        "        service_account=service_account,\n",
+        "    )\n",
+        "    print(\"endpoint_name:\", endpoint.name)\n",
+        "\n",
+        "    return model, endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mAt6NcA5Dcjl"
+      },
+      "source": [
+        "## Deploy prebuilt Llama 3.1 8B with Hex-LLM\n",
+        "\n",
+        "**Hex-LLM** is a **H**igh-**E**fficiency **L**arge **L**anguage **M**odel (LLM) TPU serving solution built with **XLA**, which is being developed by Google Cloud.\n",
+        "\n",
+        "Refer to the \"Request for TPU quota\" section for TPU quota."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9-5obzXZDcjl"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Deploy\n",
+        "\n",
+        "# @markdown This section uploads prebuilt Llama 3.1 models to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model.\n",
+        "\n",
+        "# @markdown Select one of the four model variations. More model variants will be supported by Hex-LLM in the future.\n",
+        "MODEL_ID = \"Meta-Llama-3.1-8B\"  # @param [\"Meta-Llama-3.1-8B\", \"Meta-Llama-3.1-8B-Instruct\"] {allow-input: true, isTemplate: true}\n",
+        "model_id = os.path.join(MODEL_BUCKET, MODEL_ID)\n",
+        "\n",
+        "# @markdown Find Vertex AI prediction TPUv5e machine types in\n",
+        "# @markdown https://cloud.google.com/vertex-ai/docs/predictions/use-tpu#deploy_a_model.\n",
+        "\n",
+        "# Sets ct5lp-hightpu-4t (4 TPU chips) to deploy Llama 3.1 8B models.\n",
+        "machine_type = \"ct5lp-hightpu-4t\"\n",
+        "accelerator_type = \"TPU_V5e\"\n",
+        "# Note: 1 TPU V5 chip has only one core.\n",
+        "accelerator_count = 4\n",
+        "\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=False,\n",
+        ")\n",
+        "\n",
+        "# Server parameters.\n",
+        "tensor_parallel_size = accelerator_count\n",
+        "hbm_utilization_factor = 0.8  # Fraction of HBM memory allocated for KV cache after model loading. A larger value improves throughput but gives higher risk of TPU out-of-memory errors with long prompts.\n",
+        "max_running_seqs = 256  # Maximum number of running sequences in a continuous batch.\n",
+        "max_model_len = 8192\n",
+        "\n",
+        "# Endpoint configurations.\n",
+        "min_replica_count = 1\n",
+        "max_replica_count = 1\n",
+        "\n",
+        "model_hexllm, endpoint_hexllm = deploy_model_hexllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=\"llama_3_1-hexllm-serve\"),\n",
+        "    model_id=model_id,\n",
+        "    service_account=SERVICE_ACCOUNT,\n",
+        "    machine_type=machine_type,\n",
+        "    tensor_parallel_size=tensor_parallel_size,\n",
+        "    hbm_utilization_factor=hbm_utilization_factor,\n",
+        "    max_running_seqs=max_running_seqs,\n",
+        "    max_model_len=max_model_len,\n",
+        "    min_replica_count=min_replica_count,\n",
+        "    max_replica_count=max_replica_count,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cEq8oadxDcjl"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Predict\n",
+        "\n",
+        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. The first few requests may have high latency. This is because the server needs to warm up with the initial requests. The following requests should not have the same delay.\n",
+        "\n",
+        "# @markdown Example:\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown > What is a car?\n",
+        "# @markdown > A car is a four-wheeled vehicle designed for the transportation of passengers and their belongings.\n",
+        "# @markdown ```\n",
+        "\n",
+        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
+        "\n",
+        "# Loads an existing endpoint instance using the endpoint name:\n",
+        "# - Using `endpoint_name = endpoint_hexllm.name` allows us to get the endpoint\n",
+        "#   name of the endpoint `endpoint_hexllm` created in the cell above.\n",
+        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
+        "#   an existing endpoint with the ID 1234567890123456789.\n",
+        "# You may uncomment the code below to load an existing endpoint:\n",
+        "# endpoint_name = endpoint_without_peft.name\n",
+        "# # endpoint_name = \"\"  # @param {type:\"string\"}\n",
+        "# aip_endpoint_name = (\n",
+        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
+        "# )\n",
+        "# endpoint_hexllm = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "\n",
+        "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
+        "max_tokens = 50  # @param {type: \"integer\"}\n",
+        "temperature = 1.0  # @param {type: \"number\"}\n",
+        "top_p = 1.0  # @param {type: \"number\"}\n",
+        "top_k = 1  # @param {type: \"integer\"}\n",
+        "instances = [\n",
+        "    {\n",
+        "        \"prompt\": prompt,\n",
+        "        \"max_tokens\": max_tokens,\n",
+        "        \"temperature\": temperature,\n",
+        "        \"top_p\": top_p,\n",
+        "        \"top_k\": top_k,\n",
+        "    },\n",
+        "]\n",
+        "response = endpoint_hexllm.predict(instances=instances)\n",
+        "\n",
+        "prediction = response.predictions[0]\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z-XybZjtgF9M"
+      },
+      "source": [
+        "## Deploy prebuilt Llama 3.1 70B and 405B with vLLM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E8OiHHNNE_wj"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Deploy\n",
+        "\n",
+        "# @markdown This section uploads prebuilt Llama 3.1 models to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model.\n",
+        "\n",
+        "# @markdown NVIDIA_L4 GPUs are used for demonstration. The serving efficiency of L4 GPUs is inferior to that of H100 GPUs, but L4 GPUs are nevertheless good serving solutions if you do not have H100 quota.\n",
+        "\n",
+        "# @markdown H100 is hard to get for now. It's recommended to use the deployment button in the model card. You can still try to deploy H100 endpoint through the notebook, but there is a chance that resource is not available.\n",
+        "\n",
+        "# @markdown Set the model to deploy.\n",
+        "\n",
+        "base_model_name = \"Meta-Llama-3.1-70B\"  # @param [\"Meta-Llama-3.1-70B\", \"Meta-Llama-3.1-70B-Instruct\", \"Meta-Llama-3.1-405B-FP8\", \"Meta-Llama-3.1-405B-Instruct-FP8\"] {isTemplate:true}\n",
+        "model_id = os.path.join(MODEL_BUCKET, base_model_name)\n",
+        "\n",
+        "# @markdown Find Vertex AI prediction supported accelerators and regions at https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
+        "\n",
+        "if \"70\" in base_model_name:\n",
+        "    accelerator_type = \"NVIDIA_L4\"\n",
+        "    machine_type = \"g2-standard-8\"\n",
+        "    accelerator_count = 8\n",
+        "elif \"405\" in base_model_name:\n",
+        "    accelerator_type = \"NVIDIA_H100_80GB\"\n",
+        "    machine_type = \"a3-highgpu-8g\"\n",
+        "    accelerator_count = 8\n",
+        "else:\n",
+        "    raise ValueError(\n",
+        "        f\"Recommended GPU setting not found for: {accelerator_type} and {base_model_name}.\"\n",
+        "    )\n",
+        "\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=False,\n",
+        ")\n",
+        "\n",
+        "gpu_memory_utilization = 0.9\n",
+        "max_model_len = 32768  # Maximum context length.\n",
+        "\n",
+        "model, endpoint = deploy_model_vllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=\"llama_3_1-vllm-serve\"),\n",
+        "    model_id=model_id,\n",
+        "    service_account=SERVICE_ACCOUNT,\n",
+        "    machine_type=machine_type,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    gpu_memory_utilization=gpu_memory_utilization,\n",
+        "    max_model_len=max_model_len,\n",
+        ")\n",
+        "# @markdown Click \"Show Code\" to see more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rDHsCOqvFYBi"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Predict\n",
+        "\n",
+        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. Sampling parameters supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/dev/sampling_params.html).\n",
+        "\n",
+        "# @markdown Example:\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown Q: What is a car?\n",
+        "# @markdown A:  A car, or a motor car, is a road-connected human-transportation system used to move people or goods from one place to another. The term also encompasses a wide range of vehicles, including motorboats, trains, and aircrafts. Cars typically have four wheels, a cabin for passengers, and an engine or motor. They have been around since the early 19th century and are now one of the most popular forms of transportation, used for daily commuting, shopping, and other purposes.\n",
+        "# @markdown ```\n",
+        "\n",
+        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
+        "\n",
+        "# @markdown NOTE: For the raw predict (non chat-completion API), a template like \"user:<input> assistant:\" is needed in the prompt to get a meaningful response for an instruct tuned model.\n",
+        "\n",
+        "prompt = \"user:What is a car? assistant:\"  # @param {type: \"string\"}\n",
+        "max_tokens = 50  # @param {type:\"integer\"}\n",
+        "temperature = 1.0  # @param {type:\"number\"}\n",
+        "top_p = 1.0  # @param {type:\"number\"}\n",
+        "top_k = 1  # @param {type:\"integer\"}\n",
+        "raw_response = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "# @markdown Optionally, you can apply LoRA weights to prediction. Set `lora_weight` to be either a GCS URI or a HuggingFace repo containing the LoRA weight.\n",
+        "lora_weight = \"\"  # @param {type:\"string\", isTemplate: true}\n",
+        "\n",
+        "# Overides parameters for inferences.\n",
+        "# If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`,\n",
+        "# you can reduce the maximum number of output tokens, such as set max_tokens as 20.\n",
+        "instances = [\n",
+        "    {\n",
+        "        \"prompt\": prompt,\n",
+        "        \"max_tokens\": max_tokens,\n",
+        "        \"temperature\": temperature,\n",
+        "        \"top_p\": top_p,\n",
+        "        \"top_k\": top_k,\n",
+        "        \"raw_response\": raw_response,\n",
+        "        \"dynamic-lora\": lora_weight,\n",
+        "    },\n",
+        "]\n",
+        "response = endpoint.predict(instances=instances)\n",
+        "\n",
+        "for prediction in response.predictions:\n",
+        "    print(prediction)\n",
+        "\n",
+        "# @markdown You can also use the `@requestFormat` parameter to send the OpenAI chat completions request.\n",
+        "\n",
+        "message_role = \"user\"  # @param {type: \"string\"}\n",
+        "message_content = \"What is a car?\"  # @param {type: \"string\"}\n",
+        "\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": message_role,\n",
+        "        \"content\": message_content,\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "instances = [\n",
+        "    {\n",
+        "        \"messages\": messages,\n",
+        "        \"@requestFormat\": \"chatCompletions\",\n",
+        "    },\n",
+        "]\n",
+        "response = endpoint.predict(instances=instances)\n",
+        "\n",
+        "for prediction in response.predictions:\n",
+        "    print(prediction)\n",
+        "\n",
+        "# @markdown Click \"Show Code\" to see more details."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vfK-kZmwV7Bb"
+      },
+      "source": [
+        "## Use Llama guard model\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Empf8V2GWJJI"
+      },
+      "source": [
+        "You can use the Llama Guard model together with the Llama 3.1 405B Instruct API. See the [Llama Guard model](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama-guard) for details."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JETd33jIDcjm"
+      },
+      "source": [
+        "## Clean up resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "911406c1561e"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Delete the models and endpoints\n",
+        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
+        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
+        "\n",
+        "# Undeploy model and delete endpoint.\n",
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()\n",
+        "\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_pytorch_llama3_1_deployment.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/model_garden_pytorch_llama3_1_finetuning.ipynb

@@ -0,0 +1,718 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "7d9bbf86da5e"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "99c1c3fc2ca5"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Llama 3.1 Finetuning\n",
+        "\n",
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_llama3_1_finetuning.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama3_1_finetuning.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3de7470326a2"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates finetuning and deploying Llama 3.1 models with Vertex AI. All of the examples in this notebook use parameter efficient finetuning methods [PEFT (LoRA)](https://github.com/huggingface/peft) to reduce training and storage costs. LoRA (Low-Rank Adaptation) is one approach of Parameter Efficient FineTuning (PEFT), where pretrained model weights are frozen and rank decomposition matrices representing the change in model weights are trained during finetuning. Read more about LoRA in the following publication: [Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L. and Chen, W., 2021. Lora: Low-rank adaptation of large language models. *arXiv preprint arXiv:2106.09685*](https://arxiv.org/abs/2106.09685).\n",
+        "\n",
+        "After finetuning, we can deploy models on Vertex with GPU.\n",
+        "\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Finetune Llama 3.1 models with Vertex AI Custom Training Jobs.\n",
+        "- Deploy finetuned Llama 3.1 models on Vertex AI Prediction.\n",
+        "- Send prediction requests to your finetuned Llama 3.1 models.\n",
+        "\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "264c07757582"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "855d6b96f291"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Setup Google Cloud project\n",
+        "\n",
+        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
+        "\n",
+        "# @markdown 3. [Make sure that you have GPU quota for Vertex Training (finetuing) and Vertex Prediction (serving)](https://cloud.google.com/docs/quotas/view-manage). The quota name for Vertex Training is \"Custom model training your-gpu-type per region\" and the quota name for Vertex Prediction is \"Custom model serving your-gpu-type per region\" such as `Custom model training Nvidia L4 GPUs per region` and `Custom model serving Nvidia L4 GPUs per region` for L4 GPUs. [Submit a quota increase request](https://cloud.google.com/docs/quotas/view-manage#requesting_higher_quota) if additional quota is needed. At minimum, running this notebook requires 4 L4s for finetuning and 1 L4 for serving. More GPUs may be needed for larger models and different finetuning configurations. To secure GPUs for larger models, ask your customer engineer to get you allowlisted for a Shared Reservation or a Dynamic Workload Scheduler.\n",
+        "\n",
+        "# Import the necessary packages\n",
+        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
+        "\n",
+        "import importlib\n",
+        "import os\n",
+        "import uuid\n",
+        "from datetime import datetime\n",
+        "from typing import Tuple\n",
+        "\n",
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "common_util = importlib.import_module(\n",
+        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
+        ")\n",
+        "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
+        "# Get the default cloud project id.\n",
+        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
+        "\n",
+        "# Get the default region for launching jobs.\n",
+        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
+        "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
+        "# Cloud Storage bucket for storing the experiment artifacts.\n",
+        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
+        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
+        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"llama3_1\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "\n",
+        "# Gets the default SERVICE_ACCOUNT.\n",
+        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
+        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
+        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
+        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
+        "\n",
+        "\n",
+        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
+        "\n",
+        "! gcloud config set project $PROJECT_ID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "36c21f10355f"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Access Llama 3.1 models\n",
+        "\n",
+        "# @markdown For GPU based finetuning and serving, choose between accessing Llama 3.1 models on [Hugging Face](https://huggingface.co/)\n",
+        "# @markdown or Vertex AI as described below.\n",
+        "\n",
+        "# @markdown If you already obtained access to Llama 3.1 models on [Hugging Face](https://huggingface.co/), you can load models from there.\n",
+        "# @markdown Alternatively, you can also load the original Llama 3.1 models for finetuning and serving from Vertex AI after accepting the agreement.\n",
+        "\n",
+        "# @markdown **Only select and fill one of the following sections.**\n",
+        "# fmt: off\n",
+        "LOAD_MODEL_FROM = \"Hugging Face\"  # @param [\"Hugging Face\", \"Google Cloud\"] {isTemplate:true}\n",
+        "# fmt: on\n",
+        "\n",
+        "# @markdown ---\n",
+        "\n",
+        "# @markdown ### Access Llama 3.1 models on Hugging Face for GPU based finetuning and serving\n",
+        "# @markdown You must provide a Hugging Face User Access Token (read) to access the Llama 3.1 models. You can follow the [Hugging Face documentation](https://huggingface.co/docs/hub/en/security-tokens) to create a **read** access token and put it in the `HF_TOKEN` field below.\n",
+        "\n",
+        "HF_TOKEN = \"\"  # @param {type:\"string\", isTemplate:true}\n",
+        "if LOAD_MODEL_FROM == \"Hugging Face\":\n",
+        "    assert (\n",
+        "        HF_TOKEN\n",
+        "    ), \"Provide a read HF_TOKEN to load models from Hugging Face, or select a different model source.\"\n",
+        "\n",
+        "# @markdown *--- Or ---*\n",
+        "# @markdown ### Access Llama 3.1 models on Vertex AI for GPU based serving\n",
+        "# @markdown The original models from Meta are converted into the Hugging Face format for serving in Vertex AI.\n",
+        "# @markdown Accept the model agreement to access the models:\n",
+        "# @markdown 1. Open the [Llama 3.1 model card](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3_1) from [Vertex AI Model Garden](https://cloud.google.com/model-garden).\n",
+        "# @markdown 2. Review and accept the agreement in the pop-up window on the model card page. If you have previously accepted the model agreement, there will not be a pop-up window on the model card page and this step is not needed.\n",
+        "# @markdown 3. After accepting the agreement of Llama 3.1, a `gs://` URI containing Llama 3.1 pretrained and finetuned models will be shared.\n",
+        "# @markdown 4. Paste the URI in the `VERTEX_AI_MODEL_GARDEN_LLAMA3_1` field below.\n",
+        "\n",
+        "VERTEX_AI_MODEL_GARDEN_LLAMA3_1 = \"\"  # @param {type:\"string\", isTemplate:true}\n",
+        "MODEL_BUCKET = VERTEX_AI_MODEL_GARDEN_LLAMA3_1\n",
+        "\n",
+        "# @markdown ---\n",
+        "\n",
+        "\n",
+        "# The pre-built serving docker image.\n",
+        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240721_0916_RC00\"\n",
+        "\n",
+        "\n",
+        "def deploy_model_vllm(\n",
+        "    model_name: str,\n",
+        "    model_id: str,\n",
+        "    service_account: str,\n",
+        "    machine_type: str = \"g2-standard-8\",\n",
+        "    accelerator_type: str = \"NVIDIA_L4\",\n",
+        "    accelerator_count: int = 1,\n",
+        "    gpu_memory_utilization: float = 0.9,\n",
+        "    max_model_len: int = 8192,\n",
+        "    max_loras: int = 1,\n",
+        "    max_cpu_loras: int = 16,\n",
+        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
+        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
+        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "\n",
+        "    vllm_args = [\n",
+        "        \"python\",\n",
+        "        \"-m\",\n",
+        "        \"vllm.entrypoints.api_server\",\n",
+        "        \"--host=0.0.0.0\",\n",
+        "        \"--port=7080\",\n",
+        "        f\"--model={model_id}\",\n",
+        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
+        "        \"--swap-space=16\",\n",
+        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
+        "        f\"--max-model-len={max_model_len}\",\n",
+        "        f\"--max-loras={max_loras}\",\n",
+        "        f\"--max-cpu-loras={max_cpu_loras}\",\n",
+        "        \"--disable-log-stats\",\n",
+        "    ]\n",
+        "\n",
+        "    env_vars = {\n",
+        "        \"MODEL_ID\": base_model_id,\n",
+        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
+        "    }\n",
+        "\n",
+        "    try:\n",
+        "        if HF_TOKEN:\n",
+        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "    except:\n",
+        "        pass\n",
+        "\n",
+        "    model = aiplatform.Model.upload(\n",
+        "        display_name=model_name,\n",
+        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
+        "        serving_container_args=vllm_args,\n",
+        "        serving_container_ports=[7080],\n",
+        "        serving_container_predict_route=\"/generate\",\n",
+        "        serving_container_health_route=\"/ping\",\n",
+        "        serving_container_environment_variables=env_vars,\n",
+        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
+        "        serving_container_deployment_timeout=7200,\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
+        "    )\n",
+        "    model.deploy(\n",
+        "        endpoint=endpoint,\n",
+        "        machine_type=machine_type,\n",
+        "        accelerator_type=accelerator_type,\n",
+        "        accelerator_count=accelerator_count,\n",
+        "        deploy_request_timeout=1800,\n",
+        "        service_account=service_account,\n",
+        "    )\n",
+        "    print(\"endpoint_name:\", endpoint.name)\n",
+        "\n",
+        "    return model, endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cb56d402e84a"
+      },
+      "source": [
+        "## Finetune with HuggingFace PEFT and deploy with vLLM on GPUs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "KwAW99YZHTdy"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Set dataset\n",
+        "\n",
+        "# @markdown Use the Vertex AI SDK to create and run the custom training jobs.\n",
+        "\n",
+        "# @markdown This notebook uses [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset as an example.\n",
+        "# @markdown You can set `dataset_name` to any existing [Hugging Face dataset](https://huggingface.co/datasets) name, and set `instruct_column_in_dataset` to the name of the dataset column containing training data. The [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) has only one column `text`, and therefore we set `instruct_column_in_dataset` to `text` in this notebook.\n",
+        "\n",
+        "# @markdown ### (Optional) Prepare a custom JSONL dataset for finetuning\n",
+        "\n",
+        "# @markdown You can prepare a JSONL file where each line is a valid JSON string as your custom training dataset. For example, here is one line from the [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset:\n",
+        "# @markdown ```\n",
+        "# @markdown {\"text\": \"### Human: Hola### Assistant: \\u00a1Hola! \\u00bfEn qu\\u00e9 puedo ayudarte hoy?\"}\n",
+        "# @markdown ```\n",
+        "\n",
+        "# @markdown The JSON object has a key `text`, which should match `instruct_column_in_dataset`; The value should be one training data point, i.e. a string. After you prepared your JSONL file, you can either upload it to [Hugging Face datasets](https://huggingface.co/datasets) or [Google Cloud Storage](https://cloud.google.com/storage).\n",
+        "\n",
+        "# @markdown - To upload a JSONL dataset to [Hugging Face datasets](https://huggingface.co/datasets), follow the instructions on [Uploading Datasets](https://huggingface.co/docs/hub/en/datasets-adding). Then, set `dataset_name` to the name of your newly created dataset on Hugging Face.\n",
+        "\n",
+        "# @markdown - To upload a JSONL dataset to [Google Cloud Storage](https://cloud.google.com/storage), follow the instructions on [Upload objects from a filesystem](https://cloud.google.com/storage/docs/uploading-objects). Then, set `dataset_name` to the `gs://` URI to your JSONL file. For example: `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl`.\n",
+        "\n",
+        "# @markdown Optionally update the `instruct_column_in_dataset` field below if your JSON objects use a key other than the default `text`.\n",
+        "\n",
+        "# @markdown ### (Optional) Format your data with custom JSON template\n",
+        "\n",
+        "# @markdown Sometimes, your dataset might have multiple text columns and you want to construct the training data with a template. You can prepare a JSON template in the following format:\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown {\n",
+        "# @markdown   \"description\": \"Template used by Llama 3.1, accepting text-bison format.\",\n",
+        "# @markdown   \"source\": \"https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-text-models-supervised#dataset-format\",\n",
+        "# @markdown   \"prompt_input\": \"<|start_header_id|>user<|end_header_id|>\\n\\n{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n{output_text}<|eot_id|>\",\n",
+        "# @markdown   \"instruction_separator\": \"<|start_header_id|>user<|end_header_id|>\\n\\n\",\n",
+        "# @markdown   \"response_separator\": \"<|start_header_id|>assistant<|end_header_id|>\\n\\n\"\n",
+        "# @markdown }\n",
+        "# @markdown ```\n",
+        "\n",
+        "# @markdown As an example, the template above can be used to format the following training data (this line comes from `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl`):\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown {\"input_text\":\"TRANSCRIPT: \\nREASON FOR EVALUATION:,\\n\\n LABEL:\",\"output_text\":\"Chiropractic\"}\n",
+        "# @markdown ```\n",
+        "\n",
+        "# @markdown This example template simply concatenates `input_text` with `output_text` with some special tokens in between.\n",
+        "# @markdown\n",
+        "# @markdown To try such custom dataset, you can make the following changes:\n",
+        "# @markdown 1. Set `template` to `llama3-text-bison`\n",
+        "# @markdown 1. Set `train_dataset_name` to `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl`\n",
+        "# @markdown 1. Set `train_split_name` to `train`\n",
+        "# @markdown 1. Set `eval_dataset_name` to `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_eval_sample.jsonl`\n",
+        "# @markdown 1. Set `eval_split_name` to `train` (**NOT** `test`)\n",
+        "# @markdown 1. Set `instruct_column_in_dataset` as `input_text`.\n",
+        "\n",
+        "# Template name or gs:// URI to a custom template.\n",
+        "template = \"openassistant-guanaco\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Hugging Face dataset name or gs:// URI to a custom JSONL dataset.\n",
+        "train_dataset_name = \"timdettmers/openassistant-guanaco\"  # @param {type:\"string\"}\n",
+        "train_split_name = \"train\"  # @param {type:\"string\"}\n",
+        "eval_dataset_name = \"timdettmers/openassistant-guanaco\"  # @param {type:\"string\"}\n",
+        "eval_split_name = \"test\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Name of the dataset column containing training text input.\n",
+        "instruct_column_in_dataset = \"text\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "ivVGS9dHXPOz"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Finetune\n",
+        "# @markdown Use the Vertex AI SDK to create and run the custom training jobs.\n",
+        "\n",
+        "# @markdown **Note**:\n",
+        "# @markdown 1. We recommend setting `finetuning_precision_mode` to `4bit` because it enables using fewer hardware resources for finetuning.\n",
+        "# @markdown 1. We recommend using NVIDIA_L4 for 8B models and NVIDIA_A100_80GB for 70B models.\n",
+        "# @markdown 1. If `max_steps>0`, it will precedence over `epochs`. One can set a small `max_steps` value to quickly check the pipeline.\n",
+        "# @markdown 1. With the default setting, training takes between 1.5 ~ 2 hours.\n",
+        "\n",
+        "TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20240724_0936_RC00\"\n",
+        "\n",
+        "\n",
+        "# The Llama 3.1 base model.\n",
+        "MODEL_ID = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"  # @param [\"meta-llama/Meta-Llama-3.1-8B\", \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"meta-llama/Meta-Llama-3.1-70B\", \"meta-llama/Meta-Llama-3.1-70B-Instruct\"] {isTemplate:true}\n",
+        "if LOAD_MODEL_FROM == \"Google Cloud\":\n",
+        "    base_model_id = os.path.join(MODEL_BUCKET, MODEL_ID.split(\"/\")[-1])\n",
+        "else:\n",
+        "    base_model_id = MODEL_ID\n",
+        "\n",
+        "# The accelerator to use.\n",
+        "accelerator_type = \"NVIDIA_L4\"  # @param [\"NVIDIA_L4\", \"NVIDIA_A100_80GB\"]\n",
+        "\n",
+        "# Batch size for finetuning.\n",
+        "per_device_train_batch_size = 1  # @param{type:\"integer\"}\n",
+        "gradient_accumulation_steps = 8  # @param{type:\"integer\"}\n",
+        "# Maximum sequence length.\n",
+        "max_seq_length = 4096  # @param{type:\"integer\"}\n",
+        "# Setting a positive `max_steps` here will override `num_epochs`\n",
+        "max_steps = -1  # @param{type:\"integer\"}\n",
+        "num_epochs = 1.0  # @param{type:\"number\"}\n",
+        "# Precision mode for finetuning.\n",
+        "finetuning_precision_mode = \"4bit\"  # @param [\"4bit\", \"8bit\", \"float16\"]\n",
+        "# Learning rate.\n",
+        "learning_rate = 5e-5  # @param{type:\"number\"}\n",
+        "lr_scheduler_type = \"cosine\"  # @param{type:\"string\"}\n",
+        "# LoRA parameters.\n",
+        "lora_rank = 16  # @param{type:\"integer\"}\n",
+        "lora_alpha = 32  # @param{type:\"integer\"}\n",
+        "lora_dropout = 0.05  # @param{type:\"number\"}\n",
+        "enable_gradient_checkpointing = True\n",
+        "attn_implementation = \"flash_attention_2\"\n",
+        "optimizer = \"paged_adamw_32bit\"\n",
+        "warmup_ratio = \"0.01\"\n",
+        "report_to = \"tensorboard\"\n",
+        "save_steps = 10\n",
+        "logging_steps = save_steps\n",
+        "\n",
+        "# Worker pool spec.\n",
+        "machine_type = None\n",
+        "if \"8b\" in MODEL_ID.lower():\n",
+        "    if accelerator_type == \"NVIDIA_L4\":\n",
+        "        accelerator_count = 4\n",
+        "        machine_type = \"g2-standard-48\"\n",
+        "    else:\n",
+        "        raise ValueError(\n",
+        "            f\"Recommended machine settings not found for: {accelerator_type}. To use another accelerator, edit this code block to pass in an appropriate `machine_type`, `accelerator_type`, and `accelerator_count` to the deploy_model_vllm function by clicking `Show Code` and then modifying the code.\"\n",
+        "        )\n",
+        "elif \"70b\" in MODEL_ID.lower():\n",
+        "    if accelerator_type == \"NVIDIA_A100_80GB\":\n",
+        "        accelerator_count = 4\n",
+        "        machine_type = \"a2-ultragpu-4g\"\n",
+        "    else:\n",
+        "        raise ValueError(\n",
+        "            f\"Recommended machine settings not found for: {accelerator_type}. To use another accelerator, edit this code block to pass in an appropriate `machine_type`, `accelerator_type`, and `accelerator_count` to the deploy_model_vllm function by clicking `Show Code` and then modifying the code.\"\n",
+        "        )\n",
+        "else:\n",
+        "    raise ValueError(f\"Unsupported model ID or GCS path: {MODEL_ID}.\")\n",
+        "\n",
+        "replica_count = 1\n",
+        "\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=True,\n",
+        ")\n",
+        "\n",
+        "job_name = common_util.get_job_name_with_datetime(\"llama3_1-lora-train\").replace(\n",
+        "    \"_\", \"-\"\n",
+        ")\n",
+        "\n",
+        "base_output_dir = os.path.join(STAGING_BUCKET, job_name)\n",
+        "# Create a GCS folder to store the LORA adapter.\n",
+        "lora_output_dir = os.path.join(base_output_dir, \"adapter\")\n",
+        "# Create a GCS folder to store the merged model with the base model and the\n",
+        "# finetuned LORA adapter.\n",
+        "merged_model_output_dir = os.path.join(base_output_dir, \"merged-model\")\n",
+        "\n",
+        "eval_args = [\n",
+        "    f\"--eval_dataset_path={eval_dataset_name}\",\n",
+        "    f\"--eval_column={instruct_column_in_dataset}\",\n",
+        "    f\"--eval_template={template}\",\n",
+        "    f\"--eval_split={eval_split_name}\",\n",
+        "    f\"--eval_steps={save_steps}\",\n",
+        "    \"--eval_tasks=builtin_eval\",\n",
+        "    \"--eval_metric_name=loss\",\n",
+        "]\n",
+        "\n",
+        "train_job_args = [\n",
+        "    \"--config_file=vertex_vision_model_garden_peft/deepspeed_zero2_4gpu.yaml\",\n",
+        "    \"--task=instruct-lora\",\n",
+        "    \"--completion_only=True\",\n",
+        "    f\"--pretrained_model_id={base_model_id}\",\n",
+        "    f\"--dataset_name={train_dataset_name}\",\n",
+        "    f\"--train_split_name={train_split_name}\",\n",
+        "    f\"--instruct_column_in_dataset={instruct_column_in_dataset}\",\n",
+        "    f\"--output_dir={lora_output_dir}\",\n",
+        "    f\"--merge_base_and_lora_output_dir={merged_model_output_dir}\",\n",
+        "    f\"--per_device_train_batch_size={per_device_train_batch_size}\",\n",
+        "    f\"--gradient_accumulation_steps={gradient_accumulation_steps}\",\n",
+        "    f\"--lora_rank={lora_rank}\",\n",
+        "    f\"--lora_alpha={lora_alpha}\",\n",
+        "    f\"--lora_dropout={lora_dropout}\",\n",
+        "    f\"--max_steps={max_steps}\",\n",
+        "    f\"--max_seq_length={max_seq_length}\",\n",
+        "    f\"--learning_rate={learning_rate}\",\n",
+        "    f\"--lr_scheduler_type={lr_scheduler_type}\",\n",
+        "    f\"--precision_mode={finetuning_precision_mode}\",\n",
+        "    f\"--enable_gradient_checkpointing={enable_gradient_checkpointing}\",\n",
+        "    f\"--num_epochs={num_epochs}\",\n",
+        "    f\"--attn_implementation={attn_implementation}\",\n",
+        "    f\"--optimizer={optimizer}\",\n",
+        "    f\"--warmup_ratio={warmup_ratio}\",\n",
+        "    f\"--report_to={report_to}\",\n",
+        "    f\"--logging_output_dir={base_output_dir}\",\n",
+        "    f\"--save_steps={save_steps}\",\n",
+        "    f\"--logging_steps={logging_steps}\",\n",
+        "    f\"--template={template}\",\n",
+        "    f\"--huggingface_access_token={HF_TOKEN}\",\n",
+        "] + eval_args\n",
+        "\n",
+        "# Create TensorBoard\n",
+        "tensorboard = aiplatform.Tensorboard.create(job_name)\n",
+        "exp = aiplatform.TensorboardExperiment.create(\n",
+        "    tensorboard_experiment_id=job_name, tensorboard_name=tensorboard.name\n",
+        ")\n",
+        "\n",
+        "# Pass training arguments and launch job.\n",
+        "train_job = aiplatform.CustomContainerTrainingJob(\n",
+        "    display_name=job_name,\n",
+        "    container_uri=TRAIN_DOCKER_URI,\n",
+        ")\n",
+        "\n",
+        "train_job.run(\n",
+        "    args=train_job_args,\n",
+        "    environment_variables={\"WANDB_DISABLED\": True},\n",
+        "    replica_count=replica_count,\n",
+        "    machine_type=machine_type,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    boot_disk_size_gb=500,\n",
+        "    service_account=SERVICE_ACCOUNT,\n",
+        "    tensorboard=tensorboard.resource_name,\n",
+        "    base_output_dir=base_output_dir,\n",
+        ")\n",
+        "\n",
+        "print(\"LoRA adapter was saved in: \", lora_output_dir)\n",
+        "print(\"Trained and merged models were saved in: \", merged_model_output_dir)\n",
+        "\n",
+        "# @markdown Click \"Show Code\" to see more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "qmHW6m8xG_4U"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Deploy\n",
+        "# @markdown This section uploads the model to Model Registry and deploys it on the Endpoint. It takes 15 minutes to 1 hour to finish.\n",
+        "\n",
+        "print(\"Deploying models in: \", merged_model_output_dir)\n",
+        "\n",
+        "# Find Vertex AI prediction supported accelerators and regions in [here](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute).\n",
+        "if \"8b\" in MODEL_ID.lower():\n",
+        "    machine_type = \"g2-standard-12\"\n",
+        "    accelerator_type = \"NVIDIA_L4\"\n",
+        "    accelerator_count = 1\n",
+        "else:\n",
+        "    machine_type = \"g2-standard-96\"\n",
+        "    accelerator_type = \"NVIDIA_L4\"\n",
+        "    accelerator_count = 8\n",
+        "\n",
+        "common_util.check_quota(\n",
+        "    project_id=PROJECT_ID,\n",
+        "    region=REGION,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    is_for_training=False,\n",
+        ")\n",
+        "\n",
+        "gpu_memory_utilization = 0.85\n",
+        "max_model_len = 8192  # Maximum context length.\n",
+        "\n",
+        "# Ensure max_model_len does not exceed the limit\n",
+        "if max_model_len > 8192:\n",
+        "    raise ValueError(\"max_model_len cannot exceed 8192\")\n",
+        "\n",
+        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
+        "    model_name=common_util.get_job_name_with_datetime(prefix=\"llama3_1-vllm-serve\"),\n",
+        "    model_id=merged_model_output_dir,\n",
+        "    service_account=SERVICE_ACCOUNT,\n",
+        "    machine_type=machine_type,\n",
+        "    accelerator_type=accelerator_type,\n",
+        "    accelerator_count=accelerator_count,\n",
+        "    gpu_memory_utilization=gpu_memory_utilization,\n",
+        "    max_model_len=max_model_len,\n",
+        ")\n",
+        "\n",
+        "# @markdown Click \"Show Code\" to see more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "2UYUNn60G_4U"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Predict\n",
+        "\n",
+        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. Sampling parameters supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/dev/sampling_params.html).\n",
+        "\n",
+        "# @markdown Example:\n",
+        "\n",
+        "# @markdown ```\n",
+        "# @markdown Human: What is a car?\n",
+        "# @markdown Assistant:  A car, or a motor car, is a road-connected human-transportation system used to move people or goods from one place to another. The term also encompasses a wide range of vehicles, including motorboats, trains, and aircrafts. Cars typically have four wheels, a cabin for passengers, and an engine or motor. They have been around since the early 19th century and are now one of the most popular forms of transportation, used for daily commuting, shopping, and other purposes.\n",
+        "# @markdown ```\n",
+        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
+        "\n",
+        "# Loads an existing endpoint instance using the endpoint name:\n",
+        "# - Using `endpoint_name = endpoint.name` allows us to get the\n",
+        "#   endpoint name of the endpoint `endpoint` created in the cell\n",
+        "#   above.\n",
+        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
+        "#   an existing endpoint with the ID 1234567890123456789.\n",
+        "# You may uncomment the code below to load an existing endpoint.\n",
+        "\n",
+        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
+        "# aip_endpoint_name = (\n",
+        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
+        "# )\n",
+        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "\n",
+        "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
+        "max_tokens = 50  # @param {type:\"integer\"}\n",
+        "temperature = 1.0  # @param {type:\"number\"}\n",
+        "top_p = 1.0  # @param {type:\"number\"}\n",
+        "top_k = 1  # @param {type:\"integer\"}\n",
+        "raw_response = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "# Overrides parameters for inferences.\n",
+        "# If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`,\n",
+        "# you can reduce the maximum number of output tokens, such as set max_tokens as 20.\n",
+        "instances = [\n",
+        "    {\n",
+        "        \"prompt\": prompt,\n",
+        "        \"max_tokens\": max_tokens,\n",
+        "        \"temperature\": temperature,\n",
+        "        \"top_p\": top_p,\n",
+        "        \"top_k\": top_k,\n",
+        "        \"raw_response\": raw_response,\n",
+        "    },\n",
+        "]\n",
+        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
+        "\n",
+        "for prediction in response.predictions:\n",
+        "    print(prediction)\n",
+        "\n",
+        "# @markdown Click \"Show Code\" to see more details."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "af21a3cff1e0"
+      },
+      "source": [
+        "## Clean up resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "911406c1561e"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Delete the model and endpoint\n",
+        "\n",
+        "train_job.delete()\n",
+        "\n",
+        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
+        "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
+        "\n",
+        "# Undeploy model and delete endpoint.\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
+        "\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_NAME"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_pytorch_llama3_1_finetuning.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/model_garden_pytorch_llama3_finetuning.ipynb

@@ -116,6 +116,8 @@
         "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
         ")\n",
         "\n",
+        "models, endpoints = {}, {}\n",
+        "\n",
         "# Get the default cloud project id.\n",
         "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
         "\n",
@@ -238,15 +240,20 @@
         "    model_name: str,\n",
         "    model_id: str,\n",
         "    service_account: str,\n",
+        "    base_model_id: str = None,\n",
         "    machine_type: str = \"g2-standard-8\",\n",
         "    accelerator_type: str = \"NVIDIA_L4\",\n",
         "    accelerator_count: int = 1,\n",
         "    gpu_memory_utilization: float = 0.9,\n",
         "    max_model_len: int = 4096,\n",
+        "    dtype: str = \"auto\",\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
         "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
         "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
         "\n",
+        "    if not base_model_id:\n",
+        "        base_model_id = model_id\n",
+        "\n",
         "    vllm_args = [\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
@@ -255,10 +262,18 @@
         "        \"--swap-space=16\",\n",
         "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
         "        f\"--max-model-len={max_model_len}\",\n",
+        "        f\"--dtype={dtype}\",\n",
         "        \"--disable-log-stats\",\n",
         "    ]\n",
         "\n",
-        "    env_vars = {\"MODEL_ID\": model_id, \"DEPLOY_SOURCE\": \"notebook\"}\n",
+        "    env_vars = {\n",
+        "        \"MODEL_ID\": base_model_id,\n",
+        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
+        "    }\n",
+        "\n",
+        "    if HF_TOKEN:\n",
+        "        env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
+        "\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
@@ -268,6 +283,8 @@
         "        serving_container_predict_route=\"/generate\",\n",
         "        serving_container_health_route=\"/ping\",\n",
         "        serving_container_environment_variables=env_vars,\n",
+        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
+        "        serving_container_deployment_timeout=7200,\n",
         "    )\n",
         "    print(\n",
         "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
@@ -587,7 +604,7 @@
         "if max_model_len > 8192:\n",
         "    raise ValueError(\"max_model_len cannot exceed 8192\")\n",
         "\n",
-        "model, endpoint = deploy_model_vllm(\n",
+        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
         "    model_name=common_util.get_job_name_with_datetime(prefix=\"llama3-vllm-serve\"),\n",
         "    model_id=merged_model_output_dir,\n",
         "    service_account=SERVICE_ACCOUNT,\n",
@@ -622,6 +639,20 @@
         "# @markdown ```\n",
         "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
         "\n",
+        "# Loads an existing endpoint instance using the endpoint name:\n",
+        "# - Using `endpoint_name = endpoint.name` allows us to get the\n",
+        "#   endpoint name of the endpoint `endpoint` created in the cell\n",
+        "#   above.\n",
+        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
+        "#   an existing endpoint with the ID 1234567890123456789.\n",
+        "# You may uncomment the code below to load an existing endpoint.\n",
+        "\n",
+        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
+        "# aip_endpoint_name = (\n",
+        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
+        "# )\n",
+        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
+        "\n",
         "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
         "max_tokens = 50  # @param {type:\"integer\"}\n",
         "temperature = 1.0  # @param {type:\"number\"}\n",
@@ -629,9 +660,6 @@
         "top_k = 1  # @param {type:\"integer\"}\n",
         "raw_response = False  # @param {type:\"boolean\"}\n",
         "\n",
-        "# Overides parameters for inferences.\n",
-        "# If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`,\n",
-        "# you can reduce the maximum number of output tokens, such as set max_tokens as 20.\n",
         "instances = [\n",
         "    {\n",
         "        \"prompt\": prompt,\n",
@@ -642,7 +670,7 @@
         "        \"raw_response\": raw_response,\n",
         "    },\n",
         "]\n",
-        "response = endpoint.predict(instances=instances)\n",
+        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
         "\n",
         "for prediction in response.predictions:\n",
         "    print(prediction)\n",
@@ -676,10 +704,12 @@
         "# @markdown  and avoid unnecessary continouous charges that may incur.\n",
         "\n",
         "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)\n",
+        "for endpoint in endpoints.values():\n",
+        "    endpoint.delete(force=True)\n",
         "\n",
         "# Delete models.\n",
-        "model.delete()\n",
+        "for model in models.values():\n",
+        "    model.delete()\n",
         "\n",
         "delete_bucket = False  # @param {type:\"boolean\"}\n",
         "if delete_bucket:\n",

notebooks/community/model_garden/model_garden_pytorch_mistral.ipynb

@@ -1,883 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7d9bbf86da5e"
-      },
-      "outputs": [],
-      "source": [
-        "# Copyright 2023 Google LLC\n",
-        "#\n",
-        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "99c1c3fc2ca5"
-      },
-      "source": [
-        "# Vertex AI Model Garden - Mistral and Mixtral 8x7B Models\n",
-        "\n",
-        "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_mistral.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_mistral.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
-        "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_mistral.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "Open in Vertex AI Workbench\n",
-        "    </a> (A Python-3 CPU notebook is recommended)\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3de7470326a2"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "This notebook demonstrates deploying prebuilt [Mistral](https://mistral.ai/) and Mixtral 8x7B models in Vertex AI.\n",
-        "\n",
-        "### Objective\n",
-        "\n",
-        "- Deploy prebuilt [Mistral models](https://huggingface.co/mistralai) with [vLLM](https://github.com/vllm-project/vllm) containers\n",
-        "    - [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1): pretrained generative text model with 7 billion parameters\n",
-        "    - [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1): Instruction fine-tuned version of the Mistral-7B-v0.1 generative text model\n",
-        "    - [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2): Improved instruction fine-tuned version of Mistral-7B-Instruct-v0.1 supporting 32k context length\n",
-        "- Deploy prebuit [Mixtral 8x7B model](https://huggingface.co/mistralai) with [vLLM](https://github.com/vllm-project/vllm) containers\n",
-        "    - [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1): pretrained Mixture of Experts (MoE) model with 8 branches\n",
-        "    - [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1): Instruction fine-tuned version of the Mixture of Experts (MoE) model with 8 branches\n",
-        "\n",
-        "### Costs\n",
-        "\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "* Vertex AI\n",
-        "* Cloud Storage\n",
-        "\n",
-        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), [Cloud NL API pricing](https://cloud.google.com/natural-language/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "264c07757582"
-      },
-      "source": [
-        "## Before you begin"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ioensNKM8ned"
-      },
-      "source": [
-        "### Colab only\n",
-        "Run the following commands for Colab and skip this section if you are using Workbench."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2707b02ef5df"
-      },
-      "outputs": [],
-      "source": [
-        "import sys\n",
-        "\n",
-        "if \"google.colab\" in sys.modules:\n",
-        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
-        "    from google.colab import auth as google_auth\n",
-        "\n",
-        "    google_auth.authenticate_user()\n",
-        "    # Install gdown for downloading example training images.\n",
-        "    ! pip3 install gdown\n",
-        "\n",
-        "    # Restart the notebook kernel after installs.\n",
-        "    import IPython\n",
-        "\n",
-        "    app = IPython.Application.instance()\n",
-        "    app.kernel.do_shutdown(True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "46d25fe73955"
-      },
-      "source": [
-        "### Install dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "c75c2c1fa6e0"
-      },
-      "outputs": [],
-      "source": [
-        "! pip3 install transformers==4.36.0\n",
-        "! pip3 install accelerate==0.23.0"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bb7adab99e41"
-      },
-      "source": [
-        "### Setup Google Cloud project\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "1. [Enable the Vertex AI API, Compute Engine API and Cloud Natural Language API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,language.googleapis.com).\n",
-        "\n",
-        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs.\n",
-        "\n",
-        "1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console) with `Vertex AI User` and `Storage Object Admin` roles for deploying fine tuned model to Vertex AI endpoint."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c460088b873"
-      },
-      "source": [
-        "### Define environment variables\n",
-        "\n",
-        "Set the following variables for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the specified region (`REGION`). Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\")."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "855d6b96f291"
-      },
-      "outputs": [],
-      "source": [
-        "# Cloud project id.\n",
-        "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# The region you want to launch jobs in.\n",
-        "# Select region based on the accelerators and regions supported by Vertex AI Prediction\n",
-        "# https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
-        "REGION = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# The Cloud Storage bucket for storing experiments output.\n",
-        "# Start with gs:// prefix, e.g. gs://foo_bucket.\n",
-        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
-        "\n",
-        "! gcloud config set project $PROJECT_ID\n",
-        "\n",
-        "import os\n",
-        "\n",
-        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
-        "\n",
-        "# The service account looks like:\n",
-        "# '@.iam.gserviceaccount.com'\n",
-        "# Please go to https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console\n",
-        "# and create service account with `Vertex AI User` and `Storage Object Admin` roles.\n",
-        "# The service account for deploying fine tuned model.\n",
-        "SERVICE_ACCOUNT = \"\"  # @param {type:\"string\"}",
-        "# HuggingFace access token.\n",
-        "# Create a token at https://huggingface.co/settings/tokens.\n",
-        "# Accept the model terms of service for the model you wish to use.\n",
-        "HF_TOKEN = \"\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e828eb320337"
-      },
-      "source": [
-        "### Initialize Vertex AI API"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "12cd25839741"
-      },
-      "outputs": [],
-      "source": [
-        "from google.cloud import aiplatform\n",
-        "\n",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2cc825514deb"
-      },
-      "source": [
-        "### Define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b42bd4fa2b2d"
-      },
-      "outputs": [],
-      "source": [
-        "# The pre-built serving docker images with vLLM\n",
-        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240313_0916_RC00\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0c250872074f"
-      },
-      "source": [
-        "### Define common functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "354da31189dc"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from datetime import datetime\n",
-        "from typing import Tuple\n",
-        "\n",
-        "from google.cloud import aiplatform\n",
-        "\n",
-        "\n",
-        "def get_job_name_with_datetime(prefix: str) -> str:\n",
-        "    \"\"\"Gets the job name with date time when triggering training or deployment\n",
-        "    jobs in Vertex AI.\n",
-        "    \"\"\"\n",
-        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
-        "\n",
-        "\n",
-        "def deploy_model_vllm(\n",
-        "    model_name: str,\n",
-        "    model_id: str,\n",
-        "    service_account: str,\n",
-        "    machine_type: str = \"g2-standard-8\",\n",
-        "    accelerator_type: str = \"NVIDIA_L4\",\n",
-        "    accelerator_count: int = 1,\n",
-        "    max_model_len: int = 4096,\n",
-        "    gpu_memory_utilization: float = 0.9,\n",
-        "    use_openai_server: bool = False,\n",
-        "    use_chat_completions_if_openai_server: bool = False,\n",
-        "    huggingface_token: str = \"\",\n",
-        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
-        "    \"\"\"Deploys Mistral models with vLLM on Vertex AI.\n",
-        "\n",
-        "    Args:\n",
-        "        model_name: Display name of the model.\n",
-        "        model_id: Model ID or path to model weights.\n",
-        "        service_account: Service account for model uploading and deployment.\n",
-        "        machine_type: Deployment machine type.\n",
-        "        accelerator_type: Deployment accelerator type.\n",
-        "        accelerator_count: Number of accelerators to use.\n",
-        "        max_model_len: Maximum model length.\n",
-        "        gpu_memory_utilization: Fraction of GPU memory to be used for the model\n",
-        "            executor.\n",
-        "        use_openai_server: Whether to use the OpenAI-format vLLM model server.\n",
-        "        use_chat_completions_if_openai_server: If the OpenAI model server is\n",
-        "            used, whether to use the chat completion API as opposed to the text\n",
-        "            completion API. The vLLM text completion API mimics the OpenAI text\n",
-        "            completion API:\n",
-        "            https://platform.openai.com/docs/api-reference/completions/create.\n",
-        "            It has two required parameters: the model ID to direct requests to\n",
-        "            and the prompt. The response includes a \"choices\" field that\n",
-        "            contains the generated text and a \"usage\" field that contains token\n",
-        "            counts. The vLLM chat completion API mimics the OpenAI chat\n",
-        "            completion API:\n",
-        "            https://platform.openai.com/docs/api-reference/chat/create. It has\n",
-        "            two required parameters: the model ID to direct requests to and\n",
-        "            \"messages\" which is a sequence of system/user/assistant/tool\n",
-        "            messages that can represent a multi-turn chat conversation. The\n",
-        "            response includes a \"choices\" field that contains the generated\n",
-        "            message from a role and a \"usage\" field that contains token counts.\n",
-        "        huggingface_token: Huggingface token for accessing the model.\n",
-        "\n",
-        "    Returns:\n",
-        "        Model instance and endpoint instance.\n",
-        "    \"\"\"\n",
-        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
-        "\n",
-        "    dtype = \"bfloat16\"\n",
-        "    if accelerator_type in [\"NVIDIA_TESLA_T4\", \"NVIDIA_TESLA_V100\"]:\n",
-        "        dtype = \"float16\"\n",
-        "\n",
-        "    vllm_args = [\n",
-        "        \"--host=0.0.0.0\",\n",
-        "        \"--port=7080\",\n",
-        "        f\"--model={model_id}\",\n",
-        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
-        "        \"--swap-space=16\",\n",
-        "        f\"--dtype={dtype}\",\n",
-        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
-        "        f\"--max-model-len={max_model_len}\",\n",
-        "        \"--disable-log-stats\",\n",
-        "    ]\n",
-        "    serving_env = {\n",
-        "        \"MODEL_ID\": model_id,\n",
-        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
-        "        \"HF_TOKEN\": huggingface_token,\n",
-        "    }\n",
-        "    if use_openai_server:\n",
-        "        if use_chat_completions_if_openai_server:\n",
-        "            serving_container_predict_route = \"/v1/chat/completions\"\n",
-        "        else:\n",
-        "            serving_container_predict_route = \"/v1/completions\"\n",
-        "    else:\n",
-        "        serving_container_predict_route = \"/generate\"\n",
-        "    model = aiplatform.Model.upload(\n",
-        "        display_name=model_name,\n",
-        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
-        "        serving_container_command=[\n",
-        "            \"python\",\n",
-        "            \"-m\",\n",
-        "            (\n",
-        "                \"vllm.entrypoints.api_server\"\n",
-        "                if not use_openai_server\n",
-        "                else \"vllm.entrypoints.openai.api_server\"\n",
-        "            ),\n",
-        "        ],\n",
-        "        serving_container_args=vllm_args,\n",
-        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=serving_container_predict_route,\n",
-        "        serving_container_health_route=\"/health\",\n",
-        "        serving_container_environment_variables=serving_env,\n",
-        "    )\n",
-        "\n",
-        "    model.deploy(\n",
-        "        endpoint=endpoint,\n",
-        "        machine_type=machine_type,\n",
-        "        accelerator_type=accelerator_type,\n",
-        "        accelerator_count=accelerator_count,\n",
-        "        deploy_request_timeout=1800,\n",
-        "        service_account=service_account,\n",
-        "    )\n",
-        "    return model, endpoint"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e057b5edcf81"
-      },
-      "source": [
-        "## Run inferences locally with prebuilt Mistral and Mixtral models\n",
-        "\n",
-        "You will need at least 24GB of memory to run inference with Mistral-7B. You can run locally or on Vertex AI Prediction endpoints with any of the following specs:\n",
-        "- g2-standard-8 with 1 L4 GPU\n",
-        "- n1-standard-16 with 2 V100 GPUs\n",
-        "- n1-standard-16 with 2 T4 GPUs\n",
-        "- a2-highgpu-1g with 1 A100 GPU\n",
-        "\n",
-        "You will need at least 96GB of memory to run inference with Mixtral 8x7B. You can run locally or on Vertex AI Prediction endpoints with any of the following specs:\n",
-        "- g2-standard-96 with 8 L4 GPUs\n",
-        "- n1-standard-32 with 8 V100 GPUs\n",
-        "- n1-standard-32 with 8 T4 GPUs\n",
-        "- a2-highgpu-4g with 4 A100 GPUs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "31f6cc84efdd"
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "import torch\n",
-        "import transformers\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "device = \"cuda\"  # the device to load the model onto\n",
-        "model_name = \"mistralai/Mistral-7B-v0.1\"  # @param [\"mistralai/Mistral-7B-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.2\", \"mistralai/Mixtral-8x7B-v0.1\", \"mistralai/Mixtral-8x7B-Instruct-v0.1\"]\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    model_name, device_map=\"auto\", return_dict=True, torch_dtype=torch.float16\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-        "\n",
-        "pipeline = transformers.pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
-        "\n",
-        "prompt = \"My favourite condiment is\"\n",
-        "\n",
-        "sequences = pipeline(\n",
-        "    prompt,\n",
-        "    max_length=200,\n",
-        "    do_sample=True,\n",
-        "    top_k=10,\n",
-        "    num_return_sequences=1,\n",
-        "    eos_token_id=tokenizer.eos_token_id,\n",
-        ")\n",
-        "\n",
-        "for seq in sequences:\n",
-        "    print(f\"Result: {seq['generated_text']}\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YKZ4CBJ2kYaW"
-      },
-      "source": [
-        "## Deploy Prebuilt Mistral model with vLLM\n",
-        "\n",
-        "This section deploys the prebuilt Mistral model with [vLLM](https://github.com/vllm-project/vllm) on a Vertex endpoint. The model deployment step will take ~15 minutes to complete.\n",
-        "\n",
-        "vLLM is a highly optimized LLM serving framework which can significantly increase serving throughput. The higher QPS you have, the more benefits you get using vLLM."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "25b5b3a44cf8"
-      },
-      "source": [
-        "Set the prebuilt model id."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "10547af949fc"
-      },
-      "outputs": [],
-      "source": [
-        "prebuilt_model_id = \"mistralai/Mistral-7B-v0.1\"  # @param [\"mistralai/Mistral-7B-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.2\"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "03d504bcd60b"
-      },
-      "outputs": [],
-      "source": [
-        "# Find Vertex AI prediction supported accelerators and regions in\n",
-        "# https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
-        "\n",
-        "# Sets 1 L4 to deploy Mistral 7B.\n",
-        "machine_type = \"g2-standard-8\"\n",
-        "accelerator_type = \"NVIDIA_L4\"\n",
-        "accelerator_count = 1\n",
-        "\n",
-        "# Sets 2 V100s to deploy Mistral 7B.\n",
-        "# machine_type = \"n1-standard-16\"\n",
-        "# accelerator_type = \"NVIDIA_TESLA_V100\"\n",
-        "# accelerator_count = 2\n",
-        "\n",
-        "# Sets 2 T4s to deploy Mistral 7B.\n",
-        "# machine_type = \"n1-standard-16\"\n",
-        "# accelerator_type = \"NVIDIA_TESLA_T4\"\n",
-        "# accelerator_count = 2\n",
-        "\n",
-        "# Sets 1 A100 (40G) to deploy Mistral 7B.\n",
-        "# machine_type = \"a2-highgpu-1g\"\n",
-        "# accelerator_type = \"NVIDIA_TESLA_A100\"\n",
-        "# accelerator_count = 1\n",
-        "\n",
-        "# Larger setting of `max-model-len` can lead to higher requirements on\n",
-        "# `gpu-memory-utilization` and GPU configuration. Larger setting of\n",
-        "# `gpu-memory-utilization` increases the risk of running out of GPU memory with\n",
-        "# long prompts.\n",
-        "max_model_len = 4096\n",
-        "gpu_memory_utilization = 0.9\n",
-        "\n",
-        "model, endpoint = deploy_model_vllm(\n",
-        "    model_name=get_job_name_with_datetime(prefix=\"mistral-serve-vllm\"),\n",
-        "    model_id=prebuilt_model_id,\n",
-        "    service_account=SERVICE_ACCOUNT,\n",
-        "    machine_type=machine_type,\n",
-        "    accelerator_type=accelerator_type,\n",
-        "    accelerator_count=accelerator_count,\n",
-        "    max_model_len=max_model_len,\n",
-        "    gpu_memory_utilization=gpu_memory_utilization,\n",
-        "    use_openai_server=False,\n",
-        "    use_chat_completions_if_openai_server=False,\n",
-        "    huggingface_token=HF_TOKEN,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RRR11SWykYaX"
-      },
-      "source": [
-        "NOTE: If you see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint, the model server is likely still initializing. Please retry later.\n",
-        "\n",
-        "NOTE: If you receive `InternalServerError: 500 System error` during the deployment, most likely the operation failed due to unavailability of resources. Either retry or use a different accelerator type.\n",
-        "\n",
-        "Once deployment succeeds, you can send requests to the endpoint with text prompts."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3a7948c56e3d"
-      },
-      "source": [
-        "### Run sample prompt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3f5a1e1de60d"
-      },
-      "outputs": [],
-      "source": [
-        "# Loads an existing endpoint instance using the endpoint name:\n",
-        "# - Using `endpoint_name = endpoint.name` allows us to get the endpoint name of\n",
-        "#   the endpoint `endpoint` created in the cell above.\n",
-        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
-        "#   an existing endpoint with the ID 1234567890123456789.\n",
-        "# You may uncomment the code below to load an existing endpoint.\n",
-        "\n",
-        "# endpoint_name = endpoint_without_peft.name\n",
-        "# # endpoint_name = \"\"  # @param {type:\"string\"}\n",
-        "# aip_endpoint_name = (\n",
-        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
-        "# )\n",
-        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
-        "\n",
-        "instances = [\n",
-        "    {\n",
-        "        \"prompt\": \"My favourite condiment is\",\n",
-        "        \"n\": 1,\n",
-        "        \"max_tokens\": 200,\n",
-        "        \"temperature\": 1.0,\n",
-        "        \"top_p\": 1.0,\n",
-        "        \"top_k\": 10,\n",
-        "    },\n",
-        "]\n",
-        "response = endpoint.predict(instances=instances)\n",
-        "\n",
-        "for prediction in response.predictions:\n",
-        "    print(prediction)\n",
-        "\n",
-        "# Reference the following code for using the OpenAI vLLM server.\n",
-        "# import json\n",
-        "# response = endpoint.raw_predict(\n",
-        "#     body=json.dumps({\n",
-        "#         \"model\": prebuilt_model_id,\n",
-        "#         \"prompt\": \"My favourite condiment is\",\n",
-        "#         \"n\": 1,\n",
-        "#         \"max_tokens\": 200,\n",
-        "#         \"temperature\": 1.0,\n",
-        "#         \"top_p\": 1.0,\n",
-        "#         \"top_k\": 10,\n",
-        "#     }),\n",
-        "#     headers={\"Content-Type\": \"application/json\"},\n",
-        "# )\n",
-        "# print(response.json())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wOh9irbqJ-MM"
-      },
-      "source": [
-        "## Deploy Prebuilt Mixtral 8x7B model with vLLM\n",
-        "\n",
-        "This section deploys the prebuilt Mixtral 8x7B model with [vLLM](https://github.com/vllm-project/vllm) on a Vertex endpoint. The model deployment step will take ~40 minutes to complete.\n",
-        "\n",
-        "vLLM is a highly optimized LLM serving framework which can significantly increase serving throughput. The higher QPS you have, the more benefits you get using vLLM."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h2uCSnoaJ-MM"
-      },
-      "source": [
-        "Set the prebuilt model id."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-X42gkGYJ-MM"
-      },
-      "outputs": [],
-      "source": [
-        "prebuilt_model_id = \"mistralai/Mixtral-8x7B-v0.1\"  # @param [\"mistralai/Mixtral-8x7B-v0.1\", \"mistralai/Mixtral-8x7B-Instruct-v0.1\"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "M-YiJXT3J-MM"
-      },
-      "outputs": [],
-      "source": [
-        "# Find Vertex AI prediction supported accelerators and regions in\n",
-        "# https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
-        "\n",
-        "# Sets 8 L4s to deploy Mixtral 8x7B.\n",
-        "machine_type = \"g2-standard-96\"\n",
-        "accelerator_type = \"NVIDIA_L4\"\n",
-        "accelerator_count = 8\n",
-        "\n",
-        "# Sets 4 A100s (40G) to deploy Mixtral 8x7B.\n",
-        "# machine_type = \"a2-highgpu-4g\"\n",
-        "# accelerator_type = \"NVIDIA_TESLA_A100\"\n",
-        "# accelerator_count = 4\n",
-        "\n",
-        "# Larger setting of `max-model-len` can lead to higher requirements on\n",
-        "# `gpu-memory-utilization` and GPU configuration. Larger setting of\n",
-        "# `gpu-memory-utilization` increases the risk of running out of GPU memory with\n",
-        "# long prompts.\n",
-        "max_model_len = 4096\n",
-        "gpu_memory_utilization = 0.85\n",
-        "\n",
-        "model, endpoint = deploy_model_vllm(\n",
-        "    model_name=get_job_name_with_datetime(prefix=\"mixtral-serve-vllm\"),\n",
-        "    model_id=prebuilt_model_id,\n",
-        "    service_account=SERVICE_ACCOUNT,\n",
-        "    machine_type=machine_type,\n",
-        "    accelerator_type=accelerator_type,\n",
-        "    accelerator_count=accelerator_count,\n",
-        "    max_model_len=max_model_len,\n",
-        "    gpu_memory_utilization=gpu_memory_utilization,\n",
-        "    use_openai_server=False,\n",
-        "    use_chat_completions_if_openai_server=False,\n",
-        "    huggingface_token=HF_TOKEN,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "agDw0_7JJ-MM"
-      },
-      "source": [
-        "NOTE: If you see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint, the model server is likely still initializing. Please retry later.\n",
-        "\n",
-        "NOTE: If you receive `InternalServerError: 500 System error` during the deployment, most likely the operation failed due to unavailability of resources. Either retry or use a different accelerator type.\n",
-        "\n",
-        "Once deployment succeeds, you can send requests to the endpoint with text prompts."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zLyWJK5aJ-MM"
-      },
-      "source": [
-        "### Run sample prompt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NYN1Z49SJ-MM"
-      },
-      "outputs": [],
-      "source": [
-        "# Loads an existing endpoint instance using the endpoint name:\n",
-        "# - Using `endpoint_name = endpoint.name` allows us to get the endpoint name of\n",
-        "#   the endpoint `endpoint` created in the cell above.\n",
-        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
-        "#   an existing endpoint with the ID 1234567890123456789.\n",
-        "# You may uncomment the code below to load an existing endpoint.\n",
-        "\n",
-        "# endpoint_name = endpoint_without_peft.name\n",
-        "# # endpoint_name = \"\"  # @param {type:\"string\"}\n",
-        "# aip_endpoint_name = (\n",
-        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
-        "# )\n",
-        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
-        "\n",
-        "instances = [\n",
-        "    {\n",
-        "        \"prompt\": \"What is a car?\",\n",
-        "        \"max_tokens\": 50,\n",
-        "        \"temperature\": 1.0,\n",
-        "        \"top_p\": 1.0,\n",
-        "        \"top_k\": 10,\n",
-        "    },\n",
-        "]\n",
-        "response = endpoint.predict(instances=instances)\n",
-        "\n",
-        "for prediction in response.predictions:\n",
-        "    print(prediction)\n",
-        "\n",
-        "# Reference the following code for using the OpenAI vLLM server.\n",
-        "# import json\n",
-        "# response = endpoint.raw_predict(\n",
-        "#     body=json.dumps({\n",
-        "#         \"model\": prebuilt_model_id,\n",
-        "#         \"prompt\": \"My favourite condiment is\",\n",
-        "#         \"n\": 1,\n",
-        "#         \"max_tokens\": 200,\n",
-        "#         \"temperature\": 1.0,\n",
-        "#         \"top_p\": 1.0,\n",
-        "#         \"top_k\": 10,\n",
-        "#     }),\n",
-        "#     headers={\"Content-Type\": \"application/json\"},\n",
-        "# )\n",
-        "# print(response.json())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "NRaBADRM4JEn"
-      },
-      "source": [
-        "## Cleaning up\n",
-        "\n",
-        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
-        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
-        "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0a9da70d4abc"
-      },
-      "source": [
-        "### Undeploy models and Delete endpoints"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "e53749ae6b2c"
-      },
-      "outputs": [],
-      "source": [
-        "# Set this flag to delete endpoint including undeploying models\n",
-        "delete_endpoint = False"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "65078f3ec44e"
-      },
-      "outputs": [],
-      "source": [
-        "def list_endpoints():\n",
-        "    return [\n",
-        "        (r.name, r.display_name)\n",
-        "        for r in aiplatform.Endpoint.list()\n",
-        "        if r.display_name.startswith(\"mistral-serve-vllm\")\n",
-        "        or r.display_name.startswith(\"mixtral-serve-vllm\")\n",
-        "    ]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cf56ac4cc73b"
-      },
-      "outputs": [],
-      "source": [
-        "# Delete the endpoint using the Vertex AI fully qualified identifier for the endpoint\n",
-        "try:\n",
-        "    if delete_endpoint:\n",
-        "        endpoints = list_endpoints()\n",
-        "        for endpoint_id, endpoint_name in endpoints:\n",
-        "            endpoint = aiplatform.Endpoint(endpoint_id)\n",
-        "            print(\n",
-        "                f\"Undeploying all deployed models and deleting endpoint {endpoint_id} [{endpoint_name}]\"\n",
-        "            )\n",
-        "            endpoint.delete(force=True)\n",
-        "except Exception as e:\n",
-        "    print(e)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d25a89a34b5e"
-      },
-      "source": [
-        "### Delete Cloud Storage bucket"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PAr4UWWx4JEo"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "delete_bucket = False\n",
-        "\n",
-        "job.delete()\n",
-        "\n",
-        "if delete_bucket or os.getenv(\"ID_TESTING\"):\n",
-        "    ! gsutil rm -rf {BUCKET_URI}"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "model_garden_pytorch_mistral.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

notebooks/community/model_garden/model_garden_pytorch_mistral_deployment.ipynb

@@ -59,8 +59,10 @@
         "\n",
         "- Deploy prebuilt [Mistral models](https://huggingface.co/mistralai) with [vLLM](https://github.com/vllm-project/vllm) containers\n",
         "    - [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1): pretrained generative text model with 7 billion parameters\n",
-        "    - [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1): Instruction fine-tuned version of the Mistral-7B-v0.1 generative text model\n",
-        "    - [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2): Improved instruction fine-tuned version of Mistral-7B-Instruct-v0.1 supporting 32k context length\n",
+        "    - [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1): instruction fine-tuned version of the Mistral-7B-v0.1 generative text model\n",
+        "    - [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2): improved instruction fine-tuned version of Mistral-7B-Instruct-v0.1 supporting 32k context length\n",
+        "    - [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3): Mistral-7B-v0.2 with extended vocabulary of 32768 and supports function calling\n",
+        "    - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3): instruction fine-tuned version of the Mistral-7B-v0.3 generative text model\n",
         "\n",
         "### Costs\n",
         "\n",
@@ -156,7 +158,7 @@
         "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n",
         "\n",
         "# The pre-built serving docker images with vLLM\n",
-        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240313_0916_RC00\"\n",
+        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240620_1616_RC00\"\n",
         "\n",
         "\n",
         "def get_job_name_with_datetime(prefix: str) -> str:\n",
@@ -218,6 +220,13 @@
         "        dtype = \"float16\"\n",
         "\n",
         "    vllm_args = [\n",
+        "        \"python\",\n",
+        "        \"-m\",\n",
+        "        (\n",
+        "            \"vllm.entrypoints.openai.api_server\"\n",
+        "            if use_openai_server\n",
+        "            else \"vllm.entrypoints.api_server\"\n",
+        "        ),\n",
         "        \"--host=0.0.0.0\",\n",
         "        \"--port=7080\",\n",
         "        f\"--model=gs://vertex-model-garden-public-us/{model_id}\",\n",
@@ -242,15 +251,6 @@
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
-        "        serving_container_command=[\n",
-        "            \"python\",\n",
-        "            \"-m\",\n",
-        "            (\n",
-        "                \"vllm.entrypoints.openai.api_server\"\n",
-        "                if use_openai_server\n",
-        "                else \"vllm.entrypoints.api_server\"\n",
-        "            ),\n",
-        "        ],\n",
         "        serving_container_args=vllm_args,\n",
         "        serving_container_ports=[7080],\n",
         "        serving_container_predict_route=serving_container_predict_route,\n",
@@ -282,9 +282,9 @@
         "\n",
         "# @markdown This section deploys the prebuilt Mistral model with [vLLM](https://github.com/vllm-project/vllm) on a Vertex endpoint. It takes 15 minutes to 1 hour to finish depending on the model and the accelerator.\n",
         "\n",
-        "# @markdown Set the model to deploy.\n",
+        "# @markdown Set the model to deploy and the accelerator to use.\n",
         "\n",
-        "prebuilt_model_id = \"mistralai/Mistral-7B-v0.1\"  # @param [\"mistralai/Mistral-7B-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.2\"]\n",
+        "prebuilt_model_id = \"mistralai/Mistral-7B-v0.3\"  # @param [\"mistralai/Mistral-7B-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.2\", \"mistralai/Mistral-7B-v0.3\", \"mistralai/Mistral-7B-Instruct-v0.3\"]\n",
         "# Find Vertex AI prediction supported accelerators and regions in\n",
         "# https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
         "\n",

notebooks/community/model_garden/model_garden_rag.ipynb

@@ -0,0 +1,522 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rdr9qXnG1HaN"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E_X6E0Jl1NbD"
+      },
+      "source": [
+        "## Model Garden RAG API\n",
+        "\n",
+        "Last updated: 7/24/2024\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_openai_api_llama3_1.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_openai_api_llama3_1.ipynb\"\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_openai_api_llama3_1.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_openai_api_llama3_1.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P4tsSWku3wCh"
+      },
+      "source": [
+        "## Onboarding\n",
+        "If you have any questions, please reach out to *Vertex RAG API * team vertex-rag-eng@google.com, for the onboarding process.\n",
+        "\n",
+        "## 0. Set up the Environment and Test Project"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "u9mTxNC41S_1"
+      },
+      "outputs": [],
+      "source": [
+        "!pip3 install --force-reinstall google-cloud-aiplatform \"numpy<2.0.0\" --user"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ILNZ8_hw1WaC"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import auth\n",
+        "\n",
+        "auth.authenticate_user()\n",
+        "\n",
+        "# Install gcloud\n",
+        "!pip install google-cloud"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WuzltFSF1ZWw"
+      },
+      "source": [
+        "**Remember to restart after pip install.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yj8SdgZM1cOP"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o7x73NiprHhJ"
+      },
+      "source": [
+        "## Initialization\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ldoxVA24qnAF"
+      },
+      "outputs": [],
+      "source": [
+        "import vertexai\n",
+        "from vertexai.preview import rag\n",
+        "from vertexai.preview.generative_models import GenerativeModel, Tool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tB-fhAybq0T2"
+      },
+      "outputs": [],
+      "source": [
+        "# Set Project\n",
+        "PROJECT_ID = \"<your-project-id>\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "O2Rr1Ymlq3Uq"
+      },
+      "outputs": [],
+      "source": [
+        "vertexai.init(project=PROJECT_ID, location=\"us-central1\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cmxhW-LArK2L"
+      },
+      "source": [
+        "## Create a RAG corpus\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5E1tVMx3rAXF"
+      },
+      "outputs": [],
+      "source": [
+        "# Currently supports Google first-party embedding models\n",
+        "embedding_model_config = rag.EmbeddingModelConfig(\n",
+        "    publisher_model=\"publishers/google/models/text-embedding-004\"\n",
+        ")\n",
+        "\n",
+        "# Name your corpus\n",
+        "DISPLAY_NAME = \"<your-corpus-display-name>\"  # @param {type:\"string\"}\n",
+        "\n",
+        "rag_corpus = rag.create_corpus(\n",
+        "    display_name=DISPLAY_NAME, embedding_model_config=embedding_model_config\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "16i1ZInQrFnL"
+      },
+      "outputs": [],
+      "source": [
+        "# Check the corpus just created\n",
+        "rag.list_corpora()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sSLWYGF8rfMf"
+      },
+      "source": [
+        "## Upload a file to the corpus"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4G5uyvbdraMY"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile test.txt\n",
+        "\n",
+        "Here's a demo for Llama3 RAG"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z2vnvVO9rtDF"
+      },
+      "outputs": [],
+      "source": [
+        "rag_file = rag.upload_file(\n",
+        "    corpus_name=rag_corpus.name,\n",
+        "    path=\"test.txt\",\n",
+        "    display_name=\"test.txt\",\n",
+        "    description=\"my test\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oOhO9-G2r1Gc"
+      },
+      "source": [
+        "## Import files from Google Cloud Storage\n",
+        "Remember to grant \"Viewer\" access to the \"Vertex RAG Data Service Agent\" (with the format of service-{project_number}@gcp-sa-vertex-rag.iam.gserviceaccount.com) for your Google Cloud Storage bucket"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y64Hdd_9r5H9"
+      },
+      "outputs": [],
+      "source": [
+        "GS_BUCKET = \"gs://<your-gs-bucket-name>\"  # @param {type:\"string\"}\n",
+        "\n",
+        "response = await rag.import_files_async(  # noqa: F704\n",
+        "    corpus_name=rag_corpus.name,\n",
+        "    paths=[GS_BUCKET],\n",
+        "    chunk_size=512,\n",
+        "    chunk_overlap=50,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YiTAFiEasHLX"
+      },
+      "outputs": [],
+      "source": [
+        "# Check the files just imported. It may take a few seconds to process the imported files.\n",
+        "list(rag.list_files(corpus_name=rag_corpus.name))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bBsPDR-jsML5"
+      },
+      "source": [
+        "## Import files from Google Drive\n",
+        "Eligible paths can be https://drive.google.com/drive/folders/{folder_id} or https://drive.google.com/file/d/{file_id}.\n",
+        "\n",
+        "Remember to grant \"Viewer\" access to the \"Vertex RAG Data Service Agent\" (with the format of `service-{project_number}@gcp-sa-vertex-rag.iam.gserviceaccount.com`) for your Drive folder/files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "u16-LvjT2Thi"
+      },
+      "outputs": [],
+      "source": [
+        "FILE_ID = \"<your-file-id>\"  # @param {type:\"string\"}\n",
+        "FILE_PATH = f\"https://drive.google.com/file/d/{FILE_ID}\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iY4_6tshsPSA"
+      },
+      "outputs": [],
+      "source": [
+        "rag.import_files(\n",
+        "    corpus_name=rag_corpus.name,\n",
+        "    paths=[FILE_PATH],\n",
+        "    chunk_size=1024,\n",
+        "    chunk_overlap=100,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Dl8gPm9l4DQ3"
+      },
+      "outputs": [],
+      "source": [
+        "# Check the files just imported. It may take a few seconds to process the imported files.\n",
+        "list(rag.list_files(corpus_name=rag_corpus.name))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I1-joFPys-FS"
+      },
+      "source": [
+        "## Using Rag Retrieval Tool and Generate Content API for non-self-deployed Llama3 MaaS endpoint\n",
+        "\n",
+        "When retrieval query similarity distance < vector_distance_threshold, generate content will cite the retrieved context.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LvTPfijxtAQO"
+      },
+      "outputs": [],
+      "source": [
+        "rag_resource = rag.RagResource(\n",
+        "    rag_corpus=rag_corpus.name,\n",
+        "    # Need to manually get the ids from rag.list_files.\n",
+        "    # rag_file_ids=[],\n",
+        ")\n",
+        "\n",
+        "rag_retrieval_tool = Tool.from_retrieval(\n",
+        "    retrieval=rag.Retrieval(\n",
+        "        source=rag.VertexRagStore(\n",
+        "            rag_resources=[rag_resource],  # Currently only 1 corpus is allowed.\n",
+        "            similarity_top_k=5,\n",
+        "            vector_distance_threshold=0.4,\n",
+        "        ),\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T3IffRu42nRp"
+      },
+      "outputs": [],
+      "source": [
+        "ENDPOINT = f\"projects/{PROJECT_ID}/locations/us-central1/publishers/meta/models/llama3-405b-instruct-maas\"  # @param {type:\"string\"}\n",
+        "\n",
+        "rag_model = GenerativeModel(ENDPOINT, tools=[rag_retrieval_tool])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SFEEv2u0tVNz"
+      },
+      "outputs": [],
+      "source": [
+        "QUERY = \"What is RAG and why it is helpful?\"  # @param {type:\"string\"}\n",
+        "\n",
+        "response = rag_model.generate_content(QUERY)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0I2EniAZtiUt"
+      },
+      "outputs": [],
+      "source": [
+        "response"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cje1WHtOtt2q"
+      },
+      "source": [
+        "## Using Rag Retrieval Tool with other generation API for non-self-deployed Llama3 MaaS endpoint\n",
+        "\n",
+        "The retrieved contexts can be passed to any SDK or model generation API to generate final results.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dK7YmoIGtyki"
+      },
+      "outputs": [],
+      "source": [
+        "QUERY = \"What is RAG and why it is helpful?\"  # @param {type:\"string\"}\n",
+        "\n",
+        "rag_resource = rag.RagResource(\n",
+        "    rag_corpus=rag_corpus.name,\n",
+        "    # Need to manually get the ids from rag.list_files.\n",
+        "    # rag_file_ids=[],\n",
+        ")\n",
+        "\n",
+        "response = rag.retrieval_query(\n",
+        "    rag_resources=[rag_resource],  # Currently only 1 corpus is allowed.\n",
+        "    text=QUERY,\n",
+        "    similarity_top_k=5,\n",
+        "    vector_distance_threshold=0.4,\n",
+        ")\n",
+        "\n",
+        "# The retrieved context can be passed to any SDK or model generation API to generate final results.\n",
+        "retrieved_context = \" \".join(\n",
+        "    [context.text for context in response.contexts.contexts]\n",
+        ").replace(\"\\n\", \"\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "--GwKlAO29bZ"
+      },
+      "outputs": [],
+      "source": [
+        "retrieved_context"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y9wTk2vW8tq0"
+      },
+      "source": [
+        "## Cleaning up\n",
+        "\n",
+        "Clean up resources created in this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MRP8GZkw80aT"
+      },
+      "outputs": [],
+      "source": [
+        "delete_rag_corpus = True  # @param {type:\"boolean\"}\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "if delete_rag_corpus:\n",
+        "    rag_corpus_list = rag.list_corpora()\n",
+        "    for rag_corpus in rag_corpus_list:\n",
+        "        rag.delete_corpus(name=rag_corpus.name)\n",
+        "\n",
+        "if delete_bucket:\n",
+        "    ! gsutil rm -r gs://{BUCKET_NAME}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kTHflSAhupiN"
+      },
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For more details on RAG corpus/file management and detailed support please visit https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/rag-api\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_rag.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/synthetic_data_generation_using_llama3_1.ipynb

@@ -0,0 +1,464 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NoEDALsivNDl"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jCo1J-gNwJM2"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Synthetic Data Generation using Llama 3.1\n",
+        "\n",
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fsynthetic_data_generation_using_llama3_1.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/synthetic_data_generation_using_llama3_1.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3FlmJqqHyYwm"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates generating synthetic data using the [Llama 3.1 405B service API](https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama3-405b-instruct-maas).\n",
+        "\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "Leverage the Llama 3.1 405B service API to gnerate synthetic data. The framework is based on [Snowfakery](https://snowfakery.readthedocs.io/en/latest/) which is itself based on [Faker](https://faker.readthedocs.io/en/master/). It requires the expected outputs to be codified in a YAML file per Snowfakery specs, detailing all the required fields and their respective data generation strategies.\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RlelO-pw5xaT"
+      },
+      "source": [
+        "## Steps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AjA_UYD25_1w"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Setup Google Cloud project\n",
+        "\n",
+        "!pip install --upgrade --user -q openai snowfakery==3.6.2 wikipedia-api==0.6.0\n",
+        "\n",
+        "import os\n",
+        "from datetime import datetime\n",
+        "\n",
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "# Define project information\n",
+        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
+        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
+        "\n",
+        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
+        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
+        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
+        "\n",
+        "# Cloud Storage bucket for storing the experiment artifacts.\n",
+        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
+        "# prefer using your own GCS bucket, change the value yourself below.\n",
+        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
+        "\n",
+        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
+        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}\"\n",
+        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
+        "else:\n",
+        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
+        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
+        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
+        "    bucket_region = shell_output[0].strip().lower()\n",
+        "    if bucket_region != REGION:\n",
+        "        raise ValueError(\n",
+        "            \"Bucket region %s is different from notebook region %s\"\n",
+        "            % (bucket_region, REGION)\n",
+        "        )\n",
+        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
+        "\n",
+        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
+        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"llama_3_1\")\n",
+        "\n",
+        "\n",
+        "# Initialize Vertex AI API.\n",
+        "print(\"Initializing Vertex AI API.\")\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
+        "\n",
+        "from google.colab import auth\n",
+        "\n",
+        "auth.authenticate_user(project_id=PROJECT_ID)\n",
+        "\n",
+        "\n",
+        "import google.auth\n",
+        "\n",
+        "# Programmatically get an access token\n",
+        "creds, _ = google.auth.default(\n",
+        "    scopes=[\"https://www.googleapis.com/auth/cloud-platform\"]\n",
+        ")\n",
+        "auth_req = google.auth.transport.requests.Request()\n",
+        "creds.refresh(auth_req)\n",
+        "# Note: the credential lives for 1 hour by default (https://cloud.google.com/docs/authentication/token-types#at-lifetime); after expiration, it must be refreshed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Yl8gDtmA75hD"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Creating Plugins and Prompts\n",
+        "\n",
+        "# @markdown The following cells create the 2 custom plugins we need for this use case along with the needed prompts.\n",
+        "\n",
+        "import logging\n",
+        "import sys\n",
+        "import types\n",
+        "from io import StringIO\n",
+        "\n",
+        "import jinja2\n",
+        "import openai\n",
+        "import wikipediaapi\n",
+        "from snowfakery import generate_data\n",
+        "from snowfakery.plugins import SnowfakeryPlugin\n",
+        "\n",
+        "MODEL_ID = \"meta/llama3-405b-instruct-maas\"\n",
+        "ENDPOINT = \"aiplatform.googleapis.com\"\n",
+        "\n",
+        "# Pass the Vertex endpoint and authentication to the OpenAI SDK\n",
+        "client = openai.OpenAI(\n",
+        "    base_url=f\"https://us-central1-{ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/openapi\",\n",
+        "    api_key=creds.token,\n",
+        ")\n",
+        "\n",
+        "\n",
+        "class SyntheticDataGeneration:\n",
+        "    \"\"\"\n",
+        "    Implements all the extra functionality needed for this use-case\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # The first plugin allows us to interact with the Llama 3.1 405B service API.\n",
+        "    class Plugins(types.ModuleType):\n",
+        "        \"\"\"\n",
+        "        Provides the plugins needed to extend Snowfakery\n",
+        "        \"\"\"\n",
+        "\n",
+        "        class Llama3(SnowfakeryPlugin):\n",
+        "            \"\"\"\n",
+        "            Plugin for interacting with Llama3 API service.\n",
+        "            \"\"\"\n",
+        "\n",
+        "            class Functions:\n",
+        "                \"\"\"\n",
+        "                Functions to implement field / object level data generation\n",
+        "                \"\"\"\n",
+        "\n",
+        "                def fill_prompt(self, prompt_name: str, **kwargs) -> str:\n",
+        "                    \"\"\"\n",
+        "                    Returns a formatted prompt\n",
+        "                    \"\"\"\n",
+        "                    return (\n",
+        "                        jinja2.Environment(\n",
+        "                            loader=jinja2.FileSystemLoader(searchpath=\"./\")\n",
+        "                        )\n",
+        "                        .get_template(prompt_name)\n",
+        "                        .render(**kwargs)\n",
+        "                    )\n",
+        "\n",
+        "                def generate(\n",
+        "                    self,\n",
+        "                    prompt_name: str,\n",
+        "                    model=\"Llama3\",\n",
+        "                    temperature=0.9,\n",
+        "                    top_p=1,\n",
+        "                    **kwargs,\n",
+        "                ) -> str | None:\n",
+        "                    \"\"\"\n",
+        "                    A wrapper around Llama3 plugin\n",
+        "                    \"\"\"\n",
+        "                    prompt = self.fill_prompt(prompt_name, **kwargs)\n",
+        "                    try:\n",
+        "                        response = client.chat.completions.create(\n",
+        "                            model=MODEL_ID,\n",
+        "                            messages=[{\"role\": \"user\", \"content\": prompt}],\n",
+        "                            temperature=temperature,\n",
+        "                            top_p=top_p,\n",
+        "                        )\n",
+        "                        return response.choices[0].message.content\n",
+        "                    except Exception as e:\n",
+        "                        logging.trace(\n",
+        "                            (\n",
+        "                                \"Unable to generate text using %s.\\n\"\n",
+        "                                \"Prepared Prompt: \\n%s\\n\\nError: %s\"\n",
+        "                            ),\n",
+        "                            prompt_name,\n",
+        "                            prompt,\n",
+        "                            e,\n",
+        "                        )\n",
+        "                        return None\n",
+        "\n",
+        "        # The second plugin gives us the ability to interact with Wikipedia and fetch the contents for a given page.\n",
+        "        class Wikipedia(SnowfakeryPlugin):\n",
+        "            \"\"\"\n",
+        "            Plugin for interacting with Wikipedia.\n",
+        "            \"\"\"\n",
+        "\n",
+        "            class Functions:\n",
+        "                \"\"\"\n",
+        "                Implements a single function to fetch a Wikipedia page\n",
+        "                \"\"\"\n",
+        "\n",
+        "                def get_page(self, title: str):\n",
+        "                    \"\"\"\n",
+        "                    Returns the title, URL and sections of the given wikipedia page\n",
+        "                    \"\"\"\n",
+        "                    logging.info(\"Parsing Wikipedia Page %s\", title)\n",
+        "                    page = wikipediaapi.Wikipedia(\n",
+        "                        \"Snowfakery (example@google.com)\", \"en\"\n",
+        "                    ).page(title)\n",
+        "                    results = {\"sections\": {}, \"title\": page.title, \"url\": page.fullurl}\n",
+        "                    sections = [(s.title, s) for s in page.sections]\n",
+        "                    while sections:\n",
+        "                        sec_title, sec_obj = sections.pop()\n",
+        "                        if sec_title in [\n",
+        "                            \"External links\",\n",
+        "                            \"References\",\n",
+        "                            \"See also\",\n",
+        "                            \"Further reading\",\n",
+        "                        ]:\n",
+        "                            continue\n",
+        "                        if sec_obj.text:\n",
+        "                            results[\"sections\"][sec_title] = sec_obj.text\n",
+        "                        for sub_sec in sec_obj.sections:\n",
+        "                            sections.append((f\"{sec_title} - {sub_sec.title}\", sub_sec))\n",
+        "                    logging.info(\"Parsing Wikipedia Page %s Complete\", title)\n",
+        "                    return results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WSryene29Dan"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Making plugins discoverable\n",
+        "\n",
+        "# @markdown We add the created class to sys.modules to ensure Snowfakery can find them and import them as modules as needed.\n",
+        "\n",
+        "sys.modules[\"SyntheticDataGeneration.Plugins\"] = SyntheticDataGeneration.Plugins(\n",
+        "    name=\"SyntheticDataGeneration.Plugins\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cJaPNlBi9bJr"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Creating Prompt Templates\n",
+        "\n",
+        "%%writefile blog_generator.jinja\n",
+        "You are an expert content creator who writes detailed, factual blogs.\n",
+        "You have been asked to write a blog about {{idea_title}}.\n",
+        "To get you started, you have also been given the following context about the topic:\n",
+        "\n",
+        "{{idea_body}}\n",
+        "\n",
+        "Ensure the blog that you write is interesting,detailed and factual.\n",
+        "Take a deep breath and start writing:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BbOYKhD5-Bht"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile comment_generator.jinja\n",
+        "You are {{first_name}} {{last_name}}. You are {{age}} years old. You are interested in {{interests}}. You work at {{organization}} as a {{profession}}.\n",
+        "You came across the following article:\n",
+        "\n",
+        "{{blog_title}}\n",
+        "\n",
+        "{{blog_body}}\n",
+        "\n",
+        "Present your thoughts and feelings about the article in a short comment.\n",
+        "\n",
+        "Comment:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sPe-UoD_-Ilo"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Creating the Recipe\n",
+        "\n",
+        "# @markdown In order to generate synthetic data, the schema of the synthetic data must be defined first. This is done by creating a recipe in a YAML format as demonstrated below, more details on writing recipes can be found [here](https://snowfakery.readthedocs.io/en/latest/#central-concepts).\n",
+        "\n",
+        "recipe = \"\"\"\n",
+        "- plugin: SyntheticDataGeneration.Plugins.Wikipedia\n",
+        "- plugin: SyntheticDataGeneration.Plugins.Llama3\n",
+        "- option: wiki_title\n",
+        "- var: __seed\n",
+        "  value:\n",
+        "    - Wikipedia.get_page :\n",
+        "      title : ${{wiki_title}}\n",
+        "\n",
+        "- object : users\n",
+        "  count : ${{random_number(min=100, max=500)}}\n",
+        "  fields :\n",
+        "    first_name : ${{fake.FirstName}}\n",
+        "    last_name : ${{fake.FirstName}}\n",
+        "    age:\n",
+        "      random_number:\n",
+        "        min: 18\n",
+        "        max: 95\n",
+        "    email : ${{fake.Email}}\n",
+        "    phone : ${{fake.PhoneNumber}}\n",
+        "    interests : ${{fake.Bs}}\n",
+        "    postal_code : ${{fake.Postalcode}}\n",
+        "    organization : ${{fake.Company}}\n",
+        "    profession : ${{fake.Job}}\n",
+        "\n",
+        "- object : seeds\n",
+        "  fields :\n",
+        "    title : ${{__seed['title']}}\n",
+        "    url : ${{__seed['url']}}\n",
+        "    section_count : ${{__seed['sections'] | length}}\n",
+        "\n",
+        "  friends:\n",
+        "    - object : blog_ideas\n",
+        "      count : ${{seeds.section_count}}\n",
+        "      fields :\n",
+        "        seed_id : ${{seeds.id}}\n",
+        "        section : ${{(__seed.sections.keys() | list)[child_index]}}\n",
+        "        body : ${{__seed.sections[section]}}\n",
+        "\n",
+        "      friends:\n",
+        "        - object : blog_posts\n",
+        "          fields :\n",
+        "            blog_idea_id : ${{blog_ideas.id}}\n",
+        "            title : ${{seeds.title}} - ${{blog_ideas.section}}\n",
+        "            body :\n",
+        "              - Llama3.generate:\n",
+        "                prompt_name : blog_generator.jinja\n",
+        "                idea_title : ${{title}}\n",
+        "                idea_body : ${{blog_ideas.body}}\n",
+        "            author : Llama3\n",
+        "\n",
+        "          friends:\n",
+        "            - object : blog_post_comments\n",
+        "              fields :\n",
+        "                blog_post_id : ${{blog_posts.id}}\n",
+        "                author_id :\n",
+        "                  random_reference : users\n",
+        "                author_email : ${{author_id.email}}\n",
+        "                comment :\n",
+        "                  - Llama3.generate:\n",
+        "                    prompt_name : comment_generator.jinja\n",
+        "                    first_name : ${{author_id.first_name}}\n",
+        "                    last_name : ${{author_id.last_name}}\n",
+        "                    age : ${{author_id.age}}\n",
+        "                    interests : ${{author_id.interests}}\n",
+        "                    organization : ${{author_id.organization}}\n",
+        "                    profession : ${{author_id.profession}}\n",
+        "                    blog_title : ${{blog_posts.title}}\n",
+        "                    blog_body : ${{blog_posts.body | truncate(1000)}}\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JTEQU-u--ei7"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Generating Data\n",
+        "\n",
+        "generate_data(\n",
+        "    StringIO(recipe),\n",
+        "    output_format=\"csv\",\n",
+        "    output_folder=\"outputs\",\n",
+        "    user_options={\"wiki_title\": \"Python_(programming_language)\"},\n",
+        ")\n",
+        "\n",
+        "# @markdown Results The synthetic data has been generated and stored as CSV files in the `outputs` folder."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "synthetic_data_generation_using_llama3_1.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/notebook_template_review.py

@@ -1296,7 +1296,7 @@ def replace_cl(text : str ) -> str:
         'Vertex Vizier': '{{vertex_vizier_name}}',
         'Vertex AI Vizier': '{{vertex_vizier_name}}',
         'Vizier': '{{vertex_vizier_name}}',
-        'Vertex Explainable AI': '{{vertex_xai_name}}',
+        'Vertex Explainable AI': '{{xai_name_short}}',
         'Explainable AI': '{{vertex_xai_name}}',
         'NAS': '{{vertex_nas_name_short}}',
         'Vertex AI Neural Architectural Search': '{{vertex_nas_name}}',
@@ -1306,6 +1306,10 @@ def replace_cl(text : str ) -> str:
         #'Vertex SDK': '{{vertex_sdk_name}}',
         #'Vertex AI SDK': '{{vertex_sdk_name}}',
         'Vertex AI': '{{vertex_ai_name}}',
+        'Vertex AI batch prediction': '{{vertex_ai_name}} {{batch_prediction_name}}',
+        'Vertex AI SDK for Python': '{{vertex_sdk_python}}',
+        'Ray on Vertex AI': '{{ray_vertex_ai_name}}',
+        'Google Cloud console': '{{console_name}}',
         
         'Cloud Storage': '{{storage_name}}',
         'GCS': '{{storage_name}}',

notebooks/official/CODEOWNERS

@@ -2,7 +2,7 @@
 # the repo. Unless a later match takes precedence,
 # @global-owner1 and @global-owner2 will be requested for
 # review when someone opens a pull request.
-* @GoogleCloudPlatform/vertex-ai-samples-contributors  @GoogleCloudPlatform/caiis-tw
+* @GoogleCloudPlatform/vertex-ai-samples-contributors @GoogleCloudPlatform/caiis-tw
 
 # matching_engine folder
 /matching_engine @shenzhimo2 @ivanmkc
@@ -56,3 +56,5 @@
 /training/tpuv5e_gemma_peft_finetuning_and_serving.ipynb @brianchunkang
 /training/tpuv5e_llama2_pytorch_finetuning_and_serving.ipynb @brianchunkang @chiefkarlin
 /prediction/get_started_with_psc_private_endpoint.ipynb @tianjiaoliu
+/ray_on_vertex_ai/spark_on_ray_on_vertex_ai.ipynb @ravi-dalal
+/generative_ai/mistralai_intro.ipynb @sujituk

notebooks/official/automl/automl_image_classification_online_prediction.ipynb

@@ -32,25 +32,27 @@
         "# AutoML training image classification model for online prediction\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_classification_online_prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_classification_online_prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fautoml%2Fautoml_image_classification_online_prediction.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl//automl_image_classification_online_prediction.ipynb\">\n",
-        "       <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_classification_online_prediction.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -95,7 +97,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
+        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
       ]
     },
     {
@@ -118,15 +120,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the latest version of Vertex AI SDK for Python."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -138,54 +147,86 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 tensorflow"
+        "                                 tensorflow==2.15.1"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "f82e28c631cc"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "191d1345e064"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -197,103 +238,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -335,7 +280,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -358,8 +303,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "import google.cloud.aiplatform as aiplatform"
       ]
     },
@@ -393,7 +336,7 @@
       "source": [
         "# Tutorial\n",
         "\n",
-        "Now you are ready to start creating your own AutoML image classification model."
+        "Now you're ready to start creating your own AutoML image classification model."
       ]
     },
     {
@@ -415,9 +358,7 @@
       },
       "outputs": [],
       "source": [
-        "IMPORT_FILE = (\n",
-        "    \"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\"\n",
-        ")"
+        "IMPORT_FILE = \"gs://cloud-samples-data/ai-platform/flowers/flowers.csv\""
       ]
     },
     {
@@ -557,7 +498,7 @@
         "\n",
         "The `run` method when completed returns the `Model` resource.\n",
         "\n",
-        "The execution of the training pipeline will take upto 20 minutes."
+        "The execution of the training pipeline takes upto 20 minutes."
       ]
     },
     {
@@ -587,7 +528,7 @@
       "source": [
         "## Review model evaluation scores\n",
         "\n",
-        "After your model training has finished, you can review the evaluation scores for it using the `list_model_evaluations()` method. This method will return an iterator for each evaluation slice."
+        "After your model training has finished, you can review the evaluation scores for it using the `list_model_evaluations()` method. This method returns an iterator for each evaluation slice."
       ]
     },
     {
@@ -645,7 +586,7 @@
       "source": [
         "### Get test item\n",
         "\n",
-        "You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
+        "You use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- the point is to demonstrate how to make a prediction."
       ]
     },
     {
@@ -726,7 +667,7 @@
       "source": [
         "## Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you're done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -762,16 +703,11 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
-        "\n",
         "# Delete the dataset using the Vertex dataset object\n",
         "dataset.delete()\n",
         "\n",
-        "try:\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
         "\n",
         "# Delete the model using the Vertex model object\n",
         "model.delete()\n",
@@ -779,8 +715,10 @@
         "# Delete the AutoML trainig job\n",
         "dag.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb

@@ -32,25 +32,27 @@
         "# Vertex AI SDK: AutoML tabular forecasting model for batch prediction\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fautoml%2Fsdk_automl_tabular_forecasting_batch.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -62,9 +64,9 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use the Vertex AI SDK to create tabular forecasting models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
+        "This tutorial demonstrates how to use the Vertex AI SDK to create tabular forecasting models and generate batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
         "\n",
-        "Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
+        "Learn more about [Forecasting with AutoML](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
       ]
     },
     {
@@ -75,19 +77,19 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
+        "In this tutorial, you learn how to create an AutoML tabular forecasting model from a Python script, and then generate batch prediction using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `AutoML Training`\n",
-        "- `Vertex AI Batch Prediction`\n",
-        "- `Vertex AI Model` resource\n",
+        "- AutoML Training\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI Dataset` resource.\n",
-        "- Train an `AutoML` tabular forecasting `Model` resource.\n",
-        "- Obtain the evaluation metrics for the `Model` resource.\n",
+        "- Create a Vertex AI dataset resource.\n",
+        "- Train an AutoML tabular forecasting model resource.\n",
+        "- Obtain the evaluation metrics for the model resource.\n",
         "- Make a batch prediction."
       ]
     },
@@ -99,7 +101,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is a time series dataset containing samples drawn from the Iowa Liquor Retail Sales dataset. Data is made available by the Iowa Department of Commerce. It is provided under the Creative Commons Zero v1.0 Universal license. For more details, see: https://console.cloud.google.com/marketplace/product/iowa-department-of-commerce/iowa-liquor-sales. This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in BigQuery."
+        "The dataset used for this tutorial is a time series dataset containing samples drawn from the Iowa Liquor Retail Sales dataset. Data is made available by the Iowa Department of Commerce. It's provided under the Creative Commons Zero v1.0 Universal license. For more details, see: https://console.cloud.google.com/marketplace/product/iowa-department-of-commerce/iowa-liquor-sales. This dataset doesn't require any feature engineering. The version of the dataset used in this tutorial is stored in BigQuery."
       ]
     },
     {
@@ -125,12 +127,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -141,49 +150,86 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "! pip3 install --upgrade --quiet google-cloud-aiplatform"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -195,91 +241,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -308,7 +275,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -319,7 +286,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -341,8 +308,7 @@
       "source": [
         "import urllib\n",
         "\n",
-        "import google.cloud.aiplatform as aiplatform\n",
-        "from google.cloud import bigquery"
+        "from google.cloud import aiplatform, bigquery"
       ]
     },
     {
@@ -373,9 +339,9 @@
         "id": "tutorial_start:automl"
       },
       "source": [
-        "# Tutorial\n",
+        "## Tutorial\n",
         "\n",
-        "Now you are ready to start creating your own AutoML tabular forecasting model."
+        "Now you're ready to begin creating your own AutoML tabular forecasting model."
       ]
     },
     {
@@ -410,11 +376,11 @@
       "source": [
         "### Create the Dataset\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `TimeSeriesDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource by using the `create` method of the `TimeSeriesDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
-        "- `bq_source`: Alternatively, import data items from a BigQuery table into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `gcs_source`: A list of one or more dataset index files to import the data items into the dataset resource.\n",
+        "- `bq_source`: Alternatively, import data items from a BigQuery table into the dataset resource.\n",
         "\n",
         "This operation may take several minutes."
       ]
@@ -464,13 +430,15 @@
       "source": [
         "### Create and run training job\n",
         "\n",
-        "To train an AutoML model, you perform two steps: 1) create a training job, and 2) run the job.\n",
+        "To train an AutoML model, you perform two steps: \n",
+        "1) Create a training job.\n",
+        "2) Specify your training parameters and run the job.\n",
         "\n",
         "#### Create training job\n",
         "\n",
-        "An AutoML training job is created with the `AutoMLForecastingTrainingJob` class, with the following parameters:\n",
+        "An AutoML training job is created using the `AutoMLForecastingTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
+        "- `display_name`: The human readable name for the training job resource.\n",
         "- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
         "- `optimization_objective`: The optimization objective to minimize or maximize.\n",
         "    - `minimize-rmse`\n",
@@ -505,20 +473,27 @@
       "source": [
         "#### Run the training pipeline\n",
         "\n",
-        "Next, you start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, start the training job by invoking the `run` method, with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
-        "- `model_display_name`: The human readable name for the trained model.\n",
-        "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
-        "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
-        "- `target_column`: The name of the column to train as the label.\n",
-        "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
+        "- `dataset`: The dataset resource to train the model.\n",
+        "- `target_column`: The column in the dataset that contains the values the model is trying to forecast.\n",
         "- `time_column`: Time-series column for the forecast model.\n",
         "- `time_series_identifier_column`: ID column for the time-series column.\n",
+        "- `available_at_forecast_columns`: List of columns that are available at the time of forecasting.\n",
+        "- `unavailable_at_forecast_columns`: List of columns that aren't available at the time of forecasting.\n",
+        "- `time_series_attribute_columns`: Columns that contain attributes or metadata related to the time series data, such as \"city,\" \"zip_code,\" and \"county\" in this example. These attributes can help the model understand the context of the time series.\n",
+        "- `forecast_horizon`: It determines how far into the future you want to predict, representing the number of time steps ahead for which the model generates predictions.\n",
+        "- `context_window`: The number of historical time steps the model uses as context for making predictions. A context window of 30 means the model uses data from the past 30 time steps to forecast future values.\n",
+        "- `data_granularity_unit`: The unit of time used for granularity in the data, such as \"day\" or \"hour.\" This specifies the time interval between data points.\n",
+        "- `data_granularity_count`: The count of the granularity unit. For example, a data_granularity_count of 1 with a `data_granularity_unit` of \"day\" means each data point represents one day.\n",
+        "- `weight_column`: This parameter lets you assign different weights to different data points in your training set.\n",
+        "- `budget_milli_node_hours`: Maximum training time specified in unit of millihours (1000 = hour).\n",
+        "- `model_display_name`: The human readable name for the trained model.\n",
+        "- `predefined_split_column_name`: The name of a column used to specify predefined splits for training and evaluation. If not used, it’s set to `None`.\n",
         "\n",
-        "The `run` method when completed returns the `Model` resource.\n",
+        "The `run` method when completed returns the model resource.\n",
         "\n",
-        "The execution of the training pipeline will take up to one hour."
+        "The execution of the training pipeline may take up to one hour."
       ]
     },
     {
@@ -556,7 +531,7 @@
       "source": [
         "## Review model evaluation scores\n",
         "\n",
-        "After your model training has finished, you can review the evaluation scores for "
+        "Once your model training is complete, you can examine the evaluation scores to assess the model performance"
       ]
     },
     {
@@ -592,15 +567,15 @@
       "source": [
         "### Make the batch prediction request\n",
         "\n",
-        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method using a BigQuery source and destination, with the following parameters:\n",
+        "Now that your Model resource is trained, you can make a batch prediction by invoking the `batch_predict()` method using a BigQuery source and destination, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `bigquery_source`: BigQuery URI to a table, up to 2000 characters long. For example: `bq://projectId.bqDatasetId.bqTableId`\n",
-        "- `bigquery_destination_prefix`: The BigQuery dataset or table for storing the batch prediction resuls.\n",
+        "- `bigquery_destination_prefix`: The BigQuery dataset or table for storing the batch prediction results.\n",
         "- `instances_format`: The format for the input instances. Since a BigQuery source is used here, this should be set to `bigquery`.\n",
         "- `predictions_format`: The format for the output predictions, `bigquery` is used here to output to a BigQuery table.\n",
         "- `generate_explanations`: Set to `True` to generate explanations.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -618,11 +593,11 @@
         "batch_predict_bq_output_uri_prefix = \"bq://{}.{}\".format(\n",
         "    PROJECT_ID, batch_predict_bq_output_dataset_name\n",
         ")\n",
-        "# Must be the same region as batch_predict_bq_input_uri\n",
+        "# Must be the same location as batch_predict_bq_input_uri\n",
         "client = bigquery.Client(project=PROJECT_ID)\n",
         "bq_dataset_id = bigquery.Dataset(batch_predict_bq_output_dataset_path)\n",
-        "dataset_region = \"US\"  # @param {type : \"string\"}\n",
-        "bq_dataset_id.location = dataset_region\n",
+        "dataset_location = \"US\"  # @param {type : \"string\"}\n",
+        "bq_dataset_id.location = dataset_location\n",
         "# delete any existing dataset\n",
         "try:\n",
         "    client.delete_dataset(bq_dataset_id, delete_contents=True)\n",
@@ -631,7 +606,7 @@
         "bq_dataset = client.create_dataset(bq_dataset_id)\n",
         "print(\n",
         "    \"Created bigquery dataset {} in {}\".format(\n",
-        "        batch_predict_bq_output_dataset_path, dataset_region\n",
+        "        batch_predict_bq_output_dataset_path, dataset_location\n",
         "    )\n",
         ")"
       ]
@@ -642,9 +617,9 @@
         "id": "99b7a9287ba6"
       },
       "source": [
-        "For AutoML models, manual scaling can be adjusted by setting both min and max nodes i.e., `starting_replica_count` and `max_replica_count` as the same value(in this example, set to 1). The node count can be increased or decreased as required by load.\n",
+        "For AutoML models, manual scaling can be adjusted by setting both min and max nodes i.e., `starting_replica_count` and `max_replica_count` as the same value(in this example, set to 1). The node count can be increased or decreased as required by the load\n",
         " \n",
-        "`batch_predict` can export predictions either to BigQuery or GCS. This example exports to BigQuery."
+        "The `batch_predict` method can export predictions either to BigQuery or GCS. In this example, the predictions are exported to BigQuery"
       ]
     },
     {
@@ -680,7 +655,7 @@
       "source": [
         "### Wait for completion of batch prediction job\n",
         "\n",
-        "Next, wait for the batch job to complete. Alternatively, you can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
+        "Next, wait for the batch job to complete. Alternatively, you can set the `sync` parameter to `True` in the `batch_predict()` method to wait until the batch prediction job is completed."
       ]
     },
     {
@@ -702,7 +677,7 @@
       "source": [
         "### Get the predictions and explanations\n",
         "\n",
-        "Next, get the results from the completed batch prediction job and print them out. Each result row will include the prediction and explanation."
+        "Next, get the results from the completed batch prediction job and print them out. Each result row includes the prediction and explanation."
       ]
     },
     {
@@ -864,14 +839,14 @@
         "\n",
         "# Delete the dataset\n",
         "try:\n",
-        "    client.delete_dataset(bq_dataset_id)\n",
+        "    client.delete_dataset(bq_dataset_id, delete_contents=True, not_found_ok=True)\n",
         "except Exception as e:\n",
         "    print(e)\n",
         "\n",
         "# Set this to true only if you'd like to delete your bucket\n",
-        "delete_bucket = False\n",
+        "delete_bucket = False  # set True for deletion\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb

@@ -32,24 +32,27 @@
         "# Vertex AI SDK for Python: AutoML training tabular regression model for batch prediction using BigQuery\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fautoml%2Fsdk_automl_tabular_regression_batch_bq.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "</table>\n"
       ]
     },
     {
@@ -61,7 +64,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create tabular regression models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
+        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create tabular regression models and generate batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
         "\n",
         "Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
       ]
@@ -81,17 +84,14 @@
         "- Vertex AI Datasets (Tabular)\n",
         "- Vertex AI Training (AutoML Tabular Training)\n",
         "- Vertex AI Model Registry\n",
-        "- Vertex AI Endpoint\n",
-        "- Vertex AI Batch predictions\n",
+        "- Vertex AI batch prediction\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a Vertex AI `Dataset` resource.\n",
-        "- Train the model.\n",
-        "- View the model evaluation.\n",
-        "- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
-        "- Make a prediction.\n",
-        "- Undeploy the `Model`."
+        "- Create a Vertex AI dataset resource.\n",
+        "- Train an AutoML tabular regression model resource.\n",
+        "- Obtain the evaluation metrics for the model resource.\n",
+        "- Make a batch prediction."
       ]
     },
     {
@@ -102,7 +102,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [GSOD dataset](https://console.cloud.google.com/marketplace/product/noaa-public/gsod) from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). In this version of the dataset, you use the year, month, and day fields to predict the mean daily temperature (mean_temp)."
+        "The dataset used for this tutorial is the [GSOD dataset](https://console.cloud.google.com/marketplace/product/noaa-public/gsod) from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). In this version of the dataset, you use the year, month, and day fields to predict the mean daily temperature (`mean_temp`)."
       ]
     },
     {
@@ -116,7 +116,6 @@
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",
         "* Vertex AI\n",
-        "* Cloud Storage\n",
         "* BigQuery / BigQuery ML\n",
         "\n",
         "Learn about [Vertex AI\n",
@@ -127,12 +126,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -153,40 +159,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "restart"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
-        "#### Set your project ID\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -198,90 +244,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "**2. Local JupyterLab instance, uncomment and run:**\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "457c78b08293"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d3e571ce6c56"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "984a0526fb68"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2c549a59cca4"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -302,8 +270,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aiplatform\n",
-        "from google.cloud import bigquery"
+        "from google.cloud import aiplatform, bigquery"
       ]
     },
     {
@@ -325,7 +292,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -334,9 +301,9 @@
         "id": "tutorial_start:automl"
       },
       "source": [
-        "# Tutorial\n",
+        "## Tutorial\n",
         "\n",
-        "Now you are ready to start creating your own AutoML tabular regression model."
+        "Now you're ready to start creating your own AutoML tabular regression model."
       ]
     },
     {
@@ -345,7 +312,7 @@
         "id": "import_file:u_dataset,bq"
       },
       "source": [
-        "#### Location of BigQuery training data.\n",
+        "### Location of BigQuery training data.\n",
         "\n",
         "Set the `IMPORT_File` variable to the location of the data table in BigQuery."
       ]
@@ -380,7 +347,7 @@
       },
       "outputs": [],
       "source": [
-        "# Create client in default region\n",
+        "# Create client in default location\n",
         "bq_client = bigquery.Client(\n",
         "    project=PROJECT_ID,\n",
         "    credentials=aiplatform.initializer.global_config.credentials,\n",
@@ -395,13 +362,13 @@
       },
       "outputs": [],
       "source": [
-        "# Create training dataset in default region\n",
+        "# Create training dataset in default location\n",
         "TRAINING_INPUT_DATASET_ID = \"gsod_training_unique\"\n",
         "bq_dataset = bigquery.Dataset(f\"{PROJECT_ID}.{TRAINING_INPUT_DATASET_ID}\")\n",
         "bq_dataset = bq_client.create_dataset(bq_dataset)\n",
         "print(f\"Created dataset {bq_client.project}.{bq_dataset.dataset_id}\")\n",
         "\n",
-        "# Create test dataset in default region\n",
+        "# Create test dataset in default location\n",
         "PREDICTION_INPUT_DATASET_ID = \"gsod_prediction_unique\"\n",
         "bq_dataset = bigquery.Dataset(f\"{PROJECT_ID}.{PREDICTION_INPUT_DATASET_ID}\")\n",
         "bq_dataset = bq_client.create_dataset(bq_dataset)\n",
@@ -463,11 +430,11 @@
       "source": [
         "### Create the Dataset\n",
         "\n",
-        "Use `TabularDataset.create()` to create a `TabularDataset` resource, which takes the following parameters:\n",
+        "Use `TabularDataset.create()` method to create a tabular dataset resource, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
-        "- `bq_source`: Alternatively, import data items from a BigQuery table into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `gcs_source`: A list of one or more dataset index files to import the data items into the dataset resource.\n",
+        "- `bq_source`: Alternatively, import data items from a BigQuery table into the dataset resource.\n",
         "\n",
         "This operation may take several minutes."
       ]
@@ -521,11 +488,11 @@
         "\n",
         "Create an AutoML training pipeline using the `AutoMLTabularTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
+        "- `display_name`: The human readable name for the training job resource.\n",
         "- `optimization_prediction_type`: The type task to train the model for.\n",
         "  - `classification`: A tabular classification model.\n",
         "  - `regression`: A tabular regression model.\n",
-        "- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
+        "- `column_transformations`: (Optional): Transformations to apply to the input columns.\n",
         "- `optimization_objective`: The optimization objective (minimize or maximize).\n",
         "  - binary classification:\n",
         "    - `minimize-log-loss`\n",
@@ -571,18 +538,18 @@
         "\n",
         "Run the training job by invoking the `run` method with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
+        "- `dataset`: The dataset resource to train the model.\n",
         "- `model_display_name`: The human readable name for the trained model.\n",
         "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
         "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
         "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
         "- `target_column`: The name of the column to train as the label.\n",
         "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
-        "- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
+        "- `disable_early_stopping`: By default, the model training stops early if the model performance doesn't improve. Setting `disable_early_stopping` = `True` overrides this behavior, allowing the model to train for the entire specified duration.\n",
         "\n",
-        "The `run` method when completed returns the `Model` resource.\n",
+        "The `run` method, upon completion, returns the model resource.\n",
         "\n",
-        "The execution of the training pipeline will take upto 3 hours."
+        "The execution of the training pipeline may take upto 3 hours."
       ]
     },
     {
@@ -612,7 +579,7 @@
       },
       "source": [
         "## Review model evaluation scores\n",
-        "After your model has finished training, you can review its evaluation scores."
+        "After model training is complete, you can review its evaluation scores."
       ]
     },
     {
@@ -660,7 +627,7 @@
       },
       "outputs": [],
       "source": [
-        "# Create results dataset in default region\n",
+        "# Create results dataset in default location\n",
         "RESULTS_DATASET_ID = \"gsod_results_unique\"\n",
         "bq_dataset = bigquery.Dataset(f\"{PROJECT_ID}.{RESULTS_DATASET_ID}\")\n",
         "bq_dataset = bq_client.create_dataset(bq_dataset)\n",
@@ -675,7 +642,7 @@
       "source": [
         "### Make the batch prediction request\n",
         "\n",
-        "You can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
+        "You can make a batch prediction by invoking the `batch_predict()` method, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
@@ -685,7 +652,7 @@
         "- `machine_type`: The type of machine to use for training.\n",
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete.\n",
+        "- `sync`: Set `True` to wait until the completion of the job.\n",
         "\n",
         "Batch prediction job takes roughly 1 hour to finish."
       ]
@@ -698,7 +665,7 @@
       },
       "outputs": [],
       "source": [
-        "# Note: The bigquery_source and bigquery_destination_prefix must be in the same region\n",
+        "# Note: The bigquery_source and bigquery_destination_prefix must be in the same location\n",
         "PREDICTION_RESULTS_DATASET_ID = f\"{PROJECT_ID}.{RESULTS_DATASET_ID}\"\n",
         "\n",
         "batch_predict_job = model.batch_predict(\n",
@@ -753,8 +720,7 @@
         "\n",
         "- Model\n",
         "- AutoML Training Job\n",
-        "- Batch Job\n",
-        "- Cloud Storage Bucket"
+        "- Batch Job"
       ]
     },
     {

notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb

@@ -23,6 +23,16 @@
         "# limitations under the License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "64a4f198313d"
+      },
+      "source": [
+        "Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis models by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text objectives until June 15, 2025. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see                                                            \n",
+        "[Introduction to tuning](https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-gemini-overview)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb

@@ -33,23 +33,28 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fautoml%2Fsdk_automl_video_action_recognition_batch.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>                 \n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -78,25 +83,25 @@
         "\n",
         "In this tutorial, you learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Google Cloud Vertex AI services and resources:\n",
         "\n",
-        "- Vertex AI Dataset\n",
-        "- Vertex AI Model\n",
-        "- Vertex AI Batch Prediction\n",
+        "- Vertex AI dataset\n",
+        "- Vertex AI model\n",
+        "- Vertex AI batch prediction\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI Dataset` resource.\n",
+        "- Create a Vertex AI dataset resource.\n",
         "- Train the model.\n",
         "- View the model evaluation.\n",
         "- Make a batch prediction.\n",
         "\n",
         "There is one key difference between using batch prediction and using online prediction:\n",
         "\n",
-        "* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.\n",
+        "* Prediction service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.\n",
         "\n",
-        "* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
+        "* Batch prediction service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
       ]
     },
     {
@@ -133,160 +138,122 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. \n"
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "dae013b807c7"
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                google-cloud-storage"
+        "                                 google-cloud-storage"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "294fe4e5a671"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -317,7 +284,7 @@
         "id": "autoset_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -328,7 +295,29 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a56633b047ee"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "673ef17c3cff"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -349,34 +338,10 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "import os\n",
         "\n",
-        "import google.cloud.aiplatform as aiplatform\n",
         "from google.cloud import storage"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "source": [
-        "## Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -442,12 +407,12 @@
         "id": "create_dataset:video,var"
       },
       "source": [
-        "### Create the Dataset\n",
+        "### Create the dataset\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `VideoDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource using the `create` method for the `VideoDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `gcs_source`: A list of one or more dataset index files to import the data items into the dataset resource.\n",
         "\n",
         "This operation may take several minutes."
       ]
@@ -485,7 +450,7 @@
         "\n",
         "An AutoML training pipeline is created with the `AutoMLVideoTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
+        "- `display_name`: The human readable name for the TrainingJob resource.\n",
         "- `prediction_type`: The type task to train the model for.\n",
         "    - `classification`: A video classification model.\n",
         "    - `object_tracking`: A video object tracking model.\n",
@@ -518,12 +483,12 @@
         "\n",
         "Next, you run the training job by invoking the method `run`, with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
+        "- `dataset`: The dataset resource to train the model.\n",
         "- `model_display_name`: The human readable name for the trained model.\n",
         "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
         "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
         "\n",
-        "The `run` method when completed returns the `Model` resource.\n",
+        "The `run` method when completed returns the model resource.\n",
         "\n",
         "The execution of the training pipeline can take upto 40 minutes."
       ]
@@ -802,7 +767,7 @@
         "\n",
         "# Delete Cloud Storage objects\n",
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/automl/sdk_automl_video_classification_batch.ipynb

@@ -32,20 +32,25 @@
         "# Vertex AI SDK: AutoML training video classification model for batch prediction\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fautoml%2Fsdk_automl_video_classification_batch.ipynb\"\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb\" target='_blank'>            \n",
-        "    <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> \n",
+        "    <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> \n",
         "Open in Vertex AI Workbench \n",
         "    </a>\n",
         "  </td>\n",
@@ -132,160 +137,121 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the latest versions of Vertex AI and Cloud Storage SDK for Python."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2abdd254e90f"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-storage"
+        "## Get started"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "a2c2cb2109a0"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "514a03ed1a82"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
-        "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform google-cloud-storage"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "## Before you begin\n",
+        "### Restart runtime (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "f09b4dff629a"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ee775571c2b5"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "294fe4e5a671"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -327,27 +293,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_vars"
-      },
-      "source": [
-        "### Import libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "import_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "from google.cloud import aiplatform"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -369,6 +315,8 @@
       },
       "outputs": [],
       "source": [
+        "from google.cloud import aiplatform\n",
+        "\n",
         "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
@@ -842,7 +790,7 @@
         "batch_predict_job.delete()\n",
         "\n",
         "# Delete the Cloud storage bucket\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb

@@ -32,23 +32,26 @@
         "# Get started with BigQuery ML Training\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "    <td>\n",
-        "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "        </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -117,7 +120,10 @@
         "- Cloud Storage\n",
         "- BigQuery\n",
         "\n",
-        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing) and\n",
+        "[BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -126,9 +132,8 @@
         "id": "install_mlops"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the following packages for executing this notebook."
+        "## Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -153,7 +158,8 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -164,11 +170,52 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4de1bd77992b"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "befa6ca14bc0"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7de6ef0fac42"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -177,108 +224,20 @@
         "id": "yfEglUHQk9S3"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "### Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "region"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -320,7 +279,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -404,7 +363,7 @@
       "source": [
         "### Initialize Vertex AI and BigQuery SDKs for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+        "Initialize the Vertex AI SDK for Python and BigQuery SDK  with your project and the created bucket."
       ]
     },
     {
@@ -448,13 +407,13 @@
         "\n",
         "You can set hardware accelerators for prediction.\n",
         "\n",
-        "Set the variable `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variable `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you would specify:\n",
         "\n",
-        "    (aiplatform.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
-        "Learn more [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your region."
+        "Learn more [about hardware accelerator support for your region.](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators)"
       ]
     },
     {
@@ -469,11 +428,11 @@
         "\n",
         "if os.getenv(\"IS_TESTING_DEPLOY_GPU\"):\n",
         "    DEPLOY_GPU, DEPLOY_NGPU = (\n",
-        "        aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
+        "        aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4,\n",
         "        int(os.getenv(\"IS_TESTING_DEPLOY_GPU\")),\n",
         "    )\n",
         "else:\n",
-        "    DEPLOY_GPU, DEPLOY_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)"
+        "    DEPLOY_GPU, DEPLOY_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 1)"
       ]
     },
     {
@@ -482,14 +441,14 @@
         "id": "container:prediction"
       },
       "source": [
-        "### Set pre-built containers\n",
+        "### Set prebuilt containers\n",
         "\n",
-        "Set the pre-built Docker container image for prediction.\n",
+        "Set the prebuilt Docker container image for prediction.\n",
         "\n",
-        "- Set the variable `TF` to the TensorFlow version of the container image. For example, `2-1` would be version 2.1, and `1-15` would be version 1.15. The following list shows some of the pre-built images available:\n",
+        "- Set the variable `TF` to the TensorFlow version of the container image. The following list shows some of the prebuilt images available:\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+        "For the latest list, see [prebuilt containers for prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers)."
       ]
     },
     {
@@ -517,7 +476,7 @@
         "        DEPLOY_VERSION = \"tf-cpu.{}\".format(TF)\n",
         "\n",
         "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        "    LOCATION.split(\"-\")[0], DEPLOY_VERSION\n",
         ")\n",
         "\n",
         "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU)"
@@ -540,7 +499,7 @@
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*"
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs*"
       ]
     },
     {
@@ -569,7 +528,7 @@
       "source": [
         "## BigQuery ML introduction\n",
         "\n",
-        "BigQuery ML (BQML) provides the capability to train ML tabular models, such as classification and regression, in BigQuery using SQL syntax.\n",
+        "BigQuery ML (BQML) provides the capability to train ML tabular models, such as classification and regression in BigQuery using SQL syntax.\n",
         "\n",
         "Learn more about [BigQuery ML documentation](https://cloud.google.com/bigquery-ml/docs)."
       ]
@@ -623,7 +582,7 @@
         "Next, you create and train a BigQuery ML tabular classification model from the public dataset penguins and store the model in your project using the `CREATE MODEL` statement. The model configuration is specified in the `OPTIONS` statement as follows:\n",
         "\n",
         "- `model_type`: The type and archictecture of tabular model to train, e.g., DNN classification.\n",
-        "- `labels`: The column which are the labels.\n",
+        "- `labels`: The column labels.\n",
         "\n",
         "Learn more about [The CREATE MODEL statement](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create)."
       ]
@@ -710,7 +669,7 @@
       "source": [
         "### Export the model from BigQuery ML\n",
         "\n",
-        "The model you trained in BigQuery ML is a TensorFlow model. Next, you export the TensorFlow model artifacts in TF.SavedModel format."
+        "The model you trained in BigQuery ML is a TensorFlow model. Next, export the TensorFlow model artifacts in TF.SavedModel format."
       ]
     },
     {
@@ -736,7 +695,7 @@
       "source": [
         "## Upload the BigQuery ML model to a Vertex AI Model resource\n",
         "\n",
-        "Finally, now that you have the BigQuery ML model exported, you upload the model artifacts to Vertex AI Model resource, in the same way as if you were uploading a custom trained model.\n",
+        "Finally, now that you have the BigQuery ML model exported, you upload the model artifacts to Vertex AI model resource, in the same way as if you were uploading a custom trained model.\n",
         "\n",
         "Below is a partial list of mapping BigQuery ML model types to their corresponding exported model format:\n",
         "\n",
@@ -782,12 +741,12 @@
       "source": [
         "## Deploy the model\n",
         "\n",
-        "Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method, with the following parameters:\n",
+        "Next, deploy your model for online prediction. To deploy the model, invoke the `deploy` method, with the following parameters:\n",
         "\n",
         "- `deployed_model_display_name`: A human readable name for the deployed model.\n",
         "- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
         "If only one model, then specify as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
-        "If there are existing models on the endpoint, for which the traffic needs to be split, then use model_id to specify as { \"0\": percent, model_id: percent, ... }, where model_id is the model id of an existing model to the deployed endpoint. The percents must add up to 100.\n",
+        "If there are existing models on the endpoint, for which the traffic needs to split, then use model_id to specify as { \"0\": percent, model_id: percent, ... }, where model_id is the model id of an existing model to the deployed endpoint. The percent must add up to 100.\n",
         "- `machine_type`: The type of machine to use for training.\n",
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
@@ -841,7 +800,7 @@
       "source": [
         "#### Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you are done doing predictions, you undeploy the model from the endpoint resource. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -864,9 +823,9 @@
         "id": "model_delete:mbsdk"
       },
       "source": [
-        "#### Delete the `Vertex AI Model` resource\n",
+        "#### Delete the Vertex AI model resource\n",
         "\n",
-        "The method 'delete()' deletes the model."
+        "The method `delete()` deletes the model."
       ]
     },
     {
@@ -889,9 +848,9 @@
         "id": "7890ae6f6410"
       },
       "source": [
-        "### Delete the `BigQuery ML` model\n",
+        "### Delete the BigQuery ML model\n",
         "\n",
-        "Next, delete the `BigQuery ML` instance of the model."
+        "Next, delete the BigQuery ML instance of the model."
       ]
     },
     {
@@ -1040,7 +999,7 @@
       "source": [
         "### Train a BigQuery ML model with Explainability\n",
         "\n",
-        "Next, you train the same BigQuery ML model, but this time you enable Vertex AI Explainability on the model predictions by adding the option:\n",
+        "Next, you train the same BigQuery ML model, this time you enable Vertex Explainable AI on the model predictions by adding the option:\n",
         "\n",
         "- `ENABLE_GLOBAL_EXPLAIN`"
       ]
@@ -1110,23 +1069,6 @@
         "job = bqclient.query(MODEL_QUERY)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2b4498ca6fea"
-      },
-      "source": [
-        "## Model Registry\n",
-        "\n",
-        "Alternatively, you can implicitly upload your BigQuery ML model as a Vertex AI model resource with exporting and importing the model artifacts. In this method, you add additional options when training the model that tells BigQuery ML to automatically upload and register the trained model as a model resource.\n",
-        "\n",
-        "### Setting permissions to automatically register the model\n",
-        "\n",
-        "You need to set some additional IAM permissions for BigQuery ML to automatically upload and register the model after training. Depending on your service account, the setting of the permissions below may fail. In this case, we recommend executing the permissions in a Cloud Shell.\n",
-        "\n",
-        "Learn more about [Setting permissions for Model Registry](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)\n"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1149,9 +1091,9 @@
         "\n",
         "Next, you train the model and automatically register the model to the Vertex AI Model Registry, by adding the following parameters as options:\n",
         "\n",
-        "- `model_registry`: Set to \"vertex_ai\" to indicate automatic registration to Vertex AI Model Registry.\n",
+        "- `model_registry`: Set to `vertex_ai` to indicate automatic registration to Vertex AI Model Registry.\n",
         "- `vertex_ai_model_id`: The human readable display name for the registered model.\n",
-        "- `vertex_ai_model_version_aliases`: Alternate names for the model."
+        "- `vertex_ai_model_version_aliases`: Alternate name for the model."
       ]
     },
     {
@@ -1201,9 +1143,9 @@
         "id": "5b4970272040"
       },
       "source": [
-        "### Find the model in the Vertex Model Registry\n",
+        "### Find the model in the Vertex AI Model Registry\n",
         "\n",
-        "Finally, you can use the Vertex AI Model `list()` method with a filter query to find the automatically registered model."
+        "Finally, you can use the Vertex AI model `list()` method with a filter query to find the automatically registered model."
       ]
     },
     {
@@ -1301,7 +1243,7 @@
         "! bq rm -r -f $PROJECT_ID:$BQ_DATASET_NAME\n",
         "\n",
         "delete_storage = False\n",
-        "if delete_storage or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_storage:\n",
         "    # Delete the created GCS bucket\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]

notebooks/official/datasets/get_started_bq_datasets.ipynb

@@ -32,22 +32,28 @@
         "# Get started with BigQuery datasets\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_bq_datasets.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
-        "    </a>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_bq_datasets.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fdatasets%2Fget_started_bq_datasets.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/datasets/get_started_bq_datasets.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_bq_datasets.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -63,7 +69,7 @@
         "\n",
         "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with BigQuery datasets.\n",
         "\n",
-        "Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro) and [Vertex AI for BigQuery users](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [BigQuery datasets](https://cloud.google.com/bigquery/docs/datasets-intro) and [Vertex AI for BigQuery users](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
       ]
     },
     {
@@ -74,22 +80,22 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `BigQuery` as a dataset for training with `Vertex AI`.\n",
+        "In this tutorial, you learn how to use BigQuery as a dataset for training with Vertex AI.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Datasets`\n",
-        "- `BigQuery Datasets`\n",
+        "- Vertex AI datasets\n",
+        "- BigQuery datasets\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a Vertex AI `Dataset` resource from `BigQuery` table -- compatible for `AutoML` training.\n",
-        "- Extract a copy of the dataset from `BigQuery` to a CSV file in Cloud Storage -- compatible for `AutoML` or custom training.\n",
-        "- Select rows from a `BigQuery` dataset into a `pandas` dataframe -- compatible for custom training.\n",
-        "- Select rows from a `BigQuery` dataset into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.\n",
-        "- Select rows from extracted CSV files into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.\n",
-        "- Create a `BigQuery` dataset from CSV files.\n",
-        "- Extract data from `BigQuery` table into a `DMatrix` -- compatible for custom training `XGBoost` models."
+        "- Create a Vertex AI dataset resource from BigQuery table -- compatible for AutoML training.\n",
+        "- Extract a copy of the dataset from BigQuery to a CSV file in Cloud Storage -- compatible for AutoML or custom training.\n",
+        "- Select rows from a BigQuery dataset into a pandas dataframe -- compatible for custom training.\n",
+        "- Select rows from a BigQuery dataset into a `tf.data.Dataset` -- compatible for custom training TensorFlow models.\n",
+        "- Select rows from extracted CSV files into a `tf.data.Dataset` -- compatible for custom training TensorFlow models.\n",
+        "- Create a BigQuery dataset from CSV files.\n",
+        "- Extract data from BigQuery table into a DMatrix -- compatible for custom training XGBoost models."
       ]
     },
     {
@@ -103,7 +109,7 @@
         "When doing E2E MLOps on Google Cloud, following are the best practices when dealing with structured (tabular) data in BigQuery:\n",
         "\n",
         "- For AutoML training:\n",
-        "  - Create a managed dataset with Vertex AI `TabularDataset`.\n",
+        "  - Create a managed dataset with Vertex AI TabularDataset.\n",
         "  - Use the BigQuery table as the input to the dataset.\n",
         "  - Specify columns and columns transformations when running the AutoML training pipeline job.\n",
         "\n",
@@ -164,164 +170,134 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_mlops"
+        "id": "d1ea81ac77f0"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the following packages to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e5d353aa47ac"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "install_mlops"
+        "id": "9bbcbe73e685"
       },
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
         "                                 google-cloud-bigquery \\\n",
         "                                 tensorflow \\\n",
-        "                                 tensorflow-io==0.18 \\\n",
+        "                                 tensorflow-io \\\n",
         "                                 xgboost \\\n",
         "                                 numpy \\\n",
         "                                 pandas \\\n",
-        "                                 pyarrow"
+        "                                 pyarrow \\\n",
+        "                                 db-dtypes"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "c87a2a5d7e35"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5dccb1c8feb6"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c2fc3d7b6bfa"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "45769dc0c97d"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
+        "from google.cloud import aiplatform\n",
         "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -352,7 +328,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -363,7 +339,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
+        "! gsutil mb -l $LOCATION $BUCKET_URI"
       ]
     },
     {
@@ -383,34 +359,11 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aiplatform\n",
         "import pandas as pd\n",
         "import xgboost as xgb\n",
         "from google.cloud import bigquery"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,region"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk,region"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -462,14 +415,14 @@
         "id": "create_dataset:tabular,bq,lrg"
       },
       "source": [
-        "### Create the Dataset\n",
+        "### Create the dataset\n",
         "\n",
         "#### BigQuery input data\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `bq_source`: Import data items from a BigQuery table into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `bq_source`: Import data items from a BigQuery table into the dataset resource.\n",
         "- `labels`: User defined metadata. In this example, you store the location of the Cloud Storage bucket containing the user defined data.\n",
         "\n",
         "Learn more about [TabularDataset from BigQuery table](https://cloud.google.com/vertex-ai/docs/datasets/create-dataset-api#aiplatform_create_dataset_tabular_bigquery_sample-python)."
@@ -535,14 +488,14 @@
         "id": "create_dataset:tabular,lrg"
       },
       "source": [
-        "### Create the Dataset\n",
+        "### Create the dataset\n",
         "\n",
         "#### CSV input data\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `gcs_source`: A list of one or more dataset index files to import the data items into the dataset resource.\n",
         "- `labels`: User defined metadata. In this example, you store the location of the Cloud Storage bucket containing the user defined data.\n",
         "\n",
         "Learn more about [TabularDataset from CSV files](https://cloud.google.com/vertex-ai/docs/datasets/create-dataset-api#aiplatform_create_dataset_tabular_gcs_sample-python)"
@@ -1045,9 +998,9 @@
         "\n",
         "Otherwise, you can delete the individual resources you created in this tutorial:\n",
         "\n",
-        "- Vertex AI Dataset resource\n",
+        "- Vertex AI dataset resource\n",
         "- Cloud Storage Bucket\n",
-        "- BigQuery Dataset\n",
+        "- BigQuery dataset\n",
         "\n",
         "Set `delete_storage` to _True_ to delete the storage resources used in this notebook."
       ]

notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb

@@ -33,22 +33,27 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fexperiments%2Fbuild_model_experimentation_lineage_with_prebuild_code.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
         "  </td>                                                                                               \n",
         "</table>\n"
       ]
@@ -135,12 +140,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "f0316df526f8"
       },
       "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Install additional package dependencies not installed in your notebook environment."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -152,141 +164,98 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet joblib fsspec gcsfs scikit-learn \n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform==1.35 "
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "294fe4e5a671"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -317,7 +286,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -328,7 +297,40 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "70bb458c7580"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "906e31206b45"
+      },
+      "outputs": [],
+      "source": [
+        "import uuid\n",
+        "\n",
+        "from google.cloud import aiplatform as vertex_ai\n",
+        "\n",
+        "# Experiments\n",
+        "TASK = \"classification\"\n",
+        "MODEL_TYPE = \"naivebayes\"\n",
+        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{uuid.uuid1()}\"\n",
+        "EXPERIMENT_RUN_NAME = \"run-1\"\n",
+        "\n",
+        "\n",
+        "vertex_ai.init(\n",
+        "    project=PROJECT_ID, experiment=EXPERIMENT_NAME, staging_bucket=BUCKET_URI\n",
+        ")"
       ]
     },
     {
@@ -404,13 +406,9 @@
         "import collections\n",
         "import tempfile\n",
         "import time\n",
-        "import uuid\n",
         "from json import dumps\n",
         "\n",
-        "collections.Iterable = collections.abc.Iterable\n",
-        "\n",
-        "# Vertex AI\n",
-        "from google.cloud import aiplatform as vertex_ai"
+        "collections.abc.Iterable = collections.abc.Iterable"
       ]
     },
     {
@@ -436,11 +434,6 @@
         "DATASET_NAME = \"news_corpora\"\n",
         "DATASET_URI = f\"{BUCKET_URI}/{DATA_PATH}/raw/newsCorpora.csv\"\n",
         "\n",
-        "# Experiments\n",
-        "TASK = \"classification\"\n",
-        "MODEL_TYPE = \"naivebayes\"\n",
-        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{uuid.uuid1()}\"\n",
-        "EXPERIMENT_RUN_NAME = \"run-1\"\n",
         "\n",
         "# Preprocessing\n",
         "PREPROCESSED_DATASET_NAME = f\"preprocessed_{DATASET_NAME}\"\n",
@@ -472,30 +465,6 @@
         "MODEL_NAME = f\"{EXPERIMENT_NAME}-model\""
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "inR70nh38PeK"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nz0nasrh8T3c"
-      },
-      "outputs": [],
-      "source": [
-        "vertex_ai.init(\n",
-        "    project=PROJECT_ID, experiment=EXPERIMENT_NAME, staging_bucket=BUCKET_URI\n",
-        ")"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/official/experiments/get_started_with_custom_training_autologging_local_script.ipynb

@@ -90,7 +90,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to autolog paramenters and metrics of an ML experiment running on Vertex AI Training by leveraging the integration with Vertex AI Experiments.\n",
+        "In this tutorial, you learn how to autolog parameters and metrics of an ML experiment running on Vertex AI Training by leveraging the integration with Vertex AI Experiments.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",

notebooks/official/explainable_ai/xai_image_classification_feature_attributions.ipynb

@@ -38,7 +38,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fofficial%2Fexplainable_ai%2fxai_image_classification_feature_attributions.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fexplainable_ai%2fxai_image_classification_feature_attributions.ipynb\">\n",
         "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>    \n",

notebooks/official/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb

@@ -0,0 +1,598 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur8xi4C7S06n"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAPoU8Sm5E6e"
+      },
+      "source": [
+        "## Fetch historical feature values\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Foffline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this tutorial, you will learn how to use the Vertex AI SDK for Python to retrieve historical values from the feature data source in BigQuery.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "* Vertex AI Feature Store\n",
+        "* BigQuery\n",
+        "\n",
+        "The steps performed include the following:\n",
+        "\n",
+        "* Setup BigQuery data\n",
+        "* Setup Feature Registry\n",
+        "* Fetch historical feature values from feature data source in BigQuery\n",
+        "* Clean up"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "19cf444ebb99"
+      },
+      "source": [
+        "### Objective"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61RBz8LLbxCR"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tFy3H3aPgx12"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform bigframes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5Xep4W9lq-Z"
+      },
+      "source": [
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XRvKdaPDTznN"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SbmM4z7FOBpM"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "\n",
+        "import vertexai\n",
+        "\n",
+        "vertexai.init(project=PROJECT_ID, location=LOCATION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33067053f38b"
+      },
+      "source": [
+        "### Imports and IDs\n",
+        "\n",
+        "Import the packages required to use the`fetch_historical_feature_values()`\n",
+        "function in the Vertex AI SDK for Python."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c8abe818393b"
+      },
+      "outputs": [],
+      "source": [
+        "import bigframes\n",
+        "import bigframes.pandas\n",
+        "import pandas as pd\n",
+        "from google.cloud import bigquery\n",
+        "from vertexai.resources.preview.feature_store import (Feature, FeatureGroup,\n",
+        "                                                      offline_store)\n",
+        "from vertexai.resources.preview.feature_store import utils as fs_utils"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4d0295f5d524"
+      },
+      "source": [
+        "The following variables set BigQuery and Feature Group resources that will be\n",
+        "used or created. If you'd like to use your own data source (CSV), please adjust\n",
+        "`DATA_SOURCE`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ac036ecfbc32"
+      },
+      "outputs": [],
+      "source": [
+        "BQ_DATASET_ID = \"fhfv_dataset_unique\"  # @param {type:\"string\"}\n",
+        "BQ_TABLE_ID = \"fhfv_table_unique\"  # @param {type:\"string\"}\n",
+        "BQ_TABLE_URI = f\"{PROJECT_ID}.{BQ_DATASET_ID}.{BQ_TABLE_ID}\"\n",
+        "\n",
+        "FEATURE_GROUP_ID = \"fhfv_fg_unique\"  # @param {type:\"string\"}\n",
+        "\n",
+        "DATA_SOURCE = \"gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cd580a0679ce"
+      },
+      "source": [
+        "## Create BigQuery table containing feature data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1e2c688b844b"
+      },
+      "source": [
+        "First we'll use BigQuery DataFrames to load in our CSV data source. Then we'll\n",
+        "rename the `timestamp` column to `feature_timestamp` to support usage as a\n",
+        "BigQuery source in Feature Registry."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1ff4481243a8"
+      },
+      "outputs": [],
+      "source": [
+        "session = bigframes.connect(\n",
+        "    bigframes.BigQueryOptions(\n",
+        "        project=PROJECT_ID,\n",
+        "        location=LOCATION,\n",
+        "    )\n",
+        ")\n",
+        "df = session.read_csv(DATA_SOURCE)\n",
+        "df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"], utc=True)\n",
+        "df = df.rename(columns={\"timestamp\": \"feature_timestamp\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "de5008f71567"
+      },
+      "source": [
+        "Let's preview the data we'll write to the table."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "38b448c47657"
+      },
+      "outputs": [],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "967ec2a0193f"
+      },
+      "source": [
+        "And finally we'll write the DataFrame to the target BigQuery table."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4c11b88ab55d"
+      },
+      "outputs": [],
+      "source": [
+        "df.to_gbq(BQ_TABLE_URI, if_exists=\"replace\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "818a36b9da86"
+      },
+      "source": [
+        "## Create feature registry resources"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2fd96d0d8628"
+      },
+      "source": [
+        "Create a feature group backed by the BigQuery table created above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1f915ddd4669"
+      },
+      "outputs": [],
+      "source": [
+        "fg: FeatureGroup = FeatureGroup.create(\n",
+        "    f\"{FEATURE_GROUP_ID}\",\n",
+        "    fs_utils.FeatureGroupBigQuerySource(\n",
+        "        uri=f\"bq://{BQ_TABLE_URI}\", entity_id_columns=[\"users\"]\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ebb9179572f7"
+      },
+      "source": [
+        "Create the `movies` feature which corresponds to the `movies` column in the\n",
+        "recently created BigQuery table."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0dba1c02883c"
+      },
+      "outputs": [],
+      "source": [
+        "movies_feature: Feature = fg.create_feature(\"movies\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c71964219962"
+      },
+      "source": [
+        "## Fetch historical feature values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8eb9b5257006"
+      },
+      "source": [
+        "### Fetch historical feature values for an entity"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "31edf36830b7"
+      },
+      "source": [
+        "The following will fetch historical feature values for the same entity (`alice`)\n",
+        "at two different timestamps. We expect the values of the `movies` feature at\n",
+        "each of those timestamps."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8fb9774db748"
+      },
+      "outputs": [],
+      "source": [
+        "entity_df = pd.DataFrame(\n",
+        "    data={\n",
+        "        \"users\": [\"alice\", \"alice\"],\n",
+        "        \"timestamp\": [\n",
+        "            pd.Timestamp(\"2021-09-14T09:36\"),\n",
+        "            pd.Timestamp(\"2023-12-12T13:13\"),\n",
+        "        ],\n",
+        "    },\n",
+        ")\n",
+        "\n",
+        "offline_store.fetch_historical_feature_values(\n",
+        "    entity_df=entity_df,\n",
+        "    features=[movies_feature],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f9ecae3005df"
+      },
+      "source": [
+        "### Fetch with multiple entities"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f463b68f2f4f"
+      },
+      "source": [
+        "The following will fetch historical feature values for two different entities\n",
+        "at different timestamps. We expect the values of the `movies` feature for each\n",
+        "entity at it's corresponding timestamp."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "80ab288afd22"
+      },
+      "outputs": [],
+      "source": [
+        "entity_df = pd.DataFrame(\n",
+        "    data={\n",
+        "        \"users\": [\"alice\", \"bob\"],\n",
+        "        \"timestamp\": [\n",
+        "            pd.Timestamp(\"2021-09-14T09:36\"),\n",
+        "            pd.Timestamp(\"2023-12-12T13:13\"),\n",
+        "        ],\n",
+        "    },\n",
+        ")\n",
+        "\n",
+        "offline_store.fetch_historical_feature_values(\n",
+        "    entity_df=entity_df,\n",
+        "    features=[movies_feature],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2a4e033321ad"
+      },
+      "source": [
+        "## Cleaning up"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ffd3dc65a25f"
+      },
+      "source": [
+        "### Delete feature and feature group"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7517048d8510"
+      },
+      "outputs": [],
+      "source": [
+        "movies_feature.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8488287340ca"
+      },
+      "outputs": [],
+      "source": [
+        "fg.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "753e85f60d06"
+      },
+      "source": [
+        "### Delete BigQuery dataset and table"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "99e984fe9c53"
+      },
+      "outputs": [],
+      "source": [
+        "client = bigquery.Client()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6cc5fdf51c9e"
+      },
+      "outputs": [],
+      "source": [
+        "client.delete_table(f\"{BQ_TABLE_URI}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4bac93a9ffcb"
+      },
+      "outputs": [],
+      "source": [
+        "client.delete_dataset(f\"{PROJECT_ID}.{BQ_DATASET_ID}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "offline_feature_serving_from_bigquery_with_feature_registry.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store_bigtable.ipynb

@@ -29,29 +29,31 @@
         "id": "rbTKH4_6f1ux"
       },
       "source": [
-        "## Online feature serving and fetching of BigQuery data with Vertex AI Feature Store\n",
+        "# Online feature serving and fetching of BigQuery data with Vertex AI Feature Store\n",
         "\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store_bigtable.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Fonline_feature_serving_and_fetching_bigquery_data_with_feature_store_bigtable.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store_bigtable.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store_bigtable.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -130,23 +132,29 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yTJiDCrYsOmT"
+        "id": "3b1ffd5ab768"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aae9ca040eab"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "GAqbE5Z2sTVM"
+        "id": "feec187f8410"
       },
       "outputs": [],
       "source": [
-        "# Install the packages\n",
         "! pip3 install --upgrade --quiet google-cloud-aiplatform\\\n",
         "                                 google-cloud-bigquery\\\n",
         "                                 db-dtypes"
@@ -155,180 +163,98 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "np60_uuCs7X5"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "_u0aEgaSs-3v"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# # Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "mdqw6ADTtJRI"
+        "id": "54c5ef8a8f43"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "bFANidV0tPbo"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "t5cutPRQtQ7m"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "107c51893a64"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "36840c73a5e4"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lfY8yWnbtZ0K"
-      },
-      "source": [
-        "#### Region\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations). Note that the new API is currently only available in the following regions:\n",
-        "* `us-centra1`\n",
-        "* `us-east1`\n",
-        "* `us-east4`\n",
-        "* `us-west1`\n",
-        "* `us-west2`\n",
-        "* `us-west3`\n",
-        "* `europe-west2`\n",
-        "* `europe-west3`\n",
-        "* `europe-west4`\n",
-        "* `europe-west8`\n",
-        "* `asia-southeast1`\n",
-        "* `asia-northeast2`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "G6iMMALZthFM"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-east1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ni5jx6RGtzG3"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
+        "from google.cloud import aiplatform\n",
         "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UzsMphY2t4-v"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R_OnJm_Yt8bw"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "x4ybIfzhuAOc"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gmnRqX6BuBnx"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uN9JoC1buE9P"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YNAvMVJjuH5b"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)\n",
+        "API_ENDPOINT = f\"{LOCATION}-aiplatform.googleapis.com\""
       ]
     },
     {
@@ -348,7 +274,7 @@
       },
       "outputs": [],
       "source": [
-        "from google.cloud import aiplatform, bigquery\n",
+        "from google.cloud import bigquery\n",
         "from google.cloud.aiplatform_v1 import (FeatureOnlineStoreAdminServiceClient,\n",
         "                                        FeatureOnlineStoreServiceClient,\n",
         "                                        FeatureRegistryServiceClient)\n",
@@ -369,30 +295,6 @@
         "from google.cloud.aiplatform_v1.types import io as io_pb2"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6ooJNSOvu6Q5"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "mLTm3pquu9ar"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)\n",
-        "\n",
-        "API_ENDPOINT = f\"{REGION}-aiplatform.googleapis.com\""
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -499,7 +401,7 @@
       },
       "outputs": [],
       "source": [
-        "# First, create a dataset if it does not already exist. The source data for this demo is located in the US region, so the dataset must also be located in the US region.\n",
+        "# First, create a dataset if it doesn't already exist. The source data for this demo is located in the US region, so the dataset must also be located in the US region.\n",
         "\n",
         "BQ_DATASET_ID = \"featurestore_demo\"  # @param {type:\"string\"}\n",
         "dataset = bigquery.Dataset(f\"{PROJECT_ID}.{BQ_DATASET_ID}\")\n",
@@ -553,7 +455,7 @@
         "    * Create a `FeatureOnlineStore` instance with autoscaling.\n",
         "1. Define the data (`FeatureView`) to be served by the newly-created instance. This can either map to\n",
         "    * The BigQuery view that you just created for serving data.\n",
-        "    * The `FeatureGroup` and `Feature` we will create to host feature metadata.\n",
+        "    * The `FeatureGroup` and `Feature` you'll create to host feature metadata.\n",
         "\n",
         "Bigtable serving latency is affected by the (Bigtable) load. However, when Bigtable is not overloaded, benchmarks show that the expected server-side latency is around 30 ms, measured at around 100 qps. The client-side latency is expected to be more than 5 ms higher than the server-side latency."
       ]
@@ -615,7 +517,7 @@
         "\n",
         "create_store_lro = admin_client.create_feature_online_store(\n",
         "    feature_online_store_admin_service_pb2.CreateFeatureOnlineStoreRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}\",\n",
         "        feature_online_store_id=FEATURE_ONLINE_STORE_ID,\n",
         "        feature_online_store=online_store_config,\n",
         "    )\n",
@@ -666,7 +568,7 @@
       "source": [
         "# Use list to verify the store is created.\n",
         "admin_client.list_feature_online_stores(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}\"\n",
         ")"
       ]
     },
@@ -701,7 +603,7 @@
       "source": [
         "##### Data source preparation guidelines for BigQuery data source\n",
         "\n",
-        "If the feature data for online serving is not already available in a BigQuery table or view, you can create a BigQuery dataset and table, and then load the data into it.\n",
+        "If the feature data for online serving isn't already available in a BigQuery table or view, you can create a BigQuery dataset and table, and then load the data into it.\n",
         "\n",
         "Note that if you choose to use BigQuery source, Feature Store only provides the option to snapshot and sync the entire BigQuery table or view for online serving. You need to construct this table or view to reflect the latest data to be served. Therefore, a timestamp column is not needed, since timestamps aren't used to differentiate the feature values.\n",
         "\n",
@@ -736,7 +638,7 @@
         "\n",
         "create_view_lro = admin_client.create_feature_view(\n",
         "    feature_online_store_admin_service_pb2.CreateFeatureViewRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
         "        feature_view_id=FEATURE_VIEW_ID,\n",
         "        feature_view=feature_view_pb2.FeatureView(\n",
         "            big_query_source=big_query_source,\n",
@@ -768,7 +670,7 @@
       "source": [
         "#### [Optional] Create FeatureGroup/Features\n",
         "\n",
-        "Create a FeatureGroup pointing to the created BigQuery view for the demo. We will then create features for each column we would like to register.\n"
+        "Create a FeatureGroup pointing to the created BigQuery view for the demo. You then create features for each column you'd like to register.\n"
       ]
     },
     {
@@ -779,7 +681,7 @@
       "source": [
         "##### Data source preparation guidelines for Feature Registry data source\n",
         "\n",
-        "Note that if you choose to use Feature Registry source, Feature Store only provides the option to support time-series sources for which Feature Store will generate latest featureValues.\n",
+        "Note that if you choose to use Feature Registry source, Feature Store only provides the option to support time-series sources for which Feature Store generates latest featureValues.\n",
         "\n",
         "Use the following guidelines to understand the schema and constraints while creating the BigQuery source:\n",
         "\n",
@@ -829,7 +731,7 @@
         "\n",
         "create_group_lro = registry_client.create_feature_group(\n",
         "    feature_registry_service_pb2.CreateFeatureGroupRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}\",\n",
         "        feature_group_id=FEATURE_GROUP_ID,\n",
         "        feature_group=feature_group_config,\n",
         "    )\n",
@@ -869,7 +771,7 @@
         "    create_feature_lros.append(\n",
         "        registry_client.create_feature(\n",
         "            featurestore_service_pb2.CreateFeatureRequest(\n",
-        "                parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureGroups/{FEATURE_GROUP_ID}\",\n",
+        "                parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureGroups/{FEATURE_GROUP_ID}\",\n",
         "                feature_id=id,\n",
         "                feature=feature_pb2.Feature(),\n",
         "            )\n",
@@ -921,7 +823,7 @@
         "\n",
         "create_view_lro = admin_client.create_feature_view(\n",
         "    feature_online_store_admin_service_pb2.CreateFeatureViewRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
         "        feature_view_id=FEATURE_VIEW_ID,\n",
         "        feature_view=feature_view_pb2.FeatureView(\n",
         "            feature_registry_source=feature_registry_source,\n",
@@ -953,7 +855,7 @@
       "source": [
         "# Again, list all feature view under the FEATURE_ONLINE_STORE_ID to confirm\n",
         "admin_client.list_feature_views(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
         ")"
       ]
     },
@@ -989,7 +891,7 @@
       "outputs": [],
       "source": [
         "sync_response = admin_client.sync_feature_view(\n",
-        "    feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")"
       ]
     },
@@ -1045,7 +947,7 @@
       "outputs": [],
       "source": [
         "admin_client.list_feature_view_syncs(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")"
       ]
     },
@@ -1092,7 +994,7 @@
       "source": [
         "data_client.fetch_feature_values(\n",
         "    request=feature_online_store_service_pb2.FetchFeatureValuesRequest(\n",
-        "        feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
+        "        feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
         "        data_key=feature_online_store_service_pb2.FeatureViewDataKey(key=\"28098\"),\n",
         "    )\n",
         ")"
@@ -1117,7 +1019,7 @@
       "source": [
         "data_client.fetch_feature_values(\n",
         "    request=feature_online_store_service_pb2.FetchFeatureValuesRequest(\n",
-        "        feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
+        "        feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
         "        data_key=feature_online_store_service_pb2.FeatureViewDataKey(key=\"28098\"),\n",
         "        data_format=feature_online_store_service_pb2.FeatureViewDataFormat.PROTO_STRUCT,\n",
         "    )\n",
@@ -1149,22 +1051,22 @@
         "# Delete Features\n",
         "for id in FEATURE_IDS:\n",
         "    registry_client.delete_feature(\n",
-        "        name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureGroups/{FEATURE_GROUP_ID}/features/{id}\"\n",
+        "        name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureGroups/{FEATURE_GROUP_ID}/features/{id}\"\n",
         "    )\n",
         "\n",
         "# Delete Featuregroup\n",
         "registry_client.delete_feature_group(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureGroups/{FEATURE_GROUP_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureGroups/{FEATURE_GROUP_ID}\"\n",
         ")\n",
         "\n",
         "# Delete FeatureViews\n",
         "admin_client.delete_feature_view(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")\n",
         "\n",
         "# Delete OnlineStore\n",
         "admin_client.delete_feature_online_store(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
         "    force=True,\n",
         ")\n",
         "\n",

notebooks/official/feature_store/online_feature_serving_and_vector_retrieval_bigquery_data_with_feature_store.ipynb

@@ -41,7 +41,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Fonline_feature_serving_and_vector_retrieval_bigquery_data_with_feature_store.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Fonline_feature_serving_and_vector_retrieval_bigquery_data_with_feature_store.ipynb\">\n",
         "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>  \n",

notebooks/official/feature_store/vertex_ai_feature_store_based_llm_grounding_tutorial.ipynb

@@ -29,27 +29,32 @@
         "id": "awoLZ5dc5bcG"
       },
       "source": [
-        "# Vertex AI Feature Store Based LLM Grounding Tutorial\n",
+        "# Vertex AI Feature Store Based LLM Grounding tutorial\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/vertex_ai_feature_store_based_llm_grounding_tutorial.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/vertex_ai_feature_store_based_llm_grounding_tutorial.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Fvertex_ai_feature_store_based_llm_grounding_tutorial.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/vertex_ai_feature_store_based_llm_grounding_tutorial.ipynb.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/vertex_ai_feature_store_based_llm_grounding_tutorial.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -76,7 +81,7 @@
         "\n",
         "In this tutorial, you learn how to create and use an online feature store instance to host and serve data in BigQuery with Vertex AI Feature Store in an end to end workflow of features serving and vector retrieval user journey.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Google Vertex AI services and resources:\n",
         "\n",
         "- Vertex AI Feature Store\n",
         "\n",
@@ -128,15 +133,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ea81ac77f0"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "yTJiDCrYsOmT"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to run this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -161,7 +173,9 @@
         "id": "np60_uuCs7X5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -172,46 +186,62 @@
       },
       "outputs": [],
       "source": [
-        "# # Automatically restart the kernel after installation so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "mdqw6ADTtJRI"
+        "id": "01e1cc61b578"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you're running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "bFANidV0tPbo"
+        "id": "ff666ce4051c"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "60763ee24ce0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -223,108 +253,14 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lfY8yWnbtZ0K"
-      },
-      "source": [
-        "#### Region\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations). Note that the new Feature Store capability showed in the colab is currently only available in the following regions:\n",
-        "* `us-central1`\n",
-        "* `us-east1`\n",
-        "* `us-west1`\n",
-        "* `europe-west4`\n",
-        "* `asia-southeast1`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "G6iMMALZthFM"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ni5jx6RGtzG3"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
+        "from google.cloud import aiplatform\n",
         "\n",
-        "Depending on your Jupyter environment, you might have to manually authenticate. Follow these instructions:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UzsMphY2t4-v"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R_OnJm_Yt8bw"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "x4ybIfzhuAOc"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gmnRqX6BuBnx"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uN9JoC1buE9P"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YNAvMVJjuH5b"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)\n",
+        "\n",
+        "API_ENDPOINT = f\"{LOCATION}-aiplatform.googleapis.com\""
       ]
     },
     {
@@ -346,7 +282,7 @@
       "source": [
         "import uuid\n",
         "\n",
-        "from google.cloud import aiplatform, bigquery\n",
+        "from google.cloud import bigquery\n",
         "from google.cloud.aiplatform_v1 import (FeatureOnlineStoreAdminServiceClient,\n",
         "                                        FeatureOnlineStoreServiceClient)\n",
         "from google.cloud.aiplatform_v1.types import NearestNeighborQuery\n",
@@ -356,32 +292,7 @@
         "    feature_online_store_admin_service as \\\n",
         "    feature_online_store_admin_service_pb2\n",
         "from google.cloud.aiplatform_v1.types import \\\n",
-        "    feature_online_store_service as feature_online_store_service_pb2\n",
-        "from google.cloud.aiplatform_v1.types import feature_view as feature_view_pb2"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6ooJNSOvu6Q5"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "mLTm3pquu9ar"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)\n",
-        "\n",
-        "API_ENDPOINT = f\"{REGION}-aiplatform.googleapis.com\""
+        "    feature_online_store_service as feature_online_store_service_pb2"
       ]
     },
     {
@@ -425,7 +336,7 @@
         "id": "_OV9dADJb63o"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket.\n"
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket.\n"
       ]
     },
     {
@@ -436,7 +347,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {GCS_BUCKET}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {GCS_BUCKET}"
       ]
     },
     {
@@ -504,7 +415,7 @@
         "\n",
         "BQ_DATASET_ID = \"fs_grounding\"  # @param {type:\"string\"}\n",
         "dataset = bigquery.Dataset(f\"{PROJECT_ID}.{BQ_DATASET_ID}\")\n",
-        "dataset.location = REGION\n",
+        "dataset.location = LOCATION\n",
         "dataset = bq_client.create_dataset(\n",
         "    dataset, exists_ok=True, timeout=30\n",
         ")  # Make an API request.\n",
@@ -538,7 +449,7 @@
         "\n",
         "PARAMS = {\n",
         "    \"project\": PROJECT_ID,\n",
-        "    \"location\": REGION,\n",
+        "    \"location\": LOCATION,\n",
         "    \"bigquery_bp_input_uri\": BIGQUERY_BP_INPUT_URI,\n",
         "    \"bigquery_bp_output_uri\": BIGQUERY_BP_OUTPUT_URI,\n",
         "    \"input_text_gcs_dir\": INPUT_TEXT_GCS_DIR,\n",
@@ -582,7 +493,7 @@
         "    parameters=PARAMS,\n",
         "    project=PROJECT_ID,\n",
         "    pipeline_root=f\"{GCS_BUCKET}/fs_based/pipeline_root\",\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         ")\n",
         "job.wait()"
       ]
@@ -611,7 +522,7 @@
         "\n",
         "    Args:\n",
         "      bigquery_bp_input_uri: The URI to a bigquery table as the input for the\n",
-        "        batch prediction component. The chunking component will populate data to\n",
+        "        batch prediction component. The chunking component populates data to\n",
         "        this uri first before batch prediction.\n",
         "      bigquery_bp_output_uri: The URI to a bigquery table as the output for the\n",
         "        batch prediction component.\n",
@@ -711,7 +622,7 @@
         "\n",
         "create_store_lro = admin_client.create_feature_online_store(\n",
         "    feature_online_store_admin_service_pb2.CreateFeatureOnlineStoreRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}\",\n",
         "        feature_online_store_id=FEATURE_ONLINE_STORE_ID,\n",
         "        feature_online_store=online_store_config,\n",
         "    )\n",
@@ -762,7 +673,7 @@
       "source": [
         "# Use get to verify the store is created.\n",
         "admin_client.get_feature_online_store(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
         ")"
       ]
     },
@@ -785,7 +696,7 @@
       "source": [
         "# Use list to verify the store is created.\n",
         "admin_client.list_feature_online_stores(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}\"\n",
         ")"
       ]
     },
@@ -802,7 +713,7 @@
         "* A data source (BigQuery table or view URI or `FeatureGroup/features`) synced to the `FeatureOnlineStore` instance for serving.\n",
         "* The [cron](https://en.wikipedia.org/wiki/Cron) schedule to run the sync pipeline.\n",
         "\n",
-        "During feature view creation, a sync job will be scheduled, and either started immediately or following the cron schedule. In the sync job, data is exported, a index is built and deployed to GKE cluster."
+        "During feature view creation, a sync job is scheduled, and either started immediately or following the cron schedule. In the sync job, data is exported, an index is built and deployed to GKE cluster."
       ]
     },
     {
@@ -814,7 +725,7 @@
       "outputs": [],
       "source": [
         "FEATURE_VIEW_ID = \"fs_grounding_test_new\"  # @param {type: \"string\"}\n",
-        "# A schedule will be created based on cron setting.\n",
+        "# A schedule is created based on cron setting.\n",
         "# If cron is unspecified, a sync job is started immediately.\n",
         "CRON_SCHEDULE = \"TZ=America/Los_Angeles 00 13 11 8 *\"  # @param {type: \"string\"}"
       ]
@@ -857,6 +768,8 @@
       },
       "outputs": [],
       "source": [
+        "from google.cloud.aiplatform_v1.types import feature_view as feature_view_pb2\n",
+        "\n",
         "big_query_source = feature_view_pb2.FeatureView.BigQuerySource(\n",
         "    uri=DATA_SOURCE, entity_id_columns=[\"vertex_generated_chunk_id\"]\n",
         ")\n",
@@ -875,7 +788,7 @@
         "\n",
         "create_view_lro = admin_client.create_feature_view(\n",
         "    feature_online_store_admin_service_pb2.CreateFeatureViewRequest(\n",
-        "        parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
+        "        parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
         "        feature_view_id=FEATURE_VIEW_ID,\n",
         "        feature_view=feature_view_pb2.FeatureView(\n",
         "            big_query_source=big_query_source,\n",
@@ -926,7 +839,7 @@
       "outputs": [],
       "source": [
         "admin_client.get_feature_view(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")"
       ]
     },
@@ -936,7 +849,7 @@
         "id": "sqpu4nHAO4pW"
       },
       "source": [
-        "Verify that the FeatureView instance is created by listing all the feature views within the online store."
+        "Verify that the `FeatureView` instance is created by listing all the feature views within the online store."
       ]
     },
     {
@@ -948,7 +861,7 @@
       "outputs": [],
       "source": [
         "admin_client.list_feature_views(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
         ")"
       ]
     },
@@ -962,7 +875,7 @@
       "source": [
         "# Optional: Delete feature views to avoid exceeding the deployed index nodes quota.\n",
         "# views = admin_client.list_feature_views(\n",
-        "#     parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
+        "#     parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
         "# )\n",
         "# for view in views:\n",
         "#     admin_client.delete_feature_view(name=view.name)"
@@ -990,7 +903,7 @@
       "outputs": [],
       "source": [
         "sync_response = admin_client.sync_feature_view(\n",
-        "    feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")"
       ]
     },
@@ -1048,7 +961,7 @@
       "outputs": [],
       "source": [
         "admin_client.list_feature_view_syncs(\n",
-        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    parent=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")"
       ]
     },
@@ -1075,7 +988,7 @@
       "source": [
         "# Verify online store creation.\n",
         "featore_online_store_instance = admin_client.get_feature_online_store(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\"\n",
         ")\n",
         "PUBLIC_ENDPOINT = (\n",
         "    featore_online_store_instance.dedicated_serving_endpoint.public_endpoint_domain_name\n",
@@ -1147,7 +1060,7 @@
         "# A vertex_generated_chunk_id for testing\n",
         "data_client.search_nearest_entities(\n",
         "    request=feature_online_store_service_pb2.SearchNearestEntitiesRequest(\n",
-        "        feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
+        "        feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
         "        query=NearestNeighborQuery(\n",
         "            entity_id=ENTITY_ID,\n",
         "            neighbor_count=5,\n",
@@ -1187,7 +1100,7 @@
       "source": [
         "data_client.search_nearest_entities(\n",
         "    request=feature_online_store_service_pb2.SearchNearestEntitiesRequest(\n",
-        "        feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
+        "        feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
         "        query=NearestNeighborQuery(\n",
         "            embedding=NearestNeighborQuery.Embedding(value=EMBEDDINGS),\n",
         "            neighbor_count=10,\n",
@@ -1216,7 +1129,7 @@
       "source": [
         "data_client.fetch_feature_values(\n",
         "    request=feature_online_store_service_pb2.FetchFeatureValuesRequest(\n",
-        "        feature_view=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
+        "        feature_view=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\",\n",
         "        data_key=feature_online_store_service_pb2.FeatureViewDataKey(key=ENTITY_ID),\n",
         "    )\n",
         ")"
@@ -1246,20 +1159,18 @@
       "source": [
         "# Delete Feature View\n",
         "admin_client.delete_feature_view(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}/featureViews/{FEATURE_VIEW_ID}\"\n",
         ")\n",
         "\n",
         "# Delete Feature Online Store\n",
         "admin_client.delete_feature_online_store(\n",
-        "    name=f\"projects/{PROJECT_ID}/locations/{REGION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
+        "    name=f\"projects/{PROJECT_ID}/locations/{LOCATION}/featureOnlineStores/{FEATURE_ONLINE_STORE_ID}\",\n",
         "    force=True,\n",
         ")\n",
         "\n",
         "# Delete Cloud Storage objects that were created\n",
-        "import os\n",
-        "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $GCS_BUCKET"
       ]
     }

notebooks/official/feature_store_legacy/feature_store_streaming_ingestion_sdk.ipynb

@@ -32,22 +32,24 @@
         "# Vertex AI Feature Store (Legacy): Streaming import SDK\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store_legacy/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store_legacy%2Ffeature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store_legacy/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store_legacy/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -128,15 +130,22 @@
         "to generate a cost estimate based on your projected usage.\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ea81ac77f0"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -159,60 +168,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "b96b39fd4d7b"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "ff666ce4051c"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8575d303471"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -224,103 +253,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "kljmKgilI_de"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74ccc9e52986"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -331,7 +264,7 @@
       "source": [
         "### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -395,7 +328,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -459,7 +392,7 @@
         "\n",
         "`{entity_id : {feature_id : feature_value}, ...},`\n",
         "\n",
-        "or a pandas `Dataframe`, where the `index` column holds the unique entity ID strings and each remaining column represents a feature.  In this notebook, since you use a pandas `DataFrame` for ingesting features we convert the index column data type to `string` to be used as `Entity ID`."
+        "or a pandas `Dataframe`, where the `index` column holds the unique entity ID strings and each remaining column represents a feature.  In this notebook, since you use a pandas `DataFrame` for ingesting features, convert the index column data type to `string` for your `Entity ID` usage."
       ]
     },
     {
@@ -484,7 +417,7 @@
       "source": [
         "# Remove null values\n",
         "NA_VALUES = [\"NA\", \".\"]\n",
-        "penguins_df = penguins_df.replace(to_replace=NA_VALUES, value=np.NaN).dropna()"
+        "penguins_df = penguins_df.replace(to_replace=NA_VALUES, value=np.nan).dropna()"
       ]
     },
     {
@@ -514,7 +447,7 @@
         "\n",
         "You create a featurestore using `aiplatform.Featurestore.create` with the following parameters:\n",
         "\n",
-        "* `featurestore_id (str)`: The ID to use for this featurestore, which will become the final component of the `featurestore` resource name. The value must be unique within the project and location.\n",
+        "* `featurestore_id (str)`: The ID to use for this featurestore, which becomes the final component of the `featurestore` resource name. The value must be unique within the project and location.\n",
         "* `online_store_fixed_node_count`: Configuration for online serving resources.\n",
         "* `project`: Project to create the `EntityType` in. If not set, project set in `aiplatform.init` is used.\n",
         "* `location`: Location to create the `EntityType` in. If not set, location set in `aiplatform.init` is used.\n",
@@ -535,7 +468,7 @@
         "    featurestore_id=FEATURESTORE_ID,\n",
         "    online_store_fixed_node_count=1,\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    sync=True,\n",
         ")"
       ]
@@ -561,7 +494,7 @@
         "fs = aiplatform.Featurestore(\n",
         "    featurestore_name=FEATURESTORE_ID,\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         ")\n",
         "print(fs.gca_resource)"
       ]
@@ -577,7 +510,7 @@
         "An entity type is a collection of semantically related features. You define your own entity types, based on the concepts that are relevant to your use case. For example, a movie service might have the entity types `movie` and `user`, which group related features that correspond to movies or users.\n",
         "\n",
         "Here, you create an entity type entity type named `penguin_entity_type` using `create_entity_type` with the following parameters:\n",
-        "* `entity_type_id (str)`: The ID to use for the `EntityType`, which will become the final component of the `EntityType` resource name. The value must be unique within a featurestore.\n",
+        "* `entity_type_id (str)`: The ID to use for the `EntityType`, which becomes the final component of the `EntityType` resource name. The value must be unique within a featurestore.\n",
         "* `description`: Description of the `EntityType`."
       ]
     },
@@ -670,7 +603,7 @@
         "id": "AKRXJCPijM8w"
       },
       "source": [
-        "You can create features either using `create_feature` or `batch_create_features`. Here, for convinience, you have added all feature configs in one variabel, so we use `batch_create_features`."
+        "You can create features either using `create_feature` or `batch_create_features`. Here, for convinience, you have added all feature configs in one variable, so you use `batch_create_features`."
       ]
     },
     {

notebooks/official/feature_store_legacy/sdk-feature-store-pandas.ipynb

@@ -32,23 +32,24 @@
         "# Using Vertex AI Feature Store (Legacy) with Pandas Dataframe\n",
         "\n",
         "<table align=\"left\">\n",
-        "    <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> \n",
-        "        Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "    \n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store%2Fsdk-feature-store-pandas.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -136,15 +137,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ea81ac77f0"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -168,60 +176,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "b96b39fd4d7b"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "21e3cac35e75"
+        "id": "ff666ce4051c"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8575d303471"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -233,89 +261,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -357,7 +303,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -393,7 +339,7 @@
       "source": [
         "### Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project and region."
+        "Initialize the Vertex AI SDK for Python for your project and location."
       ]
     },
     {
@@ -404,7 +350,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -497,7 +443,7 @@
         "\n",
         "Add the defined features to the entity types `users` and `movies` using the following methods.\n",
         "\n",
-        "### Add features using *`create_feature`* method\n",
+        "### Add features using `create_feature` method\n",
         "\n",
         "You provide the following parameters for creating features:\n",
         "\n",
@@ -542,7 +488,7 @@
         "id": "ecb141839033"
       },
       "source": [
-        "### Add features using *`batch_create_features`* method\n",
+        "### Add features using `batch_create_features` method\n",
         "\n",
         "You can also add multiple features at a time using a config map in a dictionary format. For this, you use the `batch_create_features` method. \n",
         "\n",
@@ -1179,8 +1125,8 @@
         "! rm {USERS_AVRO_FN} {MOVIES_AVRO_FN}\n",
         "\n",
         "# Delete Cloud Storage objects that were created\n",
-        "delete_bucket = True\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/feature_store_legacy/sdk-feature-store.ipynb

@@ -32,21 +32,24 @@
         "# Online and Batch predictions using Vertex AI Feature Store (Legacy)\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store_legacy/sdk-feature-store.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ffeature_store_legacy%2Fsdk-feature-store.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "    </td>\n",
-        "    <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/sdk-feature-store.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store_legacy/sdk-feature-store.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store_legacy/sdk-feature-store.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -122,15 +125,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "s3Jje0B5zglA"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -147,60 +157,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "GlWoVi7xz1TL"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "CFS6OPNWz3KZ"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "7RMhe6650CyB"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). {TODO: Update the APIs needed for your tutorial. Edit the API names, and update the link to append the API IDs, separating each one with a comma. For example, container.googleapis.com,cloudbuild.googleapis.com}\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "T7C_dgnR0L_l"
+        "id": "f82e28c631cc"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91842ef41bbd"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -212,103 +242,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ybtwdOp40TVK"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "oLUOopdB0UkU"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G_ZkpZnv0a0b"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rfsExLao0b49"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ovUeYbbM0nmK"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "l_AmeEXr0pE1"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fsl-OPfF0sUO"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "mOh0DLZP0vUI"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qML_uytf0ymm"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -330,7 +264,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = \"gs://your-bucket-name-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = \"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -350,7 +284,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -395,7 +329,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -466,7 +400,7 @@
         "    featurestore_id=FEATURESTORE_ID,\n",
         "    online_store_fixed_node_count=ONLINE_STORE_FIXED_NODE_COUNT,\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    sync=True,\n",
         ")"
       ]
@@ -491,7 +425,7 @@
         "fs = Featurestore(\n",
         "    featurestore_name=FEATURESTORE_ID,\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         ")\n",
         "print(fs.gca_resource)"
       ]
@@ -1150,7 +1084,7 @@
         "client = bigquery.Client(project=PROJECT_ID)\n",
         "dataset_id = \"{}.{}\".format(client.project, DESTINATION_DATA_SET)\n",
         "dataset = bigquery.Dataset(dataset_id)\n",
-        "dataset.location = REGION\n",
+        "dataset.location = LOCATION\n",
         "dataset = client.create_dataset(dataset)\n",
         "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
       ]
@@ -1236,7 +1170,7 @@
         "    featurestore_online_service as featurestore_online_service_pb2\n",
         "from google.cloud.aiplatform_v1beta1.types import types as types_pb2\n",
         "\n",
-        "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n",
+        "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(LOCATION)\n",
         "# Create client connection\n",
         "admin_client = FeaturestoreServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
         "data_client = FeaturestoreOnlineServingServiceClient(\n",
@@ -1255,7 +1189,7 @@
         "# Call `write_feature_values` to import data to `users` entity type.\n",
         "data_client.write_feature_values(\n",
         "    entity_type=admin_client.entity_type_path(\n",
-        "        PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
+        "        PROJECT_ID, LOCATION, FEATURESTORE_ID, \"users\"\n",
         "    ),\n",
         "    payloads=[\n",
         "        featurestore_online_service_pb2.WriteFeatureValuesPayload(\n",
@@ -1312,18 +1246,6 @@
         "You can also keep the project, but delete the featurestore and the BigQuery dataset by running the following code:"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NBTNfN8vxz4x"
-      },
-      "outputs": [],
-      "source": [
-        "# Delete Featurestore\n",
-        "fs.delete(force=True)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1332,13 +1254,21 @@
       },
       "outputs": [],
       "source": [
+        "# Delete Featurestore\n",
+        "fs.delete(force=True)\n",
+        "\n",
         "# Delete BigQuery dataset\n",
         "client = bigquery.Client(project=PROJECT_ID)\n",
         "client.delete_dataset(\n",
         "    DESTINATION_DATA_SET, delete_contents=True, not_found_ok=True\n",
         ")  # Make an API request.\n",
         "\n",
-        "print(\"Deleted dataset '{}'.\".format(DESTINATION_DATA_SET))"
+        "print(\"Deleted dataset '{}'.\".format(DESTINATION_DATA_SET))\n",
+        "\n",
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/generative_ai/distillation.ipynb

@@ -29,41 +29,32 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Vertex AI Distill a model\n",
+        "# Vertex AI: Distill a large language model\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/distillation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/distillation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fgenerative_ai%2Fdistillation.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/generative_ai/distillation.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/distillation.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "24743cf4a1e1"
-      },
-      "source": [
-        "**_NOTE_**: This notebook has been tested in the following environment:\n",
-        "\n",
-        "* Python version = 3.9"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -72,11 +63,15 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use the Distilling Step by Step on the Vertex AI.\n",
+        "This tutorial demonstrates how to use the distilling Step by Step on the Vertex AI.\n",
         "\n",
-        "We developed the distilling step-by-step (DSS) method ([paper](https://arxiv.org/abs/2305.02301v1)) that can enrich customer’s data by eliciting the reasoning process (rationales) from a large language model (LLM). This new mechanism has shown to be able to (a) train smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by fine-tuning or distillation. Our method extracts LLM rationales as additional supervision within a multi-task training framework.\n",
+        "The distilling step-by-step (DSS) method ([paper](https://arxiv.org/abs/2305.02301v1)) can enrich customer’s data by eliciting the reasoning process (rationales) from a large language model (LLM). This new mechanism has shown to be able to (a) train smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by fine-tuning or distillation. This method extracts LLM rationales as additional supervision within a multi-task training framework.\n",
         "\n",
-        "Learn more about [distill-text-models](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models)."
+        "Learn more about [distill-text-models](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models).\n",
+        "\n",
+        "**_NOTE_**: This notebook is tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
       ]
     },
     {
@@ -87,23 +82,60 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use `Vertex AI LLM` to distill and deploy a large language model.\n",
+        "In this tutorial, you learn how to distill and deploy a large language model using Vertex AI LLM.\n",
         "\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI LLM`\n",
-        "- `Vertex AI Model Garden`\n",
-        "- `Vertex AI Prediction`\n",
+        "- Vertex AI LLM\n",
+        "- Vertex AI Model Garden\n",
+        "- Vertex AI Online prediction\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
         "- Get the Vertex AI LLM model.\n",
-        "- Distill the model.\n",
-        "  - This will automatically create a Vertex AI endpoint and deploy the model to it.\n",
-        "- Make a prediction using `Vertex AI LLM`.\n",
-        "- Make a prediction using `Vertex AI Prediction`"
+        "- Distill the model(this automatically creates a Vertex AI endpoint and deploys the model to the endpoint). \n",
+        "- Make a prediction using Vertex AI LLM."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3d269b76353d"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "Distillation works on a labeled or an unlabeled dataset. If you have a high quality labeled dataset with hundreds of examples, then it's recommended that you use the labeled dataset. Otherwise, you can use an unlabeled prompt dataset. If you use an unlabeled dataset, then the teacher model generates the labels and the rationale for distillation. More than 1,000 examples are recommended if you use an unlabeled dataset.\n",
+        "\n",
+        "For this tutorial, you use a dataset stored in a public Cloud Storage bucket at the below paths. \n",
+        "- Train sample: `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl`\n",
+        "- Validation sample: `gs://cloud-samples-data/vertex-ai/model-evaluation/peft_eval_sample.jsonl`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e7c0aa7c769f"
+      },
+      "source": [
+        "#### Input format requirement\n",
+        "\n",
+        "The labeled or unlabeled distillation dataset must be in JSON Lines (JSONL) format where each line contains a single tuning example. Before you distill your model, upload your dataset to a Cloud Storage bucket.\n",
+        "\n",
+        "Each dataset example contains an `input_text` field with the model prompt and an optional `output_text` field that contains an example response that the distilled model is expected to produce.\n",
+        "\n",
+        "The maximum token length for `input_text` is 7,168 and the maximum token length for `output_text` is 1,024. If either field exceeds the maximum token length, the excess tokens are truncated.\n",
+        "\n",
+        "The maximum number of examples that a dataset for a text generation model can contain is 10,000.\n",
+        "\n",
+        "\n",
+        "Example:\n",
+        "\n",
+        "```\n",
+        "{\"input_text\": \"question: How many people live in Beijing? context: With over 21 million residents, Beijing is the world's most populous national capital city and is China's second largest city after Shanghai. It is located in Northern China, and is governed as a municipality under the direct administration of the State Council with 16 urban, suburban, and rural districts.[14] Beijing is mostly surrounded by Hebei Province with the exception of neighboring Tianjin to the southeast; together, the three divisions form the Jingjinji megalopolis and the national capital region of China.\", \"output_text\": \"over 21 million people\"}\n",
+        "{\"input_text\": \"question: How many parishes are there in Louisiana? context: The U.S. state of Louisiana is divided into 64 parishes (French: paroisses) in the same manner that 48 other states of the United States are divided into counties, and Alaska is divided into boroughs.\", \"output_text\": \"64\"}\n",
+        "```"
       ]
     },
     {
@@ -129,189 +161,127 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "2b4ef9b72d43"
+        "id": "89d404f6cc9d"
       },
       "outputs": [],
       "source": [
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \"shapely<2.0.0\""
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
+        "                                 \"shapely<2.0.0\" \\\n",
+        "                                 PyYAML"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "54c5ef8a8f43"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5ff1a3cc4e1d"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "import vertexai\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74ccc9e52986"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "vertexai.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -322,9 +292,7 @@
       "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets.\n",
-        "\n",
-        "- *{Note to notebook author: For any user-provided strings that need to be unique (like bucket names or model ID's), append \"-unique\" to the end so proper testing can occur}*"
+        "Create a storage bucket to store intermediate artifacts such as datasets."
       ]
     },
     {
@@ -344,7 +312,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -355,7 +323,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -364,9 +332,11 @@
         "id": "08d289fa873f"
       },
       "source": [
-        "### Dataset\n",
+        "#### Copy the dataset to your bucket\n",
         "\n",
-        "We've provided the below sample data for you to get started.\n"
+        "Before you start the distillation, copy the dataset from the source to your Cloud Storage bucket.\n",
+        "\n",
+        "**Note**: Alternatively, you can directly specify the source path for the data when you perform distillation. Copying the data to your Google Cloud project is only optional."
       ]
     },
     {
@@ -381,33 +351,6 @@
         "! gsutil cp gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl {BUCKET_URI}/peft_train_sample.jsonl"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0e6f90042af6"
-      },
-      "source": [
-        "#### Data Input format requirement\n",
-        "\n",
-        "Distillation works on a labeled or an unlabeled dataset. If you have a high quality labeled dataset with hundreds of examples, then we recommend that you use that. Otherwise, you can use an unlabeled prompt dataset. If you use an unlabeled dataset, then the teacher model generates the labels and the rationale for distillation. More than 1,000 examples are recommended if you use an unlabeled dataset.\n",
-        "\n",
-        "The labeled or unlabeled distillation dataset must be in JSON Lines (JSONL) format where each line contains a single tuning example. Before you distill your model, you upload your dataset to a Cloud Storage bucket.\n",
-        "\n",
-        "Each dataset example contains an `input_text` field with the model prompt and an optional `output_text` field that contains an example response that the distilled model is expected to produce.\n",
-        "\n",
-        "The maximum token length for `input_text` is 7,168 and the maximum token length for `output_text` is 1,024. If either field exceeds the maximum token length, the excess tokens are truncated.\n",
-        "\n",
-        "The maximum number of examples that a dataset for a text generation model can contain is 10,000.\n",
-        "\n",
-        "\n",
-        "Sample dataset:\n",
-        "\n",
-        "```\n",
-        "{\"input_text\": \"question: How many people live in Beijing? context: With over 21 million residents, Beijing is the world's most populous national capital city and is China's second largest city after Shanghai. It is located in Northern China, and is governed as a municipality under the direct administration of the State Council with 16 urban, suburban, and rural districts.[14] Beijing is mostly surrounded by Hebei Province with the exception of neighboring Tianjin to the southeast; together, the three divisions form the Jingjinji megalopolis and the national capital region of China.\", \"output_text\": \"over 21 million people\"}\n",
-        "{\"input_text\": \"question: How many parishes are there in Louisiana? context: The U.S. state of Louisiana is divided into 64 parishes (French: paroisses) in the same manner that 48 other states of the United States are divided into counties, and Alaska is divided into boroughs.\", \"output_text\": \"64\"}\n",
-        "```"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -425,44 +368,21 @@
       },
       "outputs": [],
       "source": [
-        "import vertexai\n",
         "from google.cloud import aiplatform\n",
         "from vertexai.preview.language_models import (TextGenerationModel,\n",
         "                                              TuningEvaluationSpec)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "outputs": [],
-      "source": [
-        "vertexai.init(project=PROJECT_ID, location=REGION)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "a0275c76a30f"
       },
       "source": [
-        "### Load pretrained model\n",
+        "## Load pretrained model\n",
         "\n",
         "Load the pretrained BISON model from Vertex AI LLM Model Garden.\n",
-        "See the models that supports distillation in [here](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models#supported_models)."
+        "See the [list of models that support distillation](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models#supported_models)."
       ]
     },
     {
@@ -485,20 +405,19 @@
         "id": "3f35db20ac38"
       },
       "source": [
-        "### Distill the model\n",
+        "## Distill the model\n",
         "\n",
         "Next, you distill the model using the `distill_from()` method, with the following parameters:\n",
         "\n",
-        "`teacher_model`: The teacher model that you would like to distill the knowledge from.\n",
-        "`dataset`: A pandas Dataframe or Cloud Storage location of the training data for tuning the model.<br>\n",
-        "`learning_rate_multiplier`: A multiplier to apply to the recommended learning rate. To use the recommended learning rate, use 1.0. <br>\n",
-        "`train_steps`: The number of steps to run for model tuning. The batch size varies by tuning location:<br>\n",
-        "- us-central1 has a batch size of 8.\n",
-        "- europe-west4 has a batch size of 24.<br>\n",
+        "- `teacher_model`: The teacher model that you would like to distill the knowledge from.\n",
+        "- `dataset`: A pandas Dataframe or Cloud Storage location of the training data for tuning the model.\n",
+        "- `learning_rate_multiplier`: A multiplier to apply to the recommended learning rate. To use the recommended learning rate, use 1.0.\n",
+        "- `train_steps`: The number of steps to run for model tuning. The default value is 300. The batch size varies by tuning location as below for 8k models such as `text-bison@002`:\n",
+        "    \n",
+        "    - us-central1 has a batch size of 8.\n",
+        "    - europe-west4 has a batch size of 24.\n",
         "\n",
-        "If there are 240 examples in a training dataset, in europe-west4, it takes 240 / 24 = 10 steps to process the entire dataset once. In us-central1, it takes 240 / 8 = 30 steps to process the entire dataset once. The default value is 300.<br>\n",
-        "\n",
-        "For more context, see this [doc](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models#create_a_text_model_distilling_job) for definition of the parameters. "
+        "For parameter definitions and further context, see [Create a text model distilling job](https://cloud.google.com/vertex-ai/docs/generative-ai/models/distill-text-models#create_a_text_model_distilling_job). "
       ]
     },
     {
@@ -517,6 +436,17 @@
         "eval_spec.evaluation_interval = 20"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "495abb2c72c8"
+      },
+      "source": [
+        "Set a display name for your model resource and the endpoint resource using the `DISPLAY_NAME` parameter.\n",
+        "\n",
+        "**Note**: In the tuning pipeline, the model and endpoint share the same display name."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -525,15 +455,22 @@
       },
       "outputs": [],
       "source": [
-        "student_model.distill_from(\n",
+        "# Set the display name\n",
+        "DISPLAY_NAME = \"vertex-distillation-model-unique\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Create the tuning pipeline job\n",
+        "pipeline = student_model.distill_from(\n",
         "    teacher_model=teacher_model,\n",
         "    dataset=f\"{BUCKET_URI}/peft_train_sample.jsonl\",\n",
         "    train_steps=200,\n",
         "    learning_rate_multiplier=1,\n",
         "    accelerator_type=\"TPU\",\n",
-        "    model_display_name=\"test-vertex-distillation\",\n",
+        "    model_display_name=DISPLAY_NAME,\n",
         "    evaluation_spec=eval_spec,\n",
-        ")"
+        ")\n",
+        "\n",
+        "# Wait until the tuning pipeline job finishes\n",
+        "pipeline._job.wait()"
       ]
     },
     {
@@ -542,7 +479,7 @@
         "id": "d432a5238785"
       },
       "source": [
-        "### Make a prediction with Vertex AI LLM\n",
+        "## Make a prediction with Vertex AI LLM\n",
         "\n",
         "Now, make a prediction using the `predict()` method from the Vertex AI LLM interface."
       ]
@@ -555,6 +492,7 @@
       },
       "outputs": [],
       "source": [
+        "# Define the prompt\n",
         "prompt = \"TRANSCRIPT: \\nPROCEDURE PERFORMED: , Umbilical hernia repair.,PROCEDURE:,  After informed consent was obtained, the patient was brought to the operative suite and placed supine on the operating table.  The patient was sedated, and an adequate local anesthetic was administered using 1% lidocaine without epinephrine.  The patient was prepped and draped in the usual sterile manner.,A standard curvilinear umbilical incision was made, and dissection was carried down to the hernia sac using a combination of Metzenbaum scissors and Bovie electrocautery.  The sac was cleared of overlying adherent tissue, and the fascial defect was delineated.  The fascia was cleared of any adherent tissue for a distance of 1.5 cm from the defect.  The sac was then placed into the abdominal cavity and the defect was closed primarily using simple interrupted 0 Vicryl sutures.  The umbilicus was then re-formed using 4-0 Vicryl to tack the umbilical skin to the fascia.,The wound was then irrigated using sterile saline, and hemostasis was obtained using Bovie electrocautery.  The skin was approximated with 4-0 Vicryl in a subcuticular fashion.  The skin was prepped with benzoin, and Steri-Strips were applied.  A dressing was then applied.  All surgical counts were reported as correct.,Having tolerated the procedure well, the patient was subsequently taken to the recovery room in good and stable condition.\\n\\n LABEL: \""
       ]
     },
@@ -566,6 +504,7 @@
       },
       "outputs": [],
       "source": [
+        "# Print the prompt\n",
         "print(student_model.predict(prompt))"
       ]
     },
@@ -591,27 +530,30 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "# Fetch the endpoint resource using the display name and create time\n",
+        "endpoints = aiplatform.Endpoint.list(\n",
+        "    filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        ")\n",
+        "if len(endpoints) > 0:\n",
+        "    # Undeploy the model from the endpoint\n",
+        "    endpoints[0].undeploy_all()\n",
+        "    # Delete the endpoint\n",
+        "    endpoints[0].delete()\n",
         "\n",
-        "delete_bucket = False\n",
+        "# Fetch the model resource using the display name and create time\n",
+        "models = aiplatform.Model.list(\n",
+        "    filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        ")\n",
+        "if len(models) > 0:\n",
+        "    # Delete the model\n",
+        "    models[0].delete()\n",
         "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    # Delete endpoint resource\n",
-        "    endpoint = aiplatform.Endpoint(student_model._endpoint.resource_name)\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d473314cae55"
-      },
-      "outputs": [],
-      "source": [
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete the pipeline job\n",
+        "pipeline._job.delete()\n",
+        "\n",
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -rf {BUCKET_URI}"
       ]
     }

notebooks/official/generative_ai/mistralai_intro.ipynb

@@ -0,0 +1,352 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "9A9NkTRTfo2I"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IPprg6Oz0QDs"
+      },
+      "source": [
+        "# Getting Started with Mistral AI Models\n",
+        "<table align=\"left\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/mistralai_intro.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fgenerative_ai%2Fmistralai_intro.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">                                                                             \n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/generative_ai/mistralai_intro.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/mistralai_intro.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  \n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8fK_rdvvx1iZ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "### Mistral AI on Vertex AI\n",
+        "\n",
+        "Mistral AI models on Vertex AI offer fully managed and serverless models are offered as managed APIs. To use a Mistral AI model on Vertex AI, send a request directly to the Vertex AI API endpoint.\n",
+        "\n",
+        "You can stream your Mistral AI model responses to reduce the end-user latency perception. A streamed response uses server-sent events (SSE) to incrementally stream the response.\n",
+        "\n",
+        "Learn more about [Vertex AI](https://cloud.google.com/vertex-ai). \n",
+        "\n",
+        "### Available Mistral AI models\n",
+        "\n",
+        "*   ### Mistral Large (2407)\n",
+        "Complex tasks that require large reasoning capabilities or are highly specialized (synthetic text Generation, code generation, RAG, or agents).\n",
+        "\n",
+        "*   ### Mistral Nemo\n",
+        "Reasoning, world knowledge, and coding performance are state-of-the-art in its size category.\n",
+        "\n",
+        "*   ### Codestral\n",
+        "Coding specific tasks to enhance developers productivity with code completion and fill-in-the-middle capabilities.\n",
+        "\n",
+        "\n",
+        "## Objective\n",
+        "\n",
+        "This notebook shows how to use **Vertex AI API** to call the Mistral AI models on Vertex AI API with the Large, Nemo, and Codestral models.\n",
+        "\n",
+        "For more information, see the [Use Mistral's](https://docs.mistral.ai/) documentation and [Mistral's models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) on Google Cloud.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HcJCV6Dw5usD"
+      },
+      "source": [
+        "## Vertex AI API"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nwYvaaW25jYS"
+      },
+      "source": [
+        "## Get Started\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6a5bea26f60f"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c97be6a73155"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2fxZn4SAbxdl"
+      },
+      "source": [
+        "#### Select Mistral AI model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y8X70FTSbx7U"
+      },
+      "outputs": [],
+      "source": [
+        "MODEL = \"mistral-large\"  # @param [\"mistral-large\", \"mistral-nemo\", \"codestral\"]\n",
+        "if MODEL == \"mistral-large\":\n",
+        "    available_regions = [\"europe-west4\", \"us-central1\"]\n",
+        "    available_versions = [\"latest\", \"2407\"]\n",
+        "elif MODEL == \"mistral-nemo\":\n",
+        "    available_regions = [\"europe-west4\", \"us-central1\"]\n",
+        "    available_versions = [\"latest\", \"2407\"]\n",
+        "elif MODEL == \"codestral\":\n",
+        "    available_regions = [\"europe-west4\", \"us-central1\"]\n",
+        "    available_versions = [\"latest\", \"2405\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bpuX3sKtexlK"
+      },
+      "source": [
+        "#### Select a location"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dHl8xW45ex_O"
+      },
+      "outputs": [],
+      "source": [
+        "import ipywidgets as widgets\n",
+        "from IPython.display import display\n",
+        "\n",
+        "dropdown_loc = widgets.Dropdown(\n",
+        "    options=available_regions,\n",
+        "    description=\"Select a location:\",\n",
+        "    font_weight=\"bold\",\n",
+        "    style={\"description_width\": \"initial\"},\n",
+        ")\n",
+        "\n",
+        "dropdown_ver = widgets.Dropdown(\n",
+        "    options=available_versions,\n",
+        "    description=\"Select a Model version (optional):\",\n",
+        "    font_weight=\"bold\",\n",
+        "    style={\"description_width\": \"initial\"},\n",
+        ")\n",
+        "\n",
+        "def dropdown_loc_eventhandler(change):\n",
+        "    global LOCATION\n",
+        "    if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n",
+        "        LOCATION = change.new\n",
+        "        print(\"Selected:\", change.new)\n",
+        "\n",
+        "def dropdown_ver_eventhandler(change):\n",
+        "    global MODEL_VERSION\n",
+        "    if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n",
+        "        MODEL_VERSION = change.new\n",
+        "        print(\"Selected:\", change.new)\n",
+        "\n",
+        "LOCATION = dropdown_loc.value\n",
+        "dropdown_loc.observe(dropdown_loc_eventhandler, names=\"value\")\n",
+        "display(dropdown_loc)\n",
+        "\n",
+        "MODEL_VERSION = dropdown_ver.value\n",
+        "dropdown_ver.observe(dropdown_ver_eventhandler, names=\"value\")\n",
+        "display(dropdown_ver)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3q58icinBjoK"
+      },
+      "source": [
+        "#### Set Google Cloud project and model information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hltNx33t6cSZ"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "ENDPOINT = f\"https://{LOCATION}-aiplatform.googleapis.com\"\n",
+        "SELECTED_MODEL_VERSION = \"\" if MODEL_VERSION == \"latest\" else f\"@{MODEL_VERSION}\"\n",
+        "\n",
+        "if not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n",
+        "    raise ValueError(\"Please set your PROJECT_ID\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4NAstKRFBt4N"
+      },
+      "source": [
+        "#### Import required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QZEFLE6a6bqy"
+      },
+      "outputs": [],
+      "source": [
+        "import json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5ahw-uFjCAbo"
+      },
+      "source": [
+        "### Text generation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61107099357a"
+      },
+      "source": [
+        "#### Unary call\n",
+        "\n",
+        "Sends a POST request to the specified API endpoint to get a response from the model using the provided payload."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4zFz260B50oi"
+      },
+      "outputs": [],
+      "source": [
+        "PAYLOAD = {\n",
+        "    \"model\": MODEL,\n",
+        "    \"messages\": [{\"role\": \"user\", \"content\": \"who is the best French painter?\"}],\n",
+        "    \"max_tokens\": 100,\n",
+        "    \"stream\": False,\n",
+        "}\n",
+        "\n",
+        "request = json.dumps(PAYLOAD)\n",
+        "!curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"Content-Type: application/json\" {ENDPOINT}/v1/projects/{PROJECT_ID}/locations/{LOCATION}/publishers/mistralai/models/{MODEL}{SELECTED_MODEL_VERSION}:rawPredict -d '{request}'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e6f52fae9379"
+      },
+      "source": [
+        "#### Streaming call\n",
+        "\n",
+        "Sends a POST request to the specified API endpoint to stream a response from the model using the provided payload."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c99761dcd7da"
+      },
+      "outputs": [],
+      "source": [
+        "PAYLOAD = {\n",
+        "    \"model\": MODEL,\n",
+        "    \"messages\": [{\"role\": \"user\", \"content\": \"who is the best French painter?\"}],\n",
+        "    \"max_tokens\": 100,\n",
+        "    \"stream\": True,\n",
+        "}\n",
+        "\n",
+        "request = json.dumps(PAYLOAD)\n",
+        "!curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"Content-Type: application/json\" {ENDPOINT}/v1/projects/{PROJECT_ID}/locations/{LOCATION}/publishers/mistralai/models/{MODEL}{SELECTED_MODEL_VERSION}:streamRawPredict -d '{request}'"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "mistralai_intro.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb

@@ -32,26 +32,27 @@
         "# Vertex AI Migration: AutoML Image Classification\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-automl-image-classification-batch-online.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "</table>"
       ]
     },
     {
@@ -80,10 +81,10 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `AutoML`\n",
-        "- `Vertex AI batch prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- AutoML\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -145,54 +146,86 @@
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
         "                                 google-cloud-storage \\\n",
-        "                                 tensorflow"
+        "                                 tensorflow==2.15.1"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "6e0c0cdffff3"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "dcc98768955f"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "294459ee3484"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "23b421f88a9b"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7de6ef0fac42"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "df44641f2fdb"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -204,134 +237,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "timestamp"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -373,7 +279,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -396,9 +302,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "import google.cloud.aiplatform as aip"
+        "import google.cloud.aiplatform as aiplatform"
       ]
     },
     {
@@ -420,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -442,9 +346,7 @@
       },
       "outputs": [],
       "source": [
-        "IMPORT_FILE = (\n",
-        "    \"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\"\n",
-        ")"
+        "IMPORT_FILE = \"gs://cloud-samples-data/ai-platform/flowers/flowers.csv\""
       ]
     },
     {
@@ -523,10 +425,10 @@
       },
       "outputs": [],
       "source": [
-        "dataset = aip.ImageDataset.create(\n",
-        "    display_name=\"Flowers\" + \"_\" + UUID,\n",
+        "dataset = aiplatform.ImageDataset.create(\n",
+        "    display_name=\"Flowers\" + \"_\" + \"unique\",\n",
         "    gcs_source=[IMPORT_FILE],\n",
-        "    import_schema_uri=aip.schema.dataset.ioformat.image.single_label_classification,\n",
+        "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification,\n",
         ")\n",
         "\n",
         "print(dataset.resource_name)"
@@ -608,8 +510,8 @@
       },
       "outputs": [],
       "source": [
-        "dag = aip.AutoMLImageTrainingJob(\n",
-        "    display_name=\"flowers_\" + UUID,\n",
+        "dag = aiplatform.AutoMLImageTrainingJob(\n",
+        "    display_name=\"flowers_\" + \"unique\",\n",
         "    prediction_type=\"classification\",\n",
         "    multi_label=False,\n",
         "    model_type=\"CLOUD\",\n",
@@ -663,10 +565,10 @@
       "source": [
         "model = dag.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"flowers_\" + UUID,\n",
-        "    training_fraction_split=0.8,\n",
-        "    validation_fraction_split=0.1,\n",
-        "    test_fraction_split=0.1,\n",
+        "    model_display_name=\"flowers_\" + \"unique\",\n",
+        "    training_fraction_split=0.4,\n",
+        "    validation_fraction_split=0.3,\n",
+        "    test_fraction_split=0.3,\n",
         "    budget_milli_node_hours=8000,\n",
         "    disable_early_stopping=False,\n",
         ")"
@@ -736,11 +638,13 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=flowers_\" + UUID)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=flowers_\" + \"unique\")\n",
         "\n",
         "# Get a reference to the Model Service client\n",
-        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
-        "model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
+        "client_options = {\"api_endpoint\": f\"{LOCATION}-aiplatform.googleapis.com\"}\n",
+        "model_service_client = aiplatform.gapic.ModelServiceClient(\n",
+        "    client_options=client_options\n",
+        ")\n",
         "\n",
         "model_evaluations = model_service_client.list_model_evaluations(\n",
         "    parent=models[0].resource_name\n",
@@ -933,7 +837,7 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"flowers_\" + UUID,\n",
+        "    job_display_name=\"flowers_\" + \"unique\",\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    sync=False,\n",
@@ -1279,11 +1183,12 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
-        "\n",
         "# Delete the dataset using the Vertex dataset object\n",
         "dataset.delete()\n",
         "\n",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
+        "\n",
         "# Delete the model using the Vertex model object\n",
         "model.delete()\n",
         "\n",
@@ -1293,8 +1198,10 @@
         "# Delete the batch prediction job\n",
         "batch_predict_job.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = True  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb

@@ -33,20 +33,25 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-automl-object-tracking-batch-prediction.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",
@@ -75,15 +80,15 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.\n",
+        "In this tutorial, you learn to use AutoML to train a video model and use Vertex AI batch prediction to do batch predictions.\n",
         "\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `AutoML`\n",
-        "- `Vertex AI Batch Prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- AutoML\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -125,19 +130,26 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "5143c7e7acab"
       },
       "outputs": [],
       "source": [
@@ -149,84 +161,97 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b49704f4eeca"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -260,64 +285,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -346,7 +313,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -357,54 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_vars"
-      },
-      "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
-        "### Import libraries and define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "import google.cloud.aiplatform as aip"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "source": [
-        "## Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -486,12 +406,12 @@
         "id": "create_dataset:video,vot"
       },
       "source": [
-        "### Create the Dataset\n",
+        "### Create the dataset\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `VideoDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource using the `create` method for the `VideoDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
+        "- `display_name`: The human readable name for the dataset resource.\n",
+        "- `gcs_source`: A list of one or more dataset index files to import the data items into the dataset resource.\n",
         "\n",
         "This operation may take several minutes."
       ]
@@ -504,10 +424,12 @@
       },
       "outputs": [],
       "source": [
-        "dataset = aip.VideoDataset.create(\n",
+        "import os\n",
+        "\n",
+        "dataset = aiplatform.VideoDataset.create(\n",
         "    display_name=\"Traffic\" + \"_\" + UUID,\n",
         "    gcs_source=[IMPORT_FILE],\n",
-        "    import_schema_uri=aip.schema.dataset.ioformat.video.object_tracking,\n",
+        "    import_schema_uri=aiplatform.schema.dataset.ioformat.video.object_tracking,\n",
         ")\n",
         "if os.getenv(\"IS_TESTING\"):\n",
         "    import time\n",
@@ -560,13 +482,15 @@
       "source": [
         "### Create and run training pipeline\n",
         "\n",
-        "To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
+        "To train an AutoML model, you perform two steps: \n",
+        "1) create a training pipeline, and \n",
+        "2) run the pipeline.\n",
         "\n",
         "#### Create training pipeline\n",
         "\n",
         "An AutoML training pipeline is created with the `AutoMLVideoTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
+        "- `display_name`: The human readable name for the TrainingJob resource.\n",
         "- `prediction_type`: The type task to train the model for.\n",
         "  - `classification`: A video classification model.\n",
         "  - `object_tracking`: A video object tracking model.\n",
@@ -583,7 +507,7 @@
       },
       "outputs": [],
       "source": [
-        "dag = aip.AutoMLVideoTrainingJob(\n",
+        "dag = aiplatform.AutoMLVideoTrainingJob(\n",
         "    display_name=\"traffic_\" + UUID,\n",
         "    prediction_type=\"object_tracking\",\n",
         ")\n",
@@ -612,12 +536,12 @@
         "\n",
         "Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
+        "- `dataset`: The dataset resource to train the model.\n",
         "- `model_display_name`: The human readable name for the trained model.\n",
         "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
         "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
         "\n",
-        "The `run` method when completed returns the `Model` resource.\n",
+        "The `run` method when completed returns the model resource.\n",
         "\n",
         "The execution of the training pipeline take upto 20 minutes."
       ]
@@ -707,11 +631,13 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=traffic_\" + UUID)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=traffic_\" + UUID)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
-        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
-        "model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
+        "client_options = {\"api_endpoint\": f\"{LOCATION}-aiplatform.googleapis.com\"}\n",
+        "model_service_client = aiplatform.gapic.ModelServiceClient(\n",
+        "    client_options=client_options\n",
+        ")\n",
         "\n",
         "model_evaluations = model_service_client.list_model_evaluations(\n",
         "    parent=models[0].resource_name\n",
@@ -828,7 +754,7 @@
         "Now make a batch input file, which you store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each video. The dictionary contains the key/value pairs:\n",
         "\n",
         "- `content`: The Cloud Storage path to the video.\n",
-        "- `mimeType`: The content type. In our example, it is a `avi` file.\n",
+        "- `mimeType`: The content type. In our example, it is a avi file.\n",
         "- `timeSegmentStart`: The start timestamp in the video to do prediction on. *Note*, the timestamp must be specified as a string and followed by s (second), m (minute) or h (hour).\n",
         "- `timeSegmentEnd`: The end timestamp in the video to do prediction on."
       ]

notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb

@@ -32,25 +32,28 @@
         "# Vertex AI migration: AutoML tabular binary classification\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>\n"
       ]
     },
     {
@@ -75,23 +78,23 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you create an AutoML tabular binary classification model and deploy for online prediction from a Python script using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
+        "In this tutorial, you create an AutoML tabular binary classification model and deploy for online prediction from a Python script using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or using the online Cloud Console.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI managed Datasets\n",
+        "- Vertex AI managed dataset\n",
         "- Vertex AI Training\n",
-        "- Vertex AI Endpoints\n",
+        "- Vertex AI endpoints\n",
         "- Vertex AI prediction\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a Vertex `Dataset` resource.\n",
+        "- Create a Vertex AI dataset resource.\n",
         "- Train the model.\n",
         "- View the model evaluation.\n",
-        "- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
+        "- Deploy the model resource to a serving endpoint resource.\n",
         "- Make a prediction.\n",
-        "- Undeploy the `Model`"
+        "- Undeploy the model"
       ]
     },
     {
@@ -102,7 +105,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
+        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . This dataset doesn't require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
       ]
     },
     {
@@ -118,11 +121,9 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and \n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -131,9 +132,8 @@
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -144,7 +144,8 @@
       },
       "outputs": [],
       "source": [
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform fsspec gcsfs"
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform fsspec gcsfs \\\n",
+        "                                 pandas"
       ]
     },
     {
@@ -153,7 +154,8 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -164,159 +166,74 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "befa6ca14bc0"
       },
       "source": [
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "7de6ef0fac42"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bd0e79ceaea2"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8940d70dfdef"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "timestamp"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -347,7 +264,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -358,7 +275,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -378,7 +295,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip\n",
+        "import google.cloud.aiplatform as aiplatform\n",
         "import pandas as pd"
       ]
     },
@@ -401,7 +318,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -436,9 +353,9 @@
         "\n",
         "This tutorial uses a version of the Bank Marketing dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
         "\n",
-        "Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows.\n",
+        "Start by doing a quick peek at the data. Count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows.\n",
         "\n",
-        "You also need for training to know the heading name of the label column, which is save as `label_column`. For this dataset, it is the last column in the CSV file."
+        "You also need for training to know the heading name of the label column, which is save as `label_column`. For this dataset, it's the last column in the CSV file."
       ]
     },
     {
@@ -488,13 +405,13 @@
       "source": [
         "### Create the Dataset\n",
         "\n",
-        "Next, create the `Dataset` resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
+        "Next, create the dataset resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
-        "- `bq_source`: Alternatively, import data items from a BigQuery table into the `Dataset` resource.\n",
+        "- display_name: The human readable name for the dataset resource.\n",
+        "- gcs_source: A list of one or more dataset index files to import the data items into the dataset resource.\n",
+        "- bq_source: Alternatively, import data items from a BigQuery table into the dataset resource.\n",
         "\n",
-        "This operation may take several minutes."
+        "This operation may takes several minutes."
       ]
     },
     {
@@ -505,8 +422,8 @@
       },
       "outputs": [],
       "source": [
-        "dataset = aip.TabularDataset.create(\n",
-        "    display_name=\"Bank Marketing\" + \"_\" + UUID, gcs_source=[IMPORT_FILE]\n",
+        "dataset = aiplatform.TabularDataset.create(\n",
+        "    display_name=\"Bank Marketing\" + \"_\" + \"unique\", gcs_source=[IMPORT_FILE]\n",
         ")\n",
         "\n",
         "print(dataset.resource_name)"
@@ -560,24 +477,24 @@
         "\n",
         "An AutoML training pipeline is created with the `AutoMLTabularTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
-        "- `optimization_prediction_type`: The type task to train the model for.\n",
-        "  - `classification`: A tabuar classification model.\n",
-        "  - `regression`: A tabular regression model.\n",
-        "- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
-        "- `optimization_objective`: The optimization objective to minimize or maximize.\n",
+        "- display_name: The human readable name for the `TrainingJob` resource.\n",
+        "- optimization_prediction_type: The type of the task to train the model for.\n",
+        "  - classification: A tabuar classification model.\n",
+        "  - regression: A tabular regression model.\n",
+        "- column_transformations: (Optional): Transformations to apply to the input columns\n",
+        "- optimization_objective: The optimization objective to minimize or maximize.\n",
         "  - binary classification:\n",
-        "    - `minimize-log-loss`\n",
-        "    - `maximize-au-roc`\n",
-        "    - `maximize-au-prc`\n",
-        "    - `maximize-precision-at-recall`\n",
-        "    - `maximize-recall-at-precision`\n",
+        "    - minimize-log-loss\n",
+        "    - maximize-au-roc\n",
+        "    - maximize-au-prc\n",
+        "    - maximize-precision-at-recall\n",
+        "    - maximize-recall-at-precision\n",
         "  - multi-class classification:\n",
-        "    - `minimize-log-loss`\n",
+        "    - minimize-log-loss\n",
         "  - regression:\n",
-        "    - `minimize-rmse`\n",
-        "    - `minimize-mae`\n",
-        "    - `minimize-rmsle`\n",
+        "    - minimize-rmse\n",
+        "    - minimize-mae\n",
+        "    - minimize-rmsle\n",
         "\n",
         "The instantiated object is the DAG (directed acyclic graph) for the training pipeline."
       ]
@@ -590,8 +507,8 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.AutoMLTabularTrainingJob(\n",
-        "    display_name=\"bank_\" + UUID,\n",
+        "job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"bank_\" + \"unique\",\n",
         "    optimization_prediction_type=\"classification\",\n",
         "    optimization_objective=\"minimize-log-loss\",\n",
         ")\n",
@@ -620,18 +537,18 @@
         "\n",
         "Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
-        "- `model_display_name`: The human readable name for the trained model.\n",
-        "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
-        "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
-        "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
-        "- `target_column`: The name of the column to train as the label.\n",
-        "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
-        "- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
+        "- dataset: The dataset resource to train the model.\n",
+        "- model_display_name: The human readable name for the trained model.\n",
+        "- training_fraction_split: The percentage of the dataset to use for training.\n",
+        "- test_fraction_split: The percentage of the dataset to use for test (holdout data).\n",
+        "- validation_fraction_split: The percentage of the dataset to use for validation.\n",
+        "- target_column: The name of the column to train as the label.\n",
+        "- budget_milli_node_hours: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
+        "- disable_early_stopping: If `True`, training maybe completed before using the entire budget if the service believes it can't further improve on the model objective measurements.\n",
         "\n",
         "The `run` method when completed returns the `Model` resource.\n",
         "\n",
-        "The execution of the training pipeline will take upto 20 minutes."
+        "The execution of the training pipeline takes upto 20 minutes."
       ]
     },
     {
@@ -644,7 +561,7 @@
       "source": [
         "model = job.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"bank_\" + UUID,\n",
+        "    model_display_name=\"bank_\" + \"unique\",\n",
         "    training_fraction_split=0.6,\n",
         "    validation_fraction_split=0.2,\n",
         "    test_fraction_split=0.2,\n",
@@ -720,11 +637,13 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=bank_\" + UUID)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=bank_\" + \"unique\")\n",
         "\n",
         "# Get a reference to the Model Service client\n",
-        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
-        "model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
+        "client_options = {\"api_endpoint\": f\"{LOCATION}-aiplatform.googleapis.com\"}\n",
+        "model_service_client = aiplatform.gapic.ModelServiceClient(\n",
+        "    client_options=client_options\n",
+        ")\n",
         "\n",
         "model_evaluations = model_service_client.list_model_evaluations(\n",
         "    parent=models[0].resource_name\n",
@@ -799,7 +718,7 @@
       "source": [
         "### Make test items\n",
         "\n",
-        "You use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use synthetic data as a test data items. Don't be concerned that you are using synthetic data -- you just want to demonstrate how to make a prediction."
       ]
     },
     {
@@ -847,7 +766,7 @@
       "source": [
         "### Make the batch prediction request\n",
         "\n",
-        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
+        "Now that your Model resource is trained, you can make a batch prediction by invoking the `batch_predict()` method, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
@@ -866,7 +785,7 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"bank_\" + UUID,\n",
+        "    job_display_name=\"bank_\" + \"unique\",\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"csv\",\n",
@@ -1093,7 +1012,7 @@
       "source": [
         "### Make test item\n",
         "\n",
-        "You use synthetic data as a test data item. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use synthetic data as a test data item. Don't be concerned that you are using synthetic data -- you just want to demonstrate how to make a prediction."
       ]
     },
     {
@@ -1149,7 +1068,7 @@
         "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
         "- `displayNames`: The class names for each class label.\n",
         "- `confidences`: The predicted confidence, between 0 and 1, per class label.\n",
-        "- `deployed_model_id`: The Vertex AI identifier for the deployed `Model` resource which did the predictions."
+        "- `deployed_model_id`: The Vertex AI identifier for the deployed model resource which did the predictions."
       ]
     },
     {
@@ -1185,7 +1104,7 @@
       "source": [
         "## Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you are done doing predictions, undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -1228,8 +1147,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "# Delete the dataset using the Vertex dataset object\n",
         "dataset.delete()\n",
         "\n",
@@ -1245,8 +1162,12 @@
         "# Delete the batch prediction job using the Vertex batch prediction object\n",
         "batch_predict_job.delete()\n",
         "\n",
+        "# remove locally generated files\n",
+        "! rm -r batch.csv\n",
+        "! rm -r tmp.csv\n",
+        "\n",
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/migration/sdk-automl-text-entity-extraction-batch-prediction.ipynb

@@ -23,6 +23,16 @@
         "# limitations under the License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "90a6064798e9"
+      },
+      "source": [
+        "Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis models by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text objectives until June 15, 2025. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see \n",
+        "[Introduction to tuning](https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-gemini-overview)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb

@@ -33,23 +33,26 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-automl-video-classification-batch-prediction.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -75,15 +78,15 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.\n",
+        "In this tutorial, you learn to use AutoML to train a video model and use Vertex AI batch prediction to do batch predictions.\n",
         "\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `AutoML`\n",
-        "- `Vertex AI Batch Prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- AutoML\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -99,7 +102,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Human Motion dataset](https://TODO) from [MIT](http://cbcl.mit.edu/publications/ps/Kuehne_etal_iccv11.pdf). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket."
+        "The dataset used for this tutorial is the [Human Motion dataset](https://TODO) from [MIT](http://cbcl.mit.edu/publications/ps/Kuehne_etal_iccv11.pdf). The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
       ]
     },
     {
@@ -125,12 +128,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -143,7 +153,9 @@
       "source": [
         "import os\n",
         "\n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \n",
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform\n",
+        "\n",
+        "! pip3 install --quiet tensorflow==2.15.1\n",
         "\n",
         "if os.getenv(\"IS_TESTING\"):\n",
         "    ! pip3 install --upgrade --quiet google-cloud-storage tensorflow"
@@ -152,48 +164,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -207,29 +251,9 @@
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "! gcloud config set project {PROJECT_ID}\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -240,7 +264,8 @@
       "source": [
         "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "To avoid name collisions between users on created resources, create a uuid for each session instance. Append these uuids to the respective names of the resources \n",
+        "created in this tutorial."
       ]
     },
     {
@@ -263,64 +288,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -360,7 +327,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -386,7 +353,7 @@
         "import os\n",
         "import sys\n",
         "\n",
-        "import google.cloud.aiplatform as aip"
+        "from google.cloud import aiplatform"
       ]
     },
     {
@@ -408,7 +375,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -521,10 +488,10 @@
       },
       "outputs": [],
       "source": [
-        "dataset = aip.VideoDataset.create(\n",
+        "dataset = aiplatform.VideoDataset.create(\n",
         "    display_name=\"MIT Human Motion\" + \"_\" + UUID,\n",
         "    gcs_source=[IMPORT_FILE],\n",
-        "    import_schema_uri=aip.schema.dataset.ioformat.video.classification,\n",
+        "    import_schema_uri=aiplatform.schema.dataset.ioformat.video.classification,\n",
         ")\n",
         "\n",
         "print(dataset.resource_name)"
@@ -596,7 +563,7 @@
       },
       "outputs": [],
       "source": [
-        "dag = aip.AutoMLVideoTrainingJob(\n",
+        "dag = aiplatform.AutoMLVideoTrainingJob(\n",
         "    display_name=\"hmdb_\" + UUID,\n",
         "    prediction_type=\"classification\",\n",
         ")\n",
@@ -744,11 +711,13 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=hmdb_\" + UUID)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=hmdb_\" + UUID)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
-        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
-        "model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
+        "client_options = {\"api_endpoint\": f\"{LOCATION}-aiplatform.googleapis.com\"}\n",
+        "model_service_client = aiplatform.gapic.ModelServiceClient(\n",
+        "    client_options=client_options\n",
+        ")\n",
         "\n",
         "model_evaluations = model_service_client.list_model_evaluations(\n",
         "    parent=models[0].resource_name\n",
@@ -823,7 +792,7 @@
       "source": [
         "### Get test item(s)\n",
         "\n",
-        "Now do a batch prediction to your Vertex model. You will use arbitrary examples out of the dataset as a test items. Don't be concerned that the examples were likely used in training the model -- we just want to demonstrate how to make a prediction."
+        "Now do a batch prediction to your Vertex model. You use arbitrary examples out of the dataset as a test items. Don't be concerned that the examples were likely used in training the model -- the purpose here is to demonstrate how to make a prediction."
       ]
     },
     {
@@ -856,10 +825,10 @@
       "source": [
         "### Make a batch input file\n",
         "\n",
-        "Now make a batch input file, which you store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You will use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each video. The dictionary contains the key/value pairs:\n",
+        "Now make a batch input file, which you store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each video. The dictionary contains the key/value pairs:\n",
         "\n",
         "- `content`: The Cloud Storage path to the video.\n",
-        "- `mimeType`: The content type. In our example, it is a `avi` file.\n",
+        "- `mimeType`: The content type. In our example, it's a `avi` file.\n",
         "- `timeSegmentStart`: The start timestamp in the video to do prediction on. *Note*, the timestamp must be specified as a string and followed by s (second), m (minute) or h (hour).\n",
         "- `timeSegmentEnd`: The end timestamp in the video to do prediction on."
       ]
@@ -910,7 +879,7 @@
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
         "- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
+        "- `sync`: If set to True, the call blocks while waiting for the asynchronous batch job to complete."
       ]
     },
     {
@@ -1100,13 +1069,12 @@
         "model.delete()\n",
         "\n",
         "# Delete the AutoML or Pipeline trainig job\n",
-        "\n",
         "dag.delete()\n",
         "\n",
         "# Delete the batch prediction job using the Vertex batch prediction object\n",
         "batch_predict_job.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb

@@ -32,24 +32,26 @@
         "# Vertex AI migration: Custom image classification with a custom training container\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-custom-image-classification-custom-container.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -78,23 +80,23 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- *Vertex AI Training*\n",
-        "- *Vertex AI Model Registry*\n",
-        "- *Vertex AI Batch Predictions*\n",
-        "- *Vertex AI Endpoints*\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI Model Registry\n",
+        "- Vertex AI batch Predictions\n",
+        "- Vertex AI endpoints\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- *Package the training code into a python application.*\n",
-        "- *Containerize the training application using Cloud Build and Artifact Registry.*\n",
-        "- *Create a custom container training job in Vertex AI and run it.*\n",
-        "- *Evaluate the model generated from the training job.*\n",
-        "- *Create a model resource for the trained model in Vertex AI Model Registry.*\n",
-        "- *Run a Vertex AI batch prediction job.*\n",
-        "- *Deploy the model resource to a Vertex AI Endpoint.*\n",
-        "- *Run a online prediction job on the model resource.*\n",
-        "- *Clean up the resources created.*"
+        "- Package the training code into a python application.\n",
+        "- Containerize the training application using Cloud Build and Artifact Registry.\n",
+        "- Create a custom container training job in Vertex AI and run it.\n",
+        "- Evaluate the model generated from the training job.\n",
+        "- Create a model resource for the trained model in Vertex AI Model Registry.\n",
+        "- Run a Vertex AI batch prediction job.\n",
+        "- Deploy the model resource to a Vertex AI endpoint.\n",
+        "- Run a online prediction job on the model resource.\n",
+        "- Clean up the resources created."
       ]
     },
     {
@@ -130,15 +132,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "iMHz63rPbq6P"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -150,106 +159,124 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade google-cloud-aiplatform \\\n",
-        "                         google-cloud-storage -q\n",
+        "                         google-cloud-storage \\\n",
+        "                         opencv-python-headless \\\n",
+        "                         tensorflow==2.15.1 -q\n",
         "\n",
         "import os\n",
         "if os.getenv(\"IS_TESTING\"):\n",
         "    ! apt-get update && apt-get install -y python3-opencv-headless\n",
         "    ! apt-get install -y libgl1-mesa-dev\n",
         "    ! pip3 install --upgrade opencv-python-headless -q\n",
-        "    ! pip3 install tensorflow==2.9 -q"
+        "    ! pip3 install tensorflow==2.15.1 -q"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "#### Set your project ID\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f82e28c631cc"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91842ef41bbd"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "294fe4e5a671"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "region"
+        "id": "0bd7a5e762cc"
       },
       "source": [
-        "#### Set the region\n",
-        "\n",
-        "**Optional**: Update the 'REGION' variable to specify the region that you want to use. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+        "### UUID\n",
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "nsN5NJKSu-GU"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "timestamp"
+        "id": "3ee72715c0fd"
       },
       "outputs": [],
       "source": [
@@ -265,67 +292,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "To authenticate your Google Cloud account, follow the instructions for your Jupyter environment:\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "<br>You are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -365,7 +331,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -416,7 +382,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -434,9 +400,9 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you would specify:\n",
         "\n",
-        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
@@ -519,7 +485,7 @@
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs*."
       ]
     },
     {
@@ -530,6 +496,8 @@
       },
       "outputs": [],
       "source": [
+        "import os\n",
+        "\n",
         "MACHINE_TYPE = \"n1-standard\"\n",
         "\n",
         "VCPU = \"4\"\n",
@@ -753,7 +721,7 @@
         "For this step, your Dockerfile does:\n",
         "1. Install a pre-defined container image from TensorFlow repository for deep learning images.\n",
         "2. Copies in the Python training code, to be shown subsequently.\n",
-        "3. Sets the entry into the Python training script as `trainer/task.py`. Note that the `.py` is dropped in the ENTRYPOINT command, as it is implied."
+        "3. Sets the entry into the Python training script as `trainer/task.py`. Note that the `.py` is dropped in the ENTRYPOINT command, as it's implied."
       ]
     },
     {
@@ -831,7 +799,7 @@
       "source": [
         "REPOSITORY = \"my-docker-repo\"\n",
         "\n",
-        "! gcloud artifacts repositories create {REPOSITORY} --repository-format=docker --location={REGION} --description=\"Docker repository\"\n",
+        "! gcloud artifacts repositories create {REPOSITORY} --repository-format=docker --location={LOCATION} --description=\"Docker repository\"\n",
         "\n",
         "! gcloud artifacts repositories list"
       ]
@@ -863,7 +831,7 @@
         "\n",
         "TAG = \"latest\"\n",
         "TRAIN_IMAGE = (\n",
-        "    f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{CONTAINER_NAME}:{TAG}\"\n",
+        "    f\"{LOCATION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{CONTAINER_NAME}:{TAG}\"\n",
         ")"
       ]
     },
@@ -898,7 +866,7 @@
       "outputs": [],
       "source": [
         "%cd custom\n",
-        "!gcloud builds submit --region={REGION} --tag=$TRAIN_IMAGE\n",
+        "!gcloud builds submit --region={LOCATION} --tag=$TRAIN_IMAGE\n",
         "%cd .."
       ]
     },
@@ -957,7 +925,7 @@
       "outputs": [],
       "source": [
         "job = aiplatform.CustomContainerTrainingJob(\n",
-        "    display_name=\"cifar10_\" + UUID, container_uri=TRAIN_IMAGE\n",
+        "    display_name=\"cifar10_\" + \"unique\", container_uri=TRAIN_IMAGE\n",
         ")\n",
         "\n",
         "print(job)"
@@ -1079,7 +1047,7 @@
         "\n",
         "For model evaluation, you load the CIFAR10 test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the image data, and the corresponding labels.\n",
         "\n",
-        "You don't need the training data, and hence why it is loaded as `(_, _)`.\n",
+        "You don't need the training data, and hence why it's loaded as `(_, _)`.\n",
         "\n",
         "Before you run the data through evaluation, you need to preprocess it:\n",
         "\n",
@@ -1132,7 +1100,7 @@
       "source": [
         "## Serving function for image data\n",
         "\n",
-        "To pass images to the prediction service, you encode the compressed (e.g., JPEG) image bytes into base 64., which makes the content safe from modification while transmitting binary data over the network. Since this deployed model expects input data as raw (uncompressed) bytes, you need to ensure that the base 64 encoded data gets converted back to raw bytes before it is passed as input to the deployed model.\n",
+        "To pass images to the prediction service, you encode the compressed (e.g., JPEG) image bytes into base 64., which makes the content safe from modification while transmitting binary data over the network. Since this deployed model expects input data as raw (uncompressed) bytes, you need to ensure that the base 64 encoded data gets converted back to raw bytes before it's passed as input to the deployed model.\n",
         "\n",
         "To resolve this, define a serving function (`serving_fn`) and attach it to the model as a preprocessing step. Add a `@tf.function` decorator so the serving function is fused to the underlying model (instead of upstream on a CPU).\n",
         "\n",
@@ -1250,7 +1218,7 @@
       "outputs": [],
       "source": [
         "model = aiplatform.Model.upload(\n",
-        "    display_name=\"cifar10_\" + UUID,\n",
+        "    display_name=\"cifar10_\" + \"unique\",\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
         "    sync=False,\n",
@@ -1269,7 +1237,7 @@
         "\n",
         "### Get test items\n",
         "\n",
-        "You will use examples out of the test (holdout) portion of the dataset as a test items."
+        "You use examples out of the test (holdout) portion of the dataset as a test items."
       ]
     },
     {
@@ -1294,7 +1262,7 @@
       },
       "source": [
         "### Prepare the request content\n",
-        "You are going to send the CIFAR10 images as compressed JPG image, instead of the raw uncompressed bytes:\n",
+        "You're going to send the CIFAR10 images as compressed JPG image, instead of the raw uncompressed bytes:\n",
         "\n",
         "- `cv2.imwrite`: Use openCV to write the uncompressed image to disk as a compressed JPEG image.\n",
         " - Denormalize the image data from \\[0,1) range back to [0,255).\n",
@@ -1418,7 +1386,7 @@
         "MAX_NODES = 1\n",
         "\n",
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"cifar10_\" + UUID,\n",
+        "    job_display_name=\"cifar10_\" + \"unique\",\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"jsonl\",\n",
@@ -1535,7 +1503,7 @@
       },
       "outputs": [],
       "source": [
-        "DEPLOYED_NAME = \"cifar10-\" + UUID\n",
+        "DEPLOYED_NAME = \"cifar10-\" + \"unique\"\n",
         "\n",
         "TRAFFIC_SPLIT = {\"0\": 100}\n",
         "\n",
@@ -1671,7 +1639,7 @@
       "source": [
         "## Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you're done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -1709,8 +1677,6 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
-        "\n",
         "# Delete the model using the Vertex model object\n",
         "model.delete()\n",
         "\n",
@@ -1724,10 +1690,19 @@
         "batch_predict_job.delete()\n",
         "\n",
         "# Delete artifact repository\n",
-        "! gcloud artifacts repositories delete $REPOSITORY --location=$REGION --quiet\n",
+        "! gcloud artifacts repositories delete $REPOSITORY --location=$LOCATION --quiet\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "# remove the local users and movies avro files\n",
+        "! rm -rf custom\n",
+        "! rm -f custom.tar.gz\n",
+        "! rm tmp.jpg\n",
+        "! rm tmp1.jpg\n",
+        "! rm tmp2.jpg"
       ]
     }
   ],

notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb

@@ -32,26 +32,27 @@
         "# Vertex AI Migration: Custom image classification with a pre-built training container\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-custom-image-classification-prebuilt-container.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "</table>"
       ]
     },
     {
@@ -64,7 +65,7 @@
         "\n",
         "This tutorial demonstrates how to use the Vertex AI SDK for Python to train using a pre-built container and deploy a custom image classification model for online and batch prediction.\n",
         "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training overview](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
       ]
     },
     {
@@ -81,22 +82,21 @@
         "\n",
         "- Vertex AI Training\n",
         "- Vertex AI Model Registry\n",
-        "- Vertex AI Predictions\n",
-        "- Vertex AI Batch Predictions\n",
-        "- Vertex AI Endpoints\n",
+        "- Vertex AI batch predictions\n",
+        "- Vertex AI endpoints\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- *Package the training code into a python application.*\n",
-        "- *Containerize the training application using Cloud Build and Artifact Registry.*\n",
-        "- *Create a custom container training job in Vertex AI and run it.*\n",
-        "- *Evaluate the model generated from the training job.*\n",
-        "- *Create a model resource for the trained model in Vertex AI Model Registry.*\n",
-        "- *Run a Vertex AI batch prediction job.*\n",
-        "- *Deploy the model resource to a Vertex AI Endpoint.*\n",
-        "- *Run a online prediction job on the model resource.*\n",
-        "- *Clean up the resources created.*"
+        "- Package the training code into a python application.\n",
+        "- Containerize the training application using Cloud Build and Artifact Registry.\n",
+        "- Create a custom container training job in Vertex AI and run it.\n",
+        "- Evaluate the model generated from the training job.\n",
+        "- Create a model resource for the trained model in Vertex AI Model Registry.\n",
+        "- Run a Vertex AI batch prediction job.\n",
+        "- Deploy the model resource to a Vertex AI endpoint.\n",
+        "- Run a online prediction job on the model resource.\n",
+        "- Clean up the resources created."
       ]
     },
     {
@@ -107,7 +107,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck."
+        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you use in this tutorial is the one that's available from TensorFlow SDK. The trained model classifies images into one of ten categories: *airplane*, *automobile*, *bird*, *cat*, *deer*, *dog*, *frog*, *horse*, *ship*, or *truck*."
       ]
     },
     {
@@ -133,12 +133,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -154,54 +161,86 @@
         "! apt-get update && apt-get install -y python3-opencv-headless\n",
         "! apt-get install -y libgl1-mesa-dev\n",
         "! pip3 install --upgrade opencv-python-headless  \n",
-        "! pip3 install --upgrade tensorflow  "
+        "! pip3 install --upgrade tensorflow==2.15.1  "
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -213,122 +252,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "e87d5856317d"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -357,7 +286,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -368,7 +297,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -377,9 +306,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -391,7 +317,10 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip"
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from google.cloud import aiplatform\n",
+        "from tensorflow.keras.datasets import cifar10"
       ]
     },
     {
@@ -400,7 +329,7 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex SDK for Python\n",
+        "### Initialize Vertex SDK for Python\n",
         "\n",
         "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
@@ -413,7 +342,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -426,16 +355,16 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you'd specify:\n",
         "\n",
-        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
-        "Learn more [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your region\n",
+        "Learn more [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your location\n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "**Note**: TF releases before 2.3 for GPU support fail to load the custom model in this tutorial. It's a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -504,19 +433,19 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs used for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -544,11 +473,12 @@
         "id": "examine_training_package"
       },
       "source": [
-        "### Examine the training package\n",
+        "## Tutorial\n",
         "\n",
-        "#### Package layout\n",
         "\n",
-        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "### Package layout\n",
+        "\n",
+        "Before you start the training, take a look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -560,11 +490,11 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*: When `trainer/task.py` is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (`.py`) is dropped (`trainer.task`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you assemble the training package."
+        "In the following cells, assemble the training package."
       ]
     },
     {
@@ -602,11 +532,13 @@
         "id": "taskpy_contents:cifar10"
       },
       "source": [
-        "#### Task.py contents\n",
+        "### Task.py contents\n",
         "\n",
-        "In the next cell, you write the contents of the training script task.py. We won't go into detail, it's just there for you to browse. In summary:\n",
+        "In the next cell, write the contents of the training script *task.py*.\n",
         "\n",
-        "- Get the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
+        "To summarize, the script performs the following steps:\n",
+        "\n",
+        "- Gets the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
         "- Loads CIFAR10 dataset from TF Datasets (tfds).\n",
         "- Builds a model using TF.Keras model API.\n",
         "- Compiles the model (`compile()`).\n",
@@ -730,9 +662,9 @@
         "id": "tarball_training_script"
       },
       "source": [
-        "#### Store training script on your Cloud Storage bucket\n",
+        "### Store training script on your Cloud Storage bucket\n",
         "\n",
-        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
     },
     {
@@ -755,16 +687,13 @@
         "id": "train_a_model:migration"
       },
       "source": [
-        "## Train a model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "custom_create:migration,new,mbsdk,prebuilt"
-      },
-      "source": [
-        "### [training.create-python-pre-built-container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)"
+        "### Create and run custom training job\n",
+        "\n",
+        "Learn more about how to [Create a Python training application for a prebuilt container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)\n",
+        "\n",
+        "To train a custom model, you perform two steps:\n",
+        "1) Create a custom training job.\n",
+        "2) Specify your training parameters and run the job."
       ]
     },
     {
@@ -773,14 +702,9 @@
         "id": "create_custom_training_job:mbsdk,no_model"
       },
       "source": [
-        "### Create and run custom training job\n",
-        "\n",
-        "\n",
-        "To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
-        "\n",
         "#### Create custom training job\n",
         "\n",
-        "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
+        "A custom training job is created using the `CustomTrainingJob` class, with the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the custom training job.\n",
         "- `container_uri`: The training container image.\n",
@@ -796,8 +720,8 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.CustomTrainingJob(\n",
-        "    display_name=\"cifar10_\" + UUID,\n",
+        "job = aiplatform.CustomTrainingJob(\n",
+        "    display_name=\"cifar10-unique\",\n",
         "    script_path=\"custom/trainer/task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
         "    requirements=[\"gcsfs==0.7.1\", \"tensorflow-datasets==4.4\"],\n",
@@ -825,7 +749,7 @@
       "source": [
         "#### Run the custom training job\n",
         "\n",
-        "Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, run the custom job to start the training job by invoking the `run()` method, with the following parameters:\n",
         "\n",
         "- `args`: The command-line arguments to pass to the training script.\n",
         "- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
@@ -833,7 +757,7 @@
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
         "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
-        "- `sync`: Whether to block until completion of the job."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -844,7 +768,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, UUID)\n",
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, \"unique\")\n",
         "\n",
         "EPOCHS = 20\n",
         "STEPS = 100\n",
@@ -890,18 +814,9 @@
         "id": "run_custom_job:mbsdk,no_model"
       },
       "source": [
-        "### Wait for completion of custom training job\n",
+        "#### Wait for completion of custom training job\n",
         "\n",
-        "Next, wait for the custom training job to complete. Alternatively, one can set the parameter `sync` to `True` in the `run()` methid to block until the custom training job is completed."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "evaluate_the_model:migration"
-      },
-      "source": [
-        "## Evaluate the model"
+        "Next, wait for the custom training job to complete. Alternatively, you can set the parameter `sync` to `True` in the `run()` method to block until the custom training job is completed."
       ]
     },
     {
@@ -910,11 +825,11 @@
         "id": "load_saved_model"
       },
       "source": [
-        "## Load the saved model\n",
+        "### Load the saved model\n",
         "\n",
-        "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Now load it from the Cloud Storage bucket, and then you can do some things, like evaluate the model, and do a prediction.\n",
+        "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Now, load the model from the Cloud Storage bucket and run model evaluation, preparing it for prediction requests.\n",
         "\n",
-        "To load, you use the TF.Keras `model.load_model()` method passing it the Cloud Storage path where the model is saved -- specified by `MODEL_DIR`."
+        "To load, use the TF.Keras `model.load_model()` method passing it the Cloud Storage path where the model is saved -- specified by `MODEL_DIR`."
       ]
     },
     {
@@ -925,8 +840,6 @@
       },
       "outputs": [],
       "source": [
-        "import tensorflow as tf\n",
-        "\n",
         "local_model = tf.keras.models.load_model(MODEL_DIR)"
       ]
     },
@@ -936,23 +849,21 @@
         "id": "evaluate_custom_model:image"
       },
       "source": [
-        "## Evaluate the model\n",
+        "### Evaluate the model\n",
         "\n",
         "Now find out how good the model is.\n",
         "\n",
-        "### Load evaluation data\n",
+        "#### Load evaluation data\n",
         "\n",
-        "You will load the CIFAR10 test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the image data, and the corresponding labels.\n",
+        "Load the CIFAR10 test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the image data, and the corresponding labels.\n",
         "\n",
-        "You don't need the training data, and hence why we loaded it as `(_, _)`.\n",
+        "You don't need the training data, and hence load it as `(_, _)`.\n",
         "\n",
         "Before you can run the data through evaluation, you need to preprocess it:\n",
         "\n",
-        "`x_test`:\n",
-        "1. Normalize (rescale) the pixel data by dividing each pixel by 255. This replaces each single byte integer pixel with a 32-bit floating point number between 0 and 1.\n",
+        "`x_test`: Normalize (rescale) the pixel data by dividing each pixel by 255. This replaces each single byte integer pixel with a 32-bit floating point number between 0 and 1.\n",
         "\n",
-        "`y_test`:<br/>\n",
-        "2. The labels are currently scalar (sparse). If you look back at the `compile()` step in the `trainer/task.py` script, you will find that it was compiled for sparse labels. So we don't need to do anything more."
+        "`y_test`: The labels are currently scalar (sparse). If you look back at the `compile()` step in the `trainer/task.py` script, you can find that it was compiled for sparse labels. So we don't need to do anything more."
       ]
     },
     {
@@ -963,9 +874,6 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\n",
-        "from tensorflow.keras.datasets import cifar10\n",
-        "\n",
         "(_, _), (x_test, y_test) = cifar10.load_data()\n",
         "x_test = (x_test / 255.0).astype(np.float32)\n",
         "\n",
@@ -978,9 +886,9 @@
         "id": "perform_evaluation_custom"
       },
       "source": [
-        "### Perform the model evaluation\n",
+        "#### Perform the model evaluation\n",
         "\n",
-        "Now evaluate how well the model in the custom job did."
+        "Use the model's `evaluate()` method to perform the evaluation."
       ]
     },
     {
@@ -1000,7 +908,9 @@
         "id": "import_model:migration,new"
       },
       "source": [
-        "### [general.import-model](https://cloud.google.com/vertex-ai/docs/general/import-model)"
+        "### Import models to Vertex\n",
+        "\n",
+        "Learn more on how to [Import models to Vertex](https://cloud.google.com/vertex-ai/docs/general/import-model)"
       ]
     },
     {
@@ -1009,11 +919,11 @@
         "id": "serving_function_image"
       },
       "source": [
-        "### Serving function for image data\n",
+        "#### Serving function for image data\n",
         "\n",
-        "To pass images to the prediction service, you encode the compressed (e.g., JPEG) image bytes into base 64 -- which makes the content safe from modification while transmitting binary data over the network. Since this deployed model expects input data as raw (uncompressed) bytes, you need to ensure that the base 64 encoded data gets converted back to raw bytes before it is passed as input to the deployed model.\n",
+        "To pass images to the prediction service, encode the compressed (e.g., JPEG) image bytes into base 64 -- which makes the content safe from modification while transmitting binary data over the network. Since this deployed model expects input data as raw (uncompressed) bytes, you need to ensure that the base 64 encoded data gets converted back to raw bytes before it's passed as input to the deployed model.\n",
         "\n",
-        "To resolve this, define a serving function (`serving_fn`) and attach it to the model as a preprocessing step. Add a `@tf.function` decorator so the serving function is fused to the underlying model (instead of upstream on a CPU).\n",
+        "To resolve this, define a serving function (`serving_fn`) and attach it to the model as a preprocessing step. Add a `@tf.function` decorator so the serving function is used by the underlying model (instead of upstream on a CPU).\n",
         "\n",
         "When you send a prediction or explanation request, the content of the request is base 64 decoded into a Tensorflow string (`tf.string`), which is passed to the serving function (`serving_fn`). The serving function preprocesses the `tf.string` into raw (uncompressed) numpy bytes (`preprocess_fn`) to match the input requirements of the model:\n",
         "- `io.decode_jpeg`- Decompresses the JPG image which is returned as a Tensorflow tensor with three channels (RGB).\n",
@@ -1075,13 +985,13 @@
         "id": "serving_function_signature:image"
       },
       "source": [
-        "## Get the serving function signature\n",
+        "#### Get the serving function signature\n",
         "\n",
         "You can get the signatures of your model's input and output layers by reloading the model into memory, and querying it for the signatures corresponding to each layer.\n",
         "\n",
-        "For your purpose, you need the signature of the serving function. Why? Well, when we send our data for prediction as a HTTP request packet, the image data is base64 encoded, and our TF.Keras model takes numpy input. Your serving function will do the conversion from base64 to a numpy array.\n",
+        "For this purpose, you need the signature of the serving function. When the data is sent for prediction as an HTTP request packet, the image data is base64 encoded, and the TF.Keras model takes numpy input. Your serving function does the conversion from base64 to a numpy array.\n",
         "\n",
-        "When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which you will use later when you make a prediction request."
+        "When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which is used later when you make a prediction request."
       ]
     },
     {
@@ -1106,14 +1016,14 @@
         "id": "upload_model:mbsdk"
       },
       "source": [
-        "## Upload the model\n",
+        "### Upload the model\n",
         "\n",
-        "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
+        "Next, upload your model to a model resource using `Model.upload()` method, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Model` resource.\n",
+        "- `display_name`: The human readable name for the model resource.\n",
         "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
         "- `serving_container_image_uri`: The serving container image.\n",
-        "- `sync`: Whether to execute the upload asynchronously or synchronously.\n",
+        "- `sync`: Set **True** to wait until the completion of the job.\n",
         "\n",
         "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
       ]
@@ -1126,8 +1036,8 @@
       },
       "outputs": [],
       "source": [
-        "model = aip.Model.upload(\n",
-        "    display_name=\"cifar10_\" + UUID,\n",
+        "model = aiplatform.Model.upload(\n",
+        "    display_name=\"cifar10-unique\",\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
         "    sync=False,\n",
@@ -1157,16 +1067,9 @@
         "id": "make_batch_predictions:migration"
       },
       "source": [
-        "## Make batch predictions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "batchpredictionjobs_create:migration,new,mbsdk"
-      },
-      "source": [
-        "### [predictions.batch-prediction](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
+        "### Generate batch predictions\n",
+        "\n",
+        "Here is the [Overview of getting predictions on Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/overview)"
       ]
     },
     {
@@ -1175,9 +1078,9 @@
         "id": "get_test_items:test"
       },
       "source": [
-        "### Get test items\n",
+        "#### Get test items\n",
         "\n",
-        "You will use examples out of the test (holdout) portion of the dataset as a test items."
+        "Use examples out of the test (holdout) portion of the dataset as a test items."
       ]
     },
     {
@@ -1201,8 +1104,8 @@
         "id": "prepare_test_items:test,image"
       },
       "source": [
-        "### Prepare the request content\n",
-        "You are going to send the CIFAR10 images as compressed JPG image, instead of the raw uncompressed bytes:\n",
+        "#### Prepare the request content\n",
+        "You're going to send the CIFAR10 images as compressed JPG image, instead of the raw uncompressed bytes:\n",
         "\n",
         "- `cv2.imwrite`: Use openCV to write the uncompressed image to disk as a compressed JPEG image.\n",
         " - Denormalize the image data from \\[0,1) range back to [0,255).\n",
@@ -1229,7 +1132,7 @@
         "id": "copy_test_items:test"
       },
       "source": [
-        "### Copy test item(s)\n",
+        "#### Copy test item(s)\n",
         "\n",
         "For the batch prediction, copy the test items over to your Cloud Storage bucket."
       ]
@@ -1255,9 +1158,9 @@
         "id": "make_batch_file:custom,image"
       },
       "source": [
-        "### Make the batch input file\n",
+        "#### Create batch input file\n",
         "\n",
-        "Now make a batch input file, which you will store in your local Cloud Storage bucket. The batch input file can only be in JSONL format. For JSONL file, you make one dictionary entry per line for each data item (instance). The dictionary contains the key/value pairs:\n",
+        "Now create batch input file, and store it in your local Cloud Storage bucket. The batch input file can only be in JSONL format. For JSONL file, make one dictionary entry per line for each data item (instance). The dictionary contains the key/value pairs:\n",
         "\n",
         "- `input_name`: the name of the input layer of the underlying model.\n",
         "- `'b64'`: A key that indicates the content is base64 encoded.\n",
@@ -1267,7 +1170,7 @@
         "\n",
         "                        {serving_input: {'b64': content}}\n",
         "\n",
-        "To pass the image data to the prediction service you encode the bytes into base64 -- which makes the content safe from modification when transmitting binary data over the network.\n",
+        "To pass the image data to the prediction service encode the bytes into base64 -- which makes the content safe from modification when transmitting binary data over the network.\n",
         "\n",
         "- `tf.io.read_file`: Read the compressed JPG images into memory as raw bytes.\n",
         "- `base64.b64encode`: Encode the raw bytes into a base64 encoded string."
@@ -1302,19 +1205,19 @@
         "id": "batch_request:mbsdk,jsonl,custom"
       },
       "source": [
-        "### Make the batch prediction request\n",
+        "#### Make the batch prediction request\n",
         "\n",
-        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
+        "Now that your model resource is trained, you can make a batch prediction by invoking the `batch_predict()` method, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
         "- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
-        "- `instances_format`: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
-        "- `predictions_format`: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
+        "- `instances_format`: The format for the input instances, either *csv* or *jsonl*. Defaults to *jsonl*.\n",
+        "- `predictions_format`: The format for the output predictions, either *csv* or *jsonl*. Defaults to *jsonl*.\n",
         "- `machine_type`: The type of machine to use for training.\n",
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -1329,7 +1232,7 @@
         "MAX_NODES = 1\n",
         "\n",
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"cifar10_\" + UUID,\n",
+        "    job_display_name=\"cifar10-unique\",\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"jsonl\",\n",
@@ -1355,14 +1258,8 @@
         "*Example output:*\n",
         "\n",
         "    INFO:google.cloud.aiplatform.jobs:Creating BatchPredictionJob\n",
-        "    <google.cloud.aiplatform.jobs.BatchPredictionJob object at 0x7f806a6112d0> is waiting for upstream dependencies to complete.\n",
-        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296\n",
-        "    INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
-        "    INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296')\n",
-        "    INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
-        "    https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/5110965452507447296?project=759209241365\n",
-        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296 current state:\n",
-        "    JobState.JOB_STATE_RUNNING"
+        "    <google.cloud.aiplatform.jobs.BatchPredictionJob object at 0x7874ad735a80> is waiting for upstream dependencies to complete.\n",
+        "    "
       ]
     },
     {
@@ -1371,9 +1268,9 @@
         "id": "batch_request_wait:mbsdk"
       },
       "source": [
-        "### Wait for completion of batch prediction job\n",
+        "#### Wait for completion of batch prediction job\n",
         "\n",
-        "Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
+        "Next, wait for the batch job to complete. Alternatively, you can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
       ]
     },
     {
@@ -1395,11 +1292,6 @@
       "source": [
         "*Example Output:*\n",
         "\n",
-        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328\n",
-        "    INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
-        "    INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328')\n",
-        "    INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
-        "    https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/181835033978339328?project=759209241365\n",
         "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
         "    JobState.JOB_STATE_RUNNING\n",
         "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
@@ -1431,7 +1323,7 @@
         "\n",
         "Next, get the results from the completed batch prediction job.\n",
         "\n",
-        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
+        "The results are written to the Cloud Storage output bucket specified in the batch prediction request. Call the method `iter_outputs()` to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
         "\n",
         "- `instance`: The prediction request.\n",
         "- `prediction`: The prediction response."
@@ -1481,16 +1373,7 @@
         "id": "make_online_predictions:migration"
       },
       "source": [
-        "## Make online predictions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "deploy_model:migration,new,mbsdk"
-      },
-      "source": [
-        "### [predictions.deploy-model-api](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
+        "### Generate online predictions \n"
       ]
     },
     {
@@ -1499,13 +1382,13 @@
         "id": "deploy_model:mbsdk,all"
       },
       "source": [
-        "## Deploy the model\n",
+        "#### Deploy the model\n",
         "\n",
-        "Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method, with the following parameters:\n",
+        "Next, deploy your model for online prediction. To deploy the model, invoke the `deploy()` method, with the following parameters:\n",
         "\n",
         "- `deployed_model_display_name`: A human readable name for the deployed model.\n",
         "- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
-        "If only one model, then specify as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
+        "If there is only one model, then specify `traffic_split` as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
         "If there are existing models on the endpoint, for which the traffic will be split, then use model_id to specify as { \"0\": percent, model_id: percent, ... }, where model_id is the model id of an existing model to the deployed endpoint. The percents must add up to 100.\n",
         "- `machine_type`: The type of machine to use for training.\n",
         "- `accelerator_type`: The hardware accelerator type.\n",
@@ -1522,7 +1405,7 @@
       },
       "outputs": [],
       "source": [
-        "DEPLOYED_NAME = \"cifar10-\" + UUID\n",
+        "DEPLOYED_NAME = \"cifar10-unique\"\n",
         "\n",
         "TRAFFIC_SPLIT = {\"0\": 100}\n",
         "\n",
@@ -1575,7 +1458,7 @@
         "id": "endpoints_predict:migration,new,mbsdk"
       },
       "source": [
-        "### [predictions.online-prediction-automl](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-automl)"
+        "Learn more about how to [Train and use your own models](https://cloud.google.com/vertex-ai/docs/training-overview)"
       ]
     },
     {
@@ -1586,7 +1469,7 @@
       "source": [
         "### Get test item\n",
         "\n",
-        "You will use an example out of the test (holdout) portion of the dataset as a test item."
+        "Use an example from the test (holdout) portion of the dataset as a test item."
       ]
     },
     {
@@ -1608,8 +1491,8 @@
         "id": "prepare_test_item:test,image"
       },
       "source": [
-        "### Prepare the request content\n",
-        "You are going to send the CIFAR10 image as compressed JPG image, instead of the raw uncompressed bytes:\n",
+        "#### Prepare the request content\n",
+        "You're going to send the CIFAR10 image as compressed JPG image, instead of the raw uncompressed bytes:\n",
         "\n",
         "- `cv2.imwrite`: Use openCV to write the uncompressed image to disk as a compressed JPEG image.\n",
         " - Denormalize the image data from \\[0,1) range back to [0,255).\n",
@@ -1642,13 +1525,13 @@
         "id": "predict_request:mbsdk,custom,icn"
       },
       "source": [
-        "### Make the prediction\n",
+        "### Get the prediction\n",
         "\n",
-        "Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the Endpoint resource.\n",
+        "Now that your model resource is deployed to an endpoint resource, you can get online predictions by sending prediction requests to the endpoint resource.\n",
         "\n",
         "#### Request\n",
         "\n",
-        "Since in this example your test item is in a Cloud Storage bucket, you open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
+        "Since in this example your test item is in a Cloud Storage bucket, open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you need to encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
         "\n",
         "The format of each instance is:\n",
         "\n",
@@ -1662,7 +1545,7 @@
         "\n",
         "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
         "- `predictions`: The predicted confidence, between 0 and 1, per class label.\n",
-        "- `deployed_model_id`: The Vertex AI identifier for the deployed `Model` resource which did the predictions."
+        "- `deployed_model_id`: The Vertex AI identifier for the deployed model resource which did the predictions."
       ]
     },
     {
@@ -1700,7 +1583,7 @@
       "source": [
         "## Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you're done doing predictions, undeploy the model from the endpoint resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -1720,7 +1603,7 @@
         "id": "cleanup:mbsdk"
       },
       "source": [
-        "# Cleaning up\n",
+        "## Cleaning up\n",
         "\n",
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
@@ -1736,10 +1619,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "delete_bucket = False\n",
-        "\n",
         "# Delete the model using the Vertex model object\n",
         "model.delete()\n",
         "\n",
@@ -1752,7 +1631,12 @@
         "# Delete the batch prediction job using the Vertex batch prediction object\n",
         "batch_predict_job.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# delete locally generated files\n",
+        "! rm -rf custom custom.tar.gz tmp1.jpg tmp2.jpg tmp.jpg\n",
+        "\n",
+        "# delete cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb

@@ -32,25 +32,27 @@
         "# Vertex AI Migration: Custom Scikit-Learn model with pre-built training container\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-custom-scikit-learn-prebuilt-container.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "     <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>         \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -64,7 +66,7 @@
         "\n",
         "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom tabular classification scikit-learn model for batch prediction.\n",
         "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training overview](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
       ]
     },
     {
@@ -75,25 +77,26 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.\n",
+        "In this tutorial, you learn how to use Vertex AI Training to create a custom trained model. Then, you learn to use Vertex AI batch prediction to generate batch prediction on the trained model.\n",
         "\n",
         "\n",
         "You learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then do a prediction on the deployed model by sending data.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Batch Prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI` custom job for training a scikit-learn model.\n",
-        "- Upload the trained model artifacts as a `Model` resource.\n",
-        "- Make a batch prediction.\n",
-        "- Deploy model to a endpoint\n",
-        "- Make a online prediction"
+        "- Create a Vertex AI custom job for training a scikit-learn model.\n",
+        "- Upload the trained model artifacts as a model resource.\n",
+        "- Generate batch predictions.\n",
+        "- Deploy the model resource to a serving endpoint resource.\n",
+        "- Generate online predictions.\n",
+        "- Undeploy the model resource. "
       ]
     },
     {
@@ -150,54 +153,86 @@
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
         "                                 google-cloud-storage \\\n",
-        "                                 tensorflow"
+        "                                 tensorflow==2.15.1"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -209,33 +244,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -244,7 +258,7 @@
       "source": [
         "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -267,64 +281,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -364,7 +320,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -387,8 +343,8 @@
         "import json\n",
         "import os\n",
         "\n",
-        "import google.cloud.aiplatform as aip\n",
-        "import tensorflow as tf"
+        "import tensorflow as tf\n",
+        "from google.cloud import aiplatform"
       ]
     },
     {
@@ -397,7 +353,7 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex SDK for Python\n",
+        "### Initialize Vertex AI SDK for Python\n",
         "\n",
         "Initialize the Vertex SDK for Python for your project and corresponding bucket."
       ]
@@ -410,7 +366,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -424,10 +380,10 @@
         "Set the pre-built Docker container image for training and prediction.\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
+        "For the latest list, see [Pre-built containers for custom training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+        "For the latest list, see [Pre-built containers for prediction and explanation](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
       ]
     },
     {
@@ -458,19 +414,19 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure the compute resources for the VMs used for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You can also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -506,11 +462,13 @@
         "id": "examine_training_package"
       },
       "source": [
-        "### Examine the training package\n",
+        "### Tutorial\n",
+        "\n",
+        "Now you're ready to create your own custom model and training US census data.\n",
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the training, you look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -522,11 +480,11 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when trainer/task.py is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (.py) is dropped (`trainer.task`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you assemble the training package."
+        "In the following cells, assemble the training package."
       ]
     },
     {
@@ -636,13 +594,13 @@
         "with open('./adult.data', 'r') as train_data:\n",
         "    raw_training_data = pd.read_csv(train_data, header=None, names=COLUMNS)\n",
         "\n",
-        "# Remove the column we are trying to predict ('income-level') from our features list\n",
+        "# Remove the column you're trying to predict ('income-level') from our features list\n",
         "# Convert the Dataframe to a lists of lists\n",
         "train_features = raw_training_data.drop('income-level', axis=1).values.tolist()\n",
         "# Create our training labels list, convert the Dataframe to a lists of lists\n",
         "train_labels = (raw_training_data['income-level'] == ' >50K').values.tolist()\n",
         "\n",
-        "# Since the census data set has categorical features, we need to convert\n",
+        "# Since the census data set has categorical features, you need to convert\n",
         "# them to numerical values. We'll use a list of pipelines to convert each\n",
         "# categorical column and then use FeatureUnion to combine them before calling\n",
         "# the RandomForestClassifier.\n",
@@ -652,7 +610,7 @@
         "# To do this, each categorical column will use a pipeline that extracts one feature column via\n",
         "# SelectKBest(k=1) and a LabelBinarizer() to convert the categorical value to a numerical one.\n",
         "# A scores array (created below) will select and extract the feature column. The scores array is\n",
-        "# created by iterating over the COLUMNS and checking if it is a CATEGORICAL_COLUMN.\n",
+        "# created by iterating over the COLUMNS and checking if it's a CATEGORICAL_COLUMN.\n",
         "for i, col in enumerate(COLUMNS[:-1]):\n",
         "    if col in CATEGORICAL_COLUMNS:\n",
         "        # Create a scores array to get the individual categorical column.\n",
@@ -664,7 +622,7 @@
         "        # Returns: [['State-gov']]\n",
         "        # Build the scores array.\n",
         "        scores = [0] * len(COLUMNS[:-1])\n",
-        "        # This column is the categorical column we want to extract.\n",
+        "        # This column is the categorical column you want to extract.\n",
         "        scores[i] = 1\n",
         "        skb = SelectKBest(k=1)\n",
         "        skb.scores_ = scores\n",
@@ -725,9 +683,9 @@
         "id": "tarball_training_script"
       },
       "source": [
-        "#### Store training script on your Cloud Storage bucket\n",
+        "### Store training script on your Cloud Storage bucket\n",
         "\n",
-        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
     },
     {
@@ -750,7 +708,7 @@
         "id": "train_a_model:migration"
       },
       "source": [
-        "## Train a model"
+        "### Create and run custom training job"
       ]
     },
     {
@@ -759,7 +717,7 @@
         "id": "custom_create:migration,new,mbsdk,prebuilt"
       },
       "source": [
-        "### [training.create-python-pre-built-container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)"
+        "Learn how to [Create a Python training application for a prebuilt container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)"
       ]
     },
     {
@@ -768,14 +726,13 @@
         "id": "create_custom_training_job:mbsdk,no_model"
       },
       "source": [
-        "### Create and run custom training job\n",
-        "\n",
-        "\n",
-        "To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
+        "To train a custom model, you perform two steps: \n",
+        "1) Create a custom training job.\n",
+        "2) Specify your training parameters and run the job.\n",
         "\n",
         "#### Create custom training job\n",
         "\n",
-        "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
+        "A custom training job is created using the `CustomTrainingJob` class, with the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the custom training job.\n",
         "- `container_uri`: The training container image.\n",
@@ -791,7 +748,7 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.CustomTrainingJob(\n",
+        "job = aiplatform.CustomTrainingJob(\n",
         "    display_name=\"census_\" + UUID,\n",
         "    script_path=\"custom/trainer/task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
@@ -820,12 +777,12 @@
       "source": [
         "#### Run the custom training job\n",
         "\n",
-        "Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, run the custom job to start the training job by invoking the `run()` method, with the following parameters:\n",
         "\n",
         "- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
         "- `machine_type`: The machine type for the compute instances.\n",
-        "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
-        "- `sync`: Whether to block until completion of the job."
+        "- `base_output_dir`: The Cloud Storage location to write the model artifacts.\n",
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -850,28 +807,28 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "import_model:migration,new"
+        "id": "upload_model:mbsdk"
       },
       "source": [
-        "### [general.import-model](https://cloud.google.com/vertex-ai/docs/general/import-model)"
+        "### Upload the model\n",
+        "\n",
+        "Next, upload your model to a model resource using the `Model.upload()` method, with the following parameters:\n",
+        "\n",
+        "- `display_name`: The human readable name for the model resource.\n",
+        "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
+        "- `serving_container_image_uri`: The serving container image.\n",
+        "- `sync`: Set **True** to wait until the completion of the job.\n",
+        "\n",
+        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "upload_model:mbsdk"
+        "id": "import_model:migration,new"
       },
       "source": [
-        "## Upload the model\n",
-        "\n",
-        "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
-        "\n",
-        "- `display_name`: The human readable name for the `Model` resource.\n",
-        "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
-        "- `serving_container_image_uri`: The serving container image.\n",
-        "- `sync`: Whether to execute the upload asynchronously or synchronously.\n",
-        "\n",
-        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
+        " Learn more about how to [Import models to Vertex AI](https://cloud.google.com/vertex-ai/docs/general/import-model)"
       ]
     },
     {
@@ -882,7 +839,7 @@
       },
       "outputs": [],
       "source": [
-        "model = aip.Model.upload(\n",
+        "model = aiplatform.Model.upload(\n",
         "    display_name=\"census_\" + UUID,\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
@@ -913,7 +870,7 @@
         "id": "make_batch_predictions:migration"
       },
       "source": [
-        "## Make batch predictions"
+        "### Generate batch predictions"
       ]
     },
     {
@@ -922,7 +879,7 @@
         "id": "batchpredictionjobs_create:migration,new,mbsdk"
       },
       "source": [
-        "### [predictions.batch-prediction](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
+        "To learn more about batch predictions refer [Overview of getting predictions on Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
       ]
     },
     {
@@ -931,9 +888,9 @@
         "id": "make_test_items:scilearn,tabular,census"
       },
       "source": [
-        "### Make test items\n",
+        "#### Create test items\n",
         "\n",
-        "You use synthetic data as test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "Use synthetic data as test data items. Don’t be concerned about using synthetic data – as it's just for demonstration of generating predictions. "
       ]
     },
     {
@@ -986,9 +943,9 @@
         "id": "make_batch_file:custom,tabular,list,jsonl"
       },
       "source": [
-        "### Make the batch input file\n",
+        "#### Create a batch input file\n",
         "\n",
-        "Now make a batch input file, which you store in your local Cloud Storage bucket.  Each instance in the prediction request is a list of the form:\n",
+        "Now create a batch input file, which is stored in your local Cloud Storage bucket. Each instance in the prediction request is a list of the form:\n",
         "\n",
         "                        [ [ content_1], [content_2] ]\n",
         "\n",
@@ -1017,9 +974,9 @@
         "id": "batch_request:mbsdk,jsonl,custom,cpu"
       },
       "source": [
-        "### Make the batch prediction request\n",
+        "#### Make the batch prediction request\n",
         "\n",
-        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
+        "Now that your model resource is trained, you can make a batch prediction by invoking the `batch_predict()` method, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
@@ -1027,7 +984,7 @@
         "- `instances_format`: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
         "- `predictions_format`: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
         "- `machine_type`: The type of machine to use for training.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -1082,9 +1039,9 @@
         "id": "batch_request_wait:mbsdk"
       },
       "source": [
-        "### Wait for completion of batch prediction job\n",
+        "#### Wait for completion of batch prediction job\n",
         "\n",
-        "Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
+        "Next, wait for the batch job to complete. Alternatively, you can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
       ]
     },
     {
@@ -1138,11 +1095,11 @@
         "id": "get_batch_prediction:mbsdk,custom,lbn"
       },
       "source": [
-        "### Get the predictions\n",
+        "#### Get the predictions\n",
         "\n",
         "Next, get the results from the completed batch prediction job.\n",
         "\n",
-        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
+        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. Call the `iter_outputs()` to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
         "\n",
         "- `instance`: The prediction request.\n",
         "- `prediction`: The prediction response."
@@ -1189,7 +1146,7 @@
         "id": "make_online_predictions:migration"
       },
       "source": [
-        "## Make online predictions"
+        "### Generate online predictions"
       ]
     },
     {
@@ -1198,7 +1155,7 @@
         "id": "deploy_model:migration,new,mbsdk"
       },
       "source": [
-        "### [predictions.deploy-model-api](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
+        "To learn more of online predictions refer, [Overview of getting predictions on Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
       ]
     },
     {
@@ -1207,9 +1164,9 @@
         "id": "deploy_model:mbsdk,cpu"
       },
       "source": [
-        "## Deploy the model\n",
+        "#### Deploy the model\n",
         "\n",
-        "Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method, with the following parameters:\n",
+        "Next, deploy your model for online predictions. To deploy the model, invoke the `deploy` method, with the following parameters:\n",
         "\n",
         "- `deployed_model_display_name`: A human readable name for the deployed model.\n",
         "- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
@@ -1241,7 +1198,8 @@
         "    machine_type=DEPLOY_COMPUTE,\n",
         "    min_replica_count=MIN_NODES,\n",
         "    max_replica_count=MAX_NODES,\n",
-        ")"
+        ")\n",
+        "endpoint.wait()"
       ]
     },
     {
@@ -1268,9 +1226,9 @@
         "id": "make_test_item:scilearn,tabular,census"
       },
       "source": [
-        "### Make test item\n",
+        "#### Create a test item\n",
         "\n",
-        "You use synthetic data as a test data item. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "Use synthetic data as a test data item. Don’t be concerned about using synthetic data – since it's just for demonstration purposes"
       ]
     },
     {
@@ -1305,9 +1263,9 @@
         "id": "predict_request:mbsdk,custom,lbn"
       },
       "source": [
-        "### Make the prediction\n",
+        "#### Make the prediction\n",
         "\n",
-        "Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the `Endpoint` resource.\n",
+        "Now that your model resource is deployed to an endpoint resource, you can make online predictions by sending prediction requests to the endpoint resource.\n",
         "\n",
         "#### Request\n",
         "\n",
@@ -1315,15 +1273,15 @@
         "\n",
         "    [feature_list]\n",
         "\n",
-        "Since the predict() method can take multiple items (instances), send your single test item as a list of one test item.\n",
+        "Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
         "\n",
         "#### Response\n",
         "\n",
-        "The response from the predict() call is a Python dictionary with the following entries:\n",
+        "The response from the `predict()` call is a Python dictionary with the following entries:\n",
         "\n",
         "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
         "- `predictions`: The predicted confidence, between 0 and 1, per class label.\n",
-        "- `deployed_model_id`: The Vertex AI identifier for the deployed `Model` resource which did the predictions."
+        "- `deployed_model_id`: The Vertex AI identifier for the deployed model resource which did the predictions."
       ]
     },
     {
@@ -1357,9 +1315,9 @@
         "id": "undeploy_model:mbsdk"
       },
       "source": [
-        "## Undeploy the model\n",
+        "### Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you're done generating predictions, simply undeploy the model from the endpoint resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -1379,7 +1337,7 @@
         "id": "cleanup:mbsdk"
       },
       "source": [
-        "# Cleaning up\n",
+        "## Cleaning up\n",
         "\n",
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
@@ -1402,7 +1360,7 @@
       },
       "outputs": [],
       "source": [
-        "# delete endpoint\n",
+        "# Delete endpoint\n",
         "endpoint.delete()\n",
         "\n",
         "# Delete the model using the Vertex model object\n",
@@ -1414,8 +1372,12 @@
         "# Delete the batch prediction job using the Vertex batch prediction object\n",
         "batch_predict_job.delete()\n",
         "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete the locally generated files\n",
+        "! rm -rf custom custom.tar.gz\n",
+        "\n",
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb

@@ -32,25 +32,27 @@
         "# Vertex AI: Vertex AI Migration: Custom XGBoost model with pre-built training container\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmigration%2Fsdk-custom-xgboost-prebuilt-container.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "     <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                      \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -64,7 +66,7 @@
         "\n",
         "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom tabular classification XGBoost model for batch prediction.\n",
         "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training overview](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
       ]
     },
     {
@@ -75,25 +77,26 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.\n",
+        "In this tutorial, you learn to use Vertex AI Training to create a custom trained model. Then, you learn to use Vertex AI batch prediction to generate batch prediction on the trained model.\n",
         "\n",
         "\n",
         "You learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then do a prediction on the deployed model by sending data.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Batch Prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI batch prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI` custom job for training a scikit-learn model.\n",
-        "- Upload the trained model artifacts as a `Model` resource.\n",
-        "- Make a batch prediction.\n",
-        "- Deploy model to a endpoint\n",
-        "- Make a online prediction"
+        "- Create a Vertex AI custom job for training a xgboost model.\n",
+        "- Upload the trained model artifacts as a model resource.\n",
+        "- Generate batch predictions.\n",
+        "- Deploy the model resource to a serving endpoint resource.\n",
+        "- Generate online predictions.\n",
+        "- Undeploy the model resource."
       ]
     },
     {
@@ -104,7 +107,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Iris dataset](https://www.tensorflow.org/datasets/catalog/iris) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
+        "The dataset used for this tutorial is the [Iris dataset](https://www.tensorflow.org/datasets/catalog/iris) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset doesn't require any feature engineering. The version of the dataset used in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of Iris flower species from a class of three species: *setosa*, *virginica*, or *versicolor*."
       ]
     },
     {
@@ -130,12 +133,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -146,60 +156,88 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-storage\n",
-        "\n",
-        "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! pip3 install --upgrade --quiet tensorflow "
+        "                                 google-cloud-storage \\\n",
+        "                                 tensorflow==2.15.1"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -211,122 +249,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "e87d5856317d"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -366,7 +294,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -375,9 +303,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -389,7 +314,10 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip"
+        "import os\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from google.cloud import aiplatform"
       ]
     },
     {
@@ -398,7 +326,7 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex AI SDK for Python\n",
+        "### Initialize Vertex AI SDK for Python\n",
         "\n",
         "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
@@ -411,7 +339,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -425,10 +353,10 @@
         "Set the pre-built Docker container image for training and prediction.\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
+        "For the latest list, see [Pre-built containers for custom training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+        "For the latest list, see [Pre-built containers for prediction and explaination](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
       ]
     },
     {
@@ -456,19 +384,19 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure the compute resources for the VMs used for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You can also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -504,11 +432,11 @@
         "id": "examine_training_package"
       },
       "source": [
-        "### Examine the training package\n",
+        "### Tutorial\n",
         "\n",
-        "#### Package layout\n",
+        "#### Examine training package layout\n",
         "\n",
-        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, take a look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -518,13 +446,13 @@
         "  - \\_\\_init\\_\\_.py\n",
         "  - task.py\n",
         "\n",
-        "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
+        "The files *setup.cfg* and *setup.py* are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file *trainer/task.py* is the Python script for executing the custom training job. *Note*, When `trainer/task.py` is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (`.py`) is dropped (`trainer.task`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you will assemble the training package."
+        "In the following cells, assemble the training package."
       ]
     },
     {
@@ -586,7 +514,7 @@
         "iris_target_filename = 'iris_target.csv'\n",
         "data_dir = 'gs://cloud-samples-data/ai-platform/iris'\n",
         "\n",
-        "# gsutil outputs everything to stderr so we need to divert it to stdout.\n",
+        "# gsutil outputs everything to stderr so you need to divert it to stdout.\n",
         "subprocess.check_call(['gsutil', 'cp', os.path.join(data_dir,\n",
         "                                                    iris_data_filename),\n",
         "                       iris_data_filename], stderr=sys.stdout)\n",
@@ -628,7 +556,7 @@
       "source": [
         "#### Store training script on your Cloud Storage bucket\n",
         "\n",
-        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
     },
     {
@@ -645,24 +573,6 @@
         "! gsutil cp custom.tar.gz $BUCKET_URI/trainer_iris.tar.gz"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "train_a_model:migration"
-      },
-      "source": [
-        "## Train a model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "custom_create:migration,new,mbsdk,prebuilt"
-      },
-      "source": [
-        "### [training.create-python-pre-built-container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -671,12 +581,15 @@
       "source": [
         "### Create and run custom training job\n",
         "\n",
+        "Learn how to [Create a Python training application for a prebuilt container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container).\n",
         "\n",
-        "To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
+        "To train a custom model, you perform two steps:\n",
+        "1) Create a custom training job.\n",
+        "2) Specify your training parameters and run the job.\n",
         "\n",
         "#### Create custom training job\n",
         "\n",
-        "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
+        "A custom training job is created using the `CustomTrainingJob` class, with the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the custom training job.\n",
         "- `container_uri`: The training container image.\n",
@@ -692,8 +605,8 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.CustomTrainingJob(\n",
-        "    display_name=\"iris_\" + UUID,\n",
+        "job = aiplatform.CustomTrainingJob(\n",
+        "    display_name=\"iris-unique\",\n",
         "    script_path=\"custom/trainer/task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
         "    requirements=[\"gcsfs==0.7.1\", \"tensorflow-datasets==4.4\"],\n",
@@ -721,12 +634,12 @@
       "source": [
         "#### Run the custom training job\n",
         "\n",
-        "Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, run the custom job to start the training job by invoking the `run()` method, with the following parameters:\n",
         "\n",
         "- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
         "- `machine_type`: The machine type for the compute instances.\n",
         "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
-        "- `sync`: Whether to block until completion of the job."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -737,7 +650,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, UUID)\n",
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, \"unique\")\n",
         "\n",
         "\n",
         "job.run(\n",
@@ -748,31 +661,24 @@
         "model_path_to_deploy = MODEL_DIR"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "import_model:migration,new"
-      },
-      "source": [
-        "### [general.import-model](https://cloud.google.com/vertex-ai/docs/general/import-model)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "upload_model:mbsdk"
       },
       "source": [
-        "## Upload the model\n",
+        "### Upload the model\n",
         "\n",
-        "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
+        "Next, upload your model to a model resource using the `Model.upload()` method, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Model` resource.\n",
+        "- `display_name`: The human readable name for the model resource.\n",
         "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
         "- `serving_container_image_uri`: The serving container image.\n",
-        "- `sync`: Whether to execute the upload asynchronously or synchronously.\n",
+        "- `sync`: Set **True** to wait until the completion of job\n",
         "\n",
-        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
+        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method.\n",
+        "\n",
+        "Learn more about how to [Import models to Vertex AI](https://cloud.google.com/vertex-ai/docs/general/import-model)"
       ]
     },
     {
@@ -783,8 +689,8 @@
       },
       "outputs": [],
       "source": [
-        "model = aip.Model.upload(\n",
-        "    display_name=\"iris_\" + UUID,\n",
+        "model = aiplatform.Model.upload(\n",
+        "    display_name=\"iris-unique\",\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
         "    sync=False,\n",
@@ -814,16 +720,9 @@
         "id": "make_batch_predictions:migration"
       },
       "source": [
-        "## Make batch predictions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "batchpredictionjobs_create:migration,new,mbsdk"
-      },
-      "source": [
-        "### [predictions.batch-prediction](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
+        "### Generate batch predictions\n",
+        "\n",
+        "To learn more about batch predictions refer [Overview of getting predictions on Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/overview)."
       ]
     },
     {
@@ -832,9 +731,9 @@
         "id": "make_test_items:xgboost,tabular,iris"
       },
       "source": [
-        "### Make test items\n",
+        "#### Create test items\n",
         "\n",
-        "You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "Use synthetic data as test data items. Don’t be concerned about using synthetic data – as it's just for demonstration of generating predictions."
       ]
     },
     {
@@ -856,7 +755,7 @@
       "source": [
         "### Make the batch input file\n",
         "\n",
-        "Now make a batch input file, which you will store in your local Cloud Storage bucket.  Each instance in the prediction request is a list of the form:\n",
+        "Now create a batch input file, which is stored in your Cloud Storage bucket. Each instance in the prediction request is a list of the form:\n",
         "\n",
         "                        [ [ content_1], [content_2] ]\n",
         "\n",
@@ -871,8 +770,6 @@
       },
       "outputs": [],
       "source": [
-        "import tensorflow as tf\n",
-        "\n",
         "gcs_input_uri = BUCKET_URI + \"/\" + \"test.jsonl\"\n",
         "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
         "    for i in INSTANCES:\n",
@@ -887,17 +784,17 @@
         "id": "batch_request:mbsdk,jsonl,custom,cpu"
       },
       "source": [
-        "### Make the batch prediction request\n",
+        "#### Make the batch prediction request\n",
         "\n",
-        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
+        "Now that your model resource is trained, you can make a batch prediction by invoking the `batch_predict()` method, with the following parameters:\n",
         "\n",
         "- `job_display_name`: The human readable name for the batch prediction job.\n",
         "- `gcs_source`: A list of one or more batch request input files.\n",
         "- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
-        "- `instances_format`: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
-        "- `predictions_format`: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
+        "- `instances_format`: The format for the input instances, either *csv* or *jsonl*. Defaults to *jsonl*.\n",
+        "- `predictions_format`: The format for the output predictions, either *csv* or *jsonl*. Defaults to *jsonl*.\n",
         "- `machine_type`: The type of machine to use for training.\n",
-        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -912,7 +809,7 @@
         "MAX_NODES = 1\n",
         "\n",
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"iris_\" + UUID,\n",
+        "    job_display_name=\"iris-unique\",\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"jsonl\",\n",
@@ -952,9 +849,9 @@
         "id": "batch_request_wait:mbsdk"
       },
       "source": [
-        "### Wait for completion of batch prediction job\n",
+        "#### Wait for completion of batch prediction job\n",
         "\n",
-        "Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
+        "Next, wait for the batch job to complete. Alternatively, you can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
       ]
     },
     {
@@ -1008,11 +905,11 @@
         "id": "get_batch_prediction:mbsdk,custom,lcn"
       },
       "source": [
-        "### Get the predictions\n",
+        "#### Get the predictions\n",
         "\n",
         "Next, get the results from the completed batch prediction job.\n",
         "\n",
-        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
+        "The results are written to the Cloud Storage output bucket specified in the batch prediction request. Call the `iter_outputs()` method to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
         "\n",
         "- `instance`: The prediction request.\n",
         "- `prediction`: The prediction response."
@@ -1062,16 +959,9 @@
         "id": "make_online_predictions:migration"
       },
       "source": [
-        "## Make online predictions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "deploy_model:migration,new,mbsdk"
-      },
-      "source": [
-        "### [predictions.deploy-model-api](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
+        "### Generate online predictions\n",
+        "\n",
+        "To learn more about online predictions refer, [Overview of getting predictions on Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/overview)."
       ]
     },
     {
@@ -1080,13 +970,13 @@
         "id": "deploy_model:mbsdk,cpu"
       },
       "source": [
-        "## Deploy the model\n",
+        "#### Deploy the model\n",
         "\n",
-        "Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method, with the following parameters:\n",
+        "Next, deploy your model for online predictions. To deploy the model, invoke the `deploy` method, with the following parameters:\n",
         "\n",
         "- `deployed_model_display_name`: A human readable name for the deployed model.\n",
         "- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
-        "If only one model, then specify as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
+        "If there is only one model, then specify `traffic_split` as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
         "If there are existing models on the endpoint, for which the traffic will be split, then use model_id to specify as { \"0\": percent, model_id: percent, ... }, where model_id is the model id of an existing model to the deployed endpoint. The percents must add up to 100.\n",
         "- `machine_type`: The type of machine to use for training.\n",
         "- `starting_replica_count`: The number of compute instances to initially provision.\n",
@@ -1101,7 +991,7 @@
       },
       "outputs": [],
       "source": [
-        "DEPLOYED_NAME = \"iris-\" + UUID\n",
+        "DEPLOYED_NAME = \"iris-unique\"\n",
         "\n",
         "TRAFFIC_SPLIT = {\"0\": 100}\n",
         "\n",
@@ -1114,7 +1004,8 @@
         "    machine_type=DEPLOY_COMPUTE,\n",
         "    min_replica_count=MIN_NODES,\n",
         "    max_replica_count=MAX_NODES,\n",
-        ")"
+        ")\n",
+        "endpoint.wait()"
       ]
     },
     {
@@ -1135,24 +1026,15 @@
         "    INFO:google.cloud.aiplatform.models:Endpoint model deployed. Resource name: projects/759209241365/locations/us-central1/endpoints/4867177336350441472"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "endpoints_predict:migration,new,mbsdk"
-      },
-      "source": [
-        "### [predictions.online-prediction-automl](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-automl)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "make_test_item:xgboost,tabular,iris"
       },
       "source": [
-        "### Make test item\n",
+        "#### Create a test item\n",
         "\n",
-        "You will use synthetic data as a test data item. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "Use synthetic data as a test data item. Don’t be concerned about using synthetic data – since it's just for demonstration purposes."
       ]
     },
     {
@@ -1172,9 +1054,9 @@
         "id": "predict_request:mbsdk,custom,lcn"
       },
       "source": [
-        "### Make the prediction\n",
+        "#### Make the prediction\n",
         "\n",
-        "Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the `Endpoint` resource.\n",
+        "Now that your model resource is deployed to an endpoint resource, you can make online predictions by sending prediction requests to the endpoint resource.\n",
         "\n",
         "#### Request\n",
         "\n",
@@ -1182,15 +1064,15 @@
         "\n",
         "    [feature_list]\n",
         "\n",
-        "Since the predict() method can take multiple items (instances), send your single test item as a list of one test item.\n",
+        "Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
         "\n",
         "#### Response\n",
         "\n",
-        "The response from the predict() call is a Python dictionary with the following entries:\n",
+        "The response from the `predict()` call is a Python dictionary with the following entries:\n",
         "\n",
         "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
         "- `predictions`: The predicted confidence, between 0 and 1, per class label.\n",
-        "- `deployed_model_id`: The Vertex AI identifier for the deployed `Model` resource which did the predictions."
+        "- `deployed_model_id`: The Vertex AI identifier for the deployed model resource which did the predictions.\n"
       ]
     },
     {
@@ -1215,7 +1097,7 @@
       "source": [
         "## Undeploy the model\n",
         "\n",
-        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
+        "When you're done generating predictions, undeploy the model from the endpoint resouce. This deprovisions all compute resources and ends billing for the deployed model."
       ]
     },
     {
@@ -1251,10 +1133,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "delete_bucket = False\n",
-        "\n",
         "# Delete the model using the Vertex model object\n",
         "model.delete()\n",
         "\n",
@@ -1267,7 +1145,12 @@
         "# Delete the batch prediction job using the Vertex batch prediction object\n",
         "batch_predict_job.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete locally generated files\n",
+        "! rm -rf custom custom.tar.gz\n",
+        "\n",
+        "# Delete cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/migration/sdk-hyperparameter-tuning.ipynb

@@ -30,26 +30,27 @@
       },
       "source": [
         "# Vertex AI Migration: Hyperparameter Tuning\n",
-        "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-hyperparameter-tuning.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-hyperparameter-tuning.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-hyperparameter-tuning.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -82,12 +83,12 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Hyperparameter Tuning`\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI hyperparameter tuning\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model."
+        "- Create a Vertex AI hyperparameter tuning job for training a TensorFlow model."
       ]
     },
     {
@@ -114,11 +115,9 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and\n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -127,9 +126,8 @@
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -150,7 +148,8 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -161,159 +160,74 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "56e219dbcb9a"
       },
       "source": [
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "c97be6a73155"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "before_you_begin:nogpu"
+      },
+      "source": [
+        "## Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2d96e0c47bed"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "timestamp"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -344,7 +258,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -355,7 +269,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -378,7 +292,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip"
+        "import google.cloud.aiplatform as aiplatform"
       ]
     },
     {
@@ -400,7 +314,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -420,7 +334,7 @@
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
-        "Learn more [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your region"
+        "Learn more [about hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators)"
       ]
     },
     {
@@ -442,15 +356,15 @@
         "id": "container:training,prediction"
       },
       "source": [
-        "#### Set pre-built containers\n",
+        "#### Set prebuilt containers\n",
         "\n",
-        "Set the pre-built Docker container image for training and prediction.\n",
+        "Set the prebuilt Docker container image for training and prediction.\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
+        "For the latest list, see [prebuilt containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
         "\n",
         "\n",
-        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+        "For the latest list, see [prebuilt containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
       ]
     },
     {
@@ -489,7 +403,7 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
@@ -501,7 +415,7 @@
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs*."
       ]
     },
     {
@@ -543,7 +457,9 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom training job. \n",
+        "\n",
+        "*Note* : When trainer/task.py is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (.py) is dropped (trainer.task).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
@@ -587,10 +503,10 @@
       "source": [
         "#### Task.py contents\n",
         "\n",
-        "In the next cell, you write the contents of the hyperparameter tuning script task.py. I won't go into detail, it's just there for you to browse. In summary:\n",
+        "In the next cell, you write the contents of the hyperparameter tuning script task.py. It's there for you to browse. In summary:\n",
         "\n",
         "- Parse the command line arguments for the hyperparameter settings for the current trial.\n",
-        " - Get the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
+        " - Get the directory where the model artifacts from the command line (--model_dir) are to be saved. If unspecified, then from the environment variable AIP_MODEL_DIR is used.\n",
         "- Download and preprocess the Boston Housing dataset.\n",
         "- Build a DNN model.\n",
         "- The number of units per dense layer and learning rate hyperparameter values are used during the build and compile of the model.\n",
@@ -714,7 +630,7 @@
       "source": [
         "#### Store training script on your Cloud Storage bucket\n",
         "\n",
-        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
     },
     {
@@ -757,9 +673,9 @@
       "source": [
         "### Prepare your machine specification\n",
         "\n",
-        "Now define the machine specification for your custom training job. This tells Vertex what type of machine instance to provision for the training.\n",
+        "Now define the machine specification for your custom training job. Specify the machine type to provision for training. This informs Vertex AI about the computational resources you need for your training job.\n",
         "  - `machine_type`: The type of GCP instance to provision -- e.g., n1-standard-8.\n",
-        "  - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you use a CPU.\n",
+        "  - `accelerator_type`: The type, if any hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you use a CPU.\n",
         "  - `accelerator_count`: The number of accelerators."
       ]
     },
@@ -789,7 +705,7 @@
       "source": [
         "### Prepare your disk specification\n",
         "\n",
-        "(optional) Now define the disk specification for your custom training job. This tells Vertex what type and size of disk to provision in each machine instance for the training.\n",
+        "(optional) Now define the disk specification for your custom training job. Configure the disk options , this tells Vertex AI exactly what type and size of disk to allocate for each machine instance during training.\n",
         "\n",
         "  - `boot_disk_type`: Either SSD or Standard. SSD is faster, and Standard is less expensive. Defaults to SSD.\n",
         "  - `boot_disk_size_gb`: Size of disk in GB."
@@ -831,9 +747,9 @@
         "\n",
         "-`package_uris`: This is a list of the locations (URIs) of your python training packages to install on the provisioned instance. The locations need to be in a Cloud Storage bucket. These can be either individual python files or a zip (archive) of an entire package. In the later case, the job service  unzip (unarchive) the contents into the docker image.\n",
         "\n",
-        "-`python_module`: The Python module (script) to invoke for running the custom training job. In this example, you  be invoking `trainer.task.py` -- note that it was not neccessary to append the `.py` suffix.\n",
+        "-`python_module`: The Python module (script) to invoke for running the custom training job. In this example, you  be invoking `trainer.task.py` -- note that it wasn't neccessary to append the `.py` suffix.\n",
         "\n",
-        "-`args`: The command line arguments to pass to the corresponding Pythom module. In this example, you  be setting:\n",
+        "-`args`: The command line arguments to pass to the corresponding Python module. In this example, you are setting:\n",
         "  - `\"--model-dir=\" + MODEL_DIR` : The Cloud Storage location where to store the model artifacts. There are two ways to tell the training script where to save the model artifacts:\n",
         "      - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
         "      - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
@@ -853,7 +769,7 @@
       },
       "outputs": [],
       "source": [
-        "JOB_NAME = \"custom_job_\" + UUID\n",
+        "JOB_NAME = \"custom_job_\" + \"unique\"\n",
         "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, JOB_NAME)\n",
         "\n",
         "if not TRAIN_NGPU or TRAIN_NGPU < 2:\n",
@@ -925,7 +841,9 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.CustomJob(display_name=\"boston_\" + UUID, worker_pool_specs=worker_pool_spec)\n",
+        "job = aiplatform.CustomJob(\n",
+        "    display_name=\"boston_\" + \"unique\", worker_pool_specs=worker_pool_spec\n",
+        ")\n",
         "\n",
         "# print(job)"
       ]
@@ -956,8 +874,8 @@
       "source": [
         "from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
         "\n",
-        "hpt_job = aip.HyperparameterTuningJob(\n",
-        "    display_name=\"boston_\" + UUID,\n",
+        "hpt_job = aiplatform.HyperparameterTuningJob(\n",
+        "    display_name=\"boston_\" + \"unique\",\n",
         "    custom_job=job,\n",
         "    metric_spec={\n",
         "        \"val_loss\": \"minimize\",\n",
@@ -1159,10 +1077,10 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "delete_bucket = False\n",
         "\n",
+        "# # remove locally generated  directory\n",
+        "! rm -r custom\n",
         "# Delete the training job\n",
         "try:\n",
         "    job.delete()\n",
@@ -1172,8 +1090,8 @@
         "# Delete the HPT job using the Vertex batch prediction object\n",
         "hpt_job.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI\n"
+        "# if delete_bucket:\n",
+        "# #     ! gsutil rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/ml_metadata/get_started_with_google_artifact_registry.ipynb

@@ -32,23 +32,26 @@
         "# Get started with Google Artifact Registry\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/get_started_with_google_artifact_registry.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/get_started_with_google_artifact_registry.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/get_started_with_google_artifact_registry.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -75,11 +78,11 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Google Artifact Registry`.\n",
+        "In this tutorial, you learn how to use Google Artifact Registry.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Google Artifact Registry`\n",
+        "- Google Artifact Registry\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -90,17 +93,6 @@
         "- Deleting a private Docker repository."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4ced09c1b4ce"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "No dataset is used in this tutorial. References to an example dataset are for demonstration purposes."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -114,7 +106,9 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and\n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the\n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -123,9 +117,8 @@
         "id": "install_mlops"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing the notebook."
+        "## Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -142,48 +135,75 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hhq5zEbGg0XX"
+        "id": "8d726e21c0bb"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "EzrelQZ22IZj"
+        "id": "3b9119a60525"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "lWEdiXsJg0XY"
+        "id": "96254aa096b5"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "8bc8a29f9001"
+        "id": "1d7064423926"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "405401bbd1c7"
+      },
+      "outputs": [],
+      "source": [
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d183adfc792a"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. [Learn more about setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
       ]
     },
     {
@@ -195,103 +215,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "47bc07d4231b"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "959545da671a"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dr--iN2kAylZ"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PyQmSRbKA8r-"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -322,7 +246,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -333,19 +257,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_vars"
-      },
-      "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
-        "### Import libraries and define constants"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -356,7 +268,7 @@
       "source": [
         "## Introduction to Google Artifact Registry\n",
         "\n",
-        "The `Google Artifact Registry` is a service for storing and managing artifacts in private repositories, including container images, Helm charts, and language packages. It is the recommended container image registry for Google Cloud.\n",
+        "The Google Artifact Registry is a service for storing and managing artifacts in private repositories, including container images, Helm charts, and language packages. It's the recommended container image registry for Google Cloud.\n",
         "\n",
         "Learn more about [Quick start for Docker](https://cloud.google.com/artifact-registry/docs/docker/quickstart)"
       ]
@@ -369,7 +281,7 @@
       "source": [
         "### Enable Artifact Registry API\n",
         "\n",
-        "First, you must enable the Artifact Registry API service for your project.\n",
+        "First, enable the Artifact Registry API service for your project.\n",
         "\n",
         "Learn more about [Enabling service](https://cloud.google.com/artifact-registry/docs/enable-service)."
       ]
@@ -410,7 +322,7 @@
       "source": [
         "PRIVATE_REPO = \"my-docker-repo\"\n",
         "\n",
-        "! gcloud artifacts repositories create {PRIVATE_REPO} --repository-format=docker --location={REGION} --description=\"Docker repository\"\n",
+        "! gcloud artifacts repositories create {PRIVATE_REPO} --repository-format=docker --location={LOCATION} --description=\"Docker repository\"\n",
         "\n",
         "! gcloud artifacts repositories list"
       ]
@@ -423,7 +335,7 @@
       "source": [
         "### Configure authentication to your private repo\n",
         "\n",
-        "Before you push or pull container images, configure Docker to use the `gcloud` command-line tool to authenticate requests to `Artifact Registry` for your region."
+        "Before you push or pull container images, configure Docker to use the gcloud command-line tool to authenticate requests to Artifact Registry for your region."
       ]
     },
     {
@@ -434,7 +346,7 @@
       },
       "outputs": [],
       "source": [
-        "! gcloud auth configure-docker {REGION}-docker.pkg.dev --quiet"
+        "! gcloud auth configure-docker {LOCATION}-docker.pkg.dev --quiet"
       ]
     },
     {
@@ -471,7 +383,7 @@
         "\n",
         "- Tagging the Docker image with a repository name configures the docker push command to push the image to a specific location, e.g., us-central1-docker.pkg.dev.\n",
         "\n",
-        "- `:my-tag` is a tag you're adding to the Docker image. If a tag is not specified, it defaults to `:latest`."
+        "- :my-tag is a tag you're adding to the Docker image. It defaults to :latest."
       ]
     },
     {
@@ -506,7 +418,7 @@
       },
       "outputs": [],
       "source": [
-        "! docker push {REGION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME}"
+        "! docker push {LOCATION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME}"
       ]
     },
     {
@@ -528,7 +440,7 @@
       },
       "outputs": [],
       "source": [
-        "! docker pull {REGION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME}"
+        "! docker pull {LOCATION}-docker.pkg.dev/{PROJECT_ID}/{PRIVATE_REPO}/{CONTAINER_NAME}"
       ]
     },
     {
@@ -539,7 +451,7 @@
       "source": [
         "### Deleting your private Docker repostory\n",
         "\n",
-        "Finally, once your private repository becomes obsolete, use the command `gcloud artifacts repositories delete` to delete it `Google Artifact Registry`."
+        "Finally, once your private repository becomes obsolete, use the `gcloud artifacts repositories delete` command to remove the repository from the Google Artifact Registry."
       ]
     },
     {
@@ -550,7 +462,7 @@
       },
       "outputs": [],
       "source": [
-        "! gcloud artifacts repositories delete {PRIVATE_REPO} --location={REGION} --quiet"
+        "! gcloud artifacts repositories delete {PRIVATE_REPO} --location={LOCATION} --quiet"
       ]
     }
   ],

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb

@@ -32,25 +32,28 @@
         "# Vertex AI: Track parameters and metrics for custom training jobs\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>  \n",
-        "</table>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>"
       ]
     },
     {
@@ -63,7 +66,9 @@
         "\n",
         "This notebook demonstrates how to track metrics and parameters for Vertex AI custom training jobs, and how to perform detailed analysis using this data.\n",
         "\n",
-        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata), [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training), and [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments)."
+        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata),\n",
+        "[Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training), and \n",
+        "[Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments)."
       ]
     },
     {
@@ -113,11 +118,9 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and \n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -126,9 +129,8 @@
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "### Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "### Get Started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -141,54 +143,84 @@
       "source": [
         "! pip3 install --upgrade tensorflow \\\n",
         "                         google-cloud-aiplatform \\\n",
-        "                         scikit-learn -q"
+        "                         scikit-learn -q \\\n",
+        "                         pandas"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hhq5zEbGg0XX"
+        "id": "5eec42e37bcf"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "EzrelQZ22IZj"
+        "id": "dcc98768955f"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "lWEdiXsJg0XY"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "8bc8a29f9001"
+        "id": "56e219dbcb9a"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c97be6a73155"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "442da99b7efa"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
       ]
     },
     {
@@ -200,103 +232,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "47bc07d4231b"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "959545da671a"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dr--iN2kAylZ"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PyQmSRbKA8r-"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\""
       ]
     },
     {
@@ -327,7 +263,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -338,7 +274,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -367,8 +303,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "import pandas as pd\n",
         "from google.cloud import aiplatform\n",
         "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
@@ -381,7 +315,7 @@
         "id": "O8XJZB3gR8eL"
       },
       "source": [
-        "## Initialize Vertex AI and set an _experiment_\n"
+        "## Initialize Vertex AI and set an experiment\n"
       ]
     },
     {
@@ -423,7 +357,7 @@
       "source": [
         "aiplatform.init(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    staging_bucket=BUCKET_URI,\n",
         "    experiment=EXPERIMENT_NAME,\n",
         ")"
@@ -435,7 +369,7 @@
         "id": "6PlilQPFeS_h"
       },
       "source": [
-        "## Tracking parameters and metrics in Vertex AI custom training jobs"
+        "### Tracking parameters and metrics in Vertex AI custom training jobs"
       ]
     },
     {
@@ -444,7 +378,7 @@
         "id": "f8fd397cc4f6"
       },
       "source": [
-        "### Download the Dataset to Cloud Storage"
+        "# Download the Dataset to Cloud Storage"
       ]
     },
     {
@@ -580,7 +514,7 @@
         "id": "k_QorXXztzPH"
       },
       "source": [
-        "Start a new experiment run to track training parameters and start the training job. Note that this operation will take around 10 mins."
+        "Start a new experiment run to track training parameters and start the training job. Note that this operation takes around 10 minutes."
       ]
     },
     {
@@ -620,7 +554,7 @@
         "id": "O-uCOL3Naap4"
       },
       "source": [
-        "Next, deploy your Vertex AI Model resource to a Vertex AI Endpoint resource. This operation will take 10-20 mins."
+        "Next, deploy your Vertex AI Model resource to a Vertex AI endpoint resource. This operation takes 10-20 minutes."
       ]
     },
     {
@@ -824,7 +758,7 @@
       },
       "outputs": [],
       "source": [
-        "# Warning: Setting this to true will delete everything in your bucket\n",
+        "# Warning: Setting this to true deletes everything in your bucket\n",
         "delete_bucket = False\n",
         "\n",
         "# Delete dataset\n",
@@ -832,7 +766,7 @@
         "\n",
         "# Delete experiment\n",
         "experiment = aiplatform.Experiment(\n",
-        "    experiment_name=EXPERIMENT_NAME, project=PROJECT_ID, location=REGION\n",
+        "    experiment_name=EXPERIMENT_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "experiment.delete()\n",
         "\n",
@@ -849,7 +783,7 @@
         "model.delete()\n",
         "\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb

@@ -32,25 +32,28 @@
         "# Vertex AI: Track parameters and metrics for locally trained models\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
         "     </a>\n",
-        "     </td>\n",
-        "</table>"
+        "   </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>"
       ]
     },
     {
@@ -74,17 +77,17 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.\n",
+        "In this notebook, you learn how to use Vertex ML Metadata to track training parameters and evaluation metrics.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex ML Metadata`\n",
-        "- `Vertex AI Experiments`\n",
+        "- Vertex ML Metadata\n",
+        "- Vertex AI Experiments\n",
         "\n",
         "The steps performed include:\n",
         "\n",
         "- Track parameters and metrics for a locally trained model.\n",
-        "- Extract and perform analysis for all parameters and metrics within an Experiment."
+        "- Extract and perform analysis for all parameters and metrics within an experiment."
       ]
     },
     {
@@ -95,7 +98,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "In this notebook, we will train a simple distributed neural network (DNN) model to predict automobile's miles per gallon (MPG) based on automobile information in the [auto-mpg dataset](https://www.kaggle.com/devanshbesain/exploration-and-analysis-auto-mpg)."
+        "In this notebook, you train a simple distributed neural network (DNN) model to predict automobile's miles per gallon (MPG) based on automobile information in the [auto-mpg dataset](https://www.kaggle.com/devanshbesain/exploration-and-analysis-auto-mpg)."
       ]
     },
     {
@@ -113,11 +116,9 @@
         "* Cloud Storage\n",
         "\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and \n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -126,9 +127,8 @@
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -140,47 +140,86 @@
       "outputs": [],
       "source": [
         "! pip install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                tensorflow==2.11"
+        "                                tensorflow==2.11 \\\n",
+        "                                matplotlib \\\n",
+        "                                pandas \\\n",
+        "                                'numpy<2.0.0'"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hhq5zEbGg0XX"
+        "id": "5eec42e37bcf"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "EzrelQZ22IZj"
+        "id": "dcc98768955f"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "lWEdiXsJg0XY"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "56e219dbcb9a"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c97be6a73155"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "### Set your project ID\n",
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "- Run `gcloud config list`.\n",
-        "- Run `gcloud project list`.\n",
-        "- See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "442da99b7efa"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
       ]
     },
     {
@@ -192,88 +231,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project $PROJECT_ID"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable, used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dr--iN2kAylZ"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "Depending on your  Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "- Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "457c78b08293"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "984a0526fb68"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6679fdd7776b"
-      },
-      "source": [
-        "**4. Service Account or other**\n",
-        "- See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -282,15 +240,7 @@
         "id": "XoEqT2Y4DJmf"
       },
       "source": [
-        "### Import libraries and define constants"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Y9Uo3tifg1kx"
-      },
-      "source": [
+        "### Import libraries and define constants\n",
         "Import required libraries."
       ]
     },
@@ -347,7 +297,7 @@
       },
       "source": [
         "### Experiment\n",
-        "Experiments describe a context that groups your runs and the artifacts you create into a logical session. For example, in this notebook you create an Experiment and log data to that experiment."
+        "Experiments describe a context that groups your runs and the artifacts you create into a logical session. For example, in this notebook you create an experiment and log data to that experiment."
       ]
     },
     {
@@ -357,7 +307,7 @@
       },
       "source": [
         "### Run\n",
-        "A run represents a single path/avenue that you executed while performing an experiment. A run includes artifacts that you used as inputs or outputs, and parameters that you used in this execution. An Experiment can contain multiple runs. "
+        "A run represents a single path/avenue that you executed while performing an experiment. A run includes artifacts that you used as inputs or outputs, and parameters that you used in this execution. An experiment can contain multiple runs. "
       ]
     },
     {
@@ -563,7 +513,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, experiment=EXPERIMENT_NAME)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, experiment=EXPERIMENT_NAME)"
       ]
     },
     {
@@ -594,7 +544,7 @@
         "]\n",
         "\n",
         "for i, params in enumerate(parameters):\n",
-        "    aiplatform.start_run(run=f\"auto-mpg-local-run-{i}\")\n",
+        "    aiplatform.start_run(run=f\"auto-mpg-lcl-run-{i}\")\n",
         "    aiplatform.log_params(params)\n",
         "    model, history = train(\n",
         "        normed_train_data,\n",
@@ -633,7 +583,7 @@
         "id": "A1PqKxlpOZa2"
       },
       "source": [
-        "We can also extract all parameters and metrics associated with any Experiment into a dataframe for further analysis."
+        "You can also extract all parameters and metrics associated with any experiment into a dataframe for further analysis."
       ]
     },
     {
@@ -699,7 +649,7 @@
         "id": "F19_5lw0MqXv"
       },
       "source": [
-        "Run the following to get the URL of Vertex AI Experiments for your project.\n"
+        "Run the following to get the URL of Vertex AI experiments for your project.\n"
       ]
     },
     {
@@ -724,12 +674,30 @@
       "source": [
         "## Cleaning up\n",
         "\n",
-        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
-        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+        "delete the individual resources you created in this tutorial:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d02bde73377a"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
+        "# delete experiment and runs associated with experiment\n",
+        "experiment_name = (EXPERIMENT_NAME,)\n",
+        "project = (PROJECT_ID,)\n",
+        "location = (LOCATION,)\n",
+        "delete_backing_tensorboard_runs = (True,)\n",
         "\n",
-        "- Experiment (Can be deleted manually in the GCP Console UI)"
+        "experiment = aiplatform.Experiment(\n",
+        "    experiment_name=EXPERIMENT_NAME, project=PROJECT_ID, location=LOCATION\n",
+        ")\n",
+        "\n",
+        "experiment.delete(delete_backing_tensorboard_runs=delete_backing_tensorboard_runs)"
       ]
     }
   ],

notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb

@@ -32,24 +32,26 @@
         "# Vertex AI: Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fml_metadata%2Fvertex-pipelines-ml-metadata.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "     </a>\n",
-        "     </td>\n",
         "</table>"
       ]
     },
@@ -61,7 +63,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipelines runs, and analyze this metadata using the Vertex AI SDK for Pyrhon. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
+        "This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipeline runs, and analyze this metadata using the Vertex AI Python SDK. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
         "\n",
         "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
       ]
@@ -86,7 +88,7 @@
         "* Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI.\n",
         "* The pipeline creates a dataset, trains a scikit-learn model, and deploys the model to an endpoint.\n",
         "* Write custom pipeline components that generate artifacts and metadata.\n",
-        "* Compare Vertex AI Pipelines runs, both in the Google Cloud console and programmatically.\n",
+        "* Compare Vertex AI Pipeline runs, both in the Google Cloud console and programmatically.\n",
         "* Trace the lineage for pipeline-generated artifacts.\n",
         "* Query your pipeline run metadata."
       ]
@@ -99,7 +101,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "In this notebook, you'll train a model using scikit-learn to classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
+        "This notebook uses scikit-learn to train a model and classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
       ]
     },
     {
@@ -127,12 +129,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Run the following commands to install the Vertex AI SDK for Python and packages used in this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -150,137 +159,97 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -344,7 +313,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -491,7 +460,7 @@
         "id": "2937d462a96a"
       },
       "source": [
-        "Initialize the Vertex AI SDK"
+        "### Initialize Vertex AI SDK for Python"
       ]
     },
     {
@@ -502,7 +471,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -523,7 +492,7 @@
       },
       "source": [
         "### Pipeline Run\n",
-        "The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines. Each run generates artifacts, metrics, and associated metadata."
+        "The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines, during which artifacts, metrics, and associated metadata are generated."
       ]
     },
     {
@@ -534,7 +503,7 @@
       "source": [
         "### Artifact\n",
         "\n",
-        "An artifact is a resource generated by your pipeline. Artifacts could datasets, models, endpoints, or custom resources defined in your pipeline."
+        "An artifact is a resource generated by your pipeline. Artifacts can be datasets, models, endpoints, or custom resources defined in your pipeline."
       ]
     },
     {
@@ -545,7 +514,7 @@
       "source": [
         "### Metric\n",
         "\n",
-        "A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric could be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
+        "A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric can be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
       ]
     },
     {
@@ -556,7 +525,7 @@
       "source": [
         "### Metadata\n",
         "\n",
-        "Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, could include the URL of the model artifacts, its name, and the time it was created."
+        "Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, includes the URL of the model artifacts, its name, and the time it was created."
       ]
     },
     {
@@ -567,7 +536,7 @@
       "source": [
         "## Creating a 3-step pipeline with custom components\n",
         "\n",
-        "The focus of this lab is on understanding metadata from pipeline runs. In order to do that, you'll need a pipeline to run on Vertex AI Pipelines, which is where you’ll start. Here you’ll define a 3-step pipeline with the following custom components:\n",
+        "The focus of this lab is on understanding metadata from pipeline runs. To do that, you need a pipeline to run on Vertex AI Pipelines, which is where you start. Here, you define a 3-step pipeline with the following custom components:\n",
         "\n",
         "* `get_dataframe`: Retrieve data from a BigQuery table and convert it into a pandas DataFrame.\n",
         "* `train_sklearn_model`: Use the pandas DataFrame to train and export a scikit-learn model, along with some metrics.\n",
@@ -604,14 +573,16 @@
       "outputs": [],
       "source": [
         "@component(\n",
-        "    packages_to_install=[\"google-cloud-bigquery\", \"pandas\", \"pyarrow\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    packages_to_install=[\"google-cloud-bigquery[pandas]\", \"pyarrow\"],\n",
+        "    base_image=\"python:3.10\",\n",
         "    output_component_file=\"create_dataset.yaml\",\n",
         ")\n",
-        "def get_dataframe(bq_table: str, output_data_path: OutputPath(\"Dataset\")):\n",
+        "def get_dataframe(\n",
+        "    project_id: str, bq_table: str, output_data_path: OutputPath(\"Dataset\")\n",
+        "):\n",
         "    from google.cloud import bigquery\n",
         "\n",
-        "    bqclient = bigquery.Client(project=PROJECT_ID)\n",
+        "    bqclient = bigquery.Client(project=project_id)\n",
         "    table = bigquery.TableReference.from_string(bq_table)\n",
         "    rows = bqclient.list_rows(table)\n",
         "    dataframe = rows.to_dataframe(\n",
@@ -644,8 +615,8 @@
       "outputs": [],
       "source": [
         "@component(\n",
-        "    packages_to_install=[\"scikit-learn\", \"pandas\", \"joblib\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    packages_to_install=[\"scikit-learn==1.2\", \"pandas\", \"joblib\", \"numpy==1.26.4\"],\n",
+        "    base_image=\"python:3.10\",\n",
         "    output_component_file=\"beans_model_component.yaml\",\n",
         ")\n",
         "def sklearn_train(\n",
@@ -691,7 +662,7 @@
       "source": [
         "@component(\n",
         "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
-        "    base_image=\"python:3.9\",\n",
+        "    base_image=\"python:3.10\",\n",
         "    output_component_file=\"beans_deploy_component.yaml\",\n",
         ")\n",
         "def deploy_model(\n",
@@ -708,7 +679,7 @@
         "    deployed_model = aiplatform.Model.upload(\n",
         "        display_name=\"beans-model-pipeline\",\n",
         "        artifact_uri=model.uri.replace(\"model\", \"\"),\n",
-        "        serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest\",\n",
+        "        serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-2:latest\",\n",
         "    )\n",
         "    endpoint = deployed_model.deploy(machine_type=\"n1-standard-4\")\n",
         "\n",
@@ -741,12 +712,12 @@
         "    name=\"mlmd-pipeline\",\n",
         ")\n",
         "def pipeline(\n",
-        "    bq_table: str = \"\",\n",
-        "    output_data_path: str = \"data.csv\",\n",
-        "    project: str = PROJECT_ID,\n",
-        "    region: str = REGION,\n",
+        "    bq_table: str,\n",
+        "    output_data_path: str,\n",
+        "    project: str,\n",
+        "    region: str,\n",
         "):\n",
-        "    dataset_task = get_dataframe(bq_table)\n",
+        "    dataset_task = get_dataframe(project, bq_table)\n",
         "\n",
         "    model_task = sklearn_train(dataset_task.output)\n",
         "\n",
@@ -759,7 +730,7 @@
         "id": "910541af051c"
       },
       "source": [
-        "The following generates a JSON file that you'll use to run the pipeline:"
+        "The following generates a JSON file that is then used to run the pipeline:"
       ]
     },
     {
@@ -779,9 +750,22 @@
         "id": "u-iTnzt3B6Z_"
       },
       "source": [
-        "### Start two pipeline runs\n",
+        "### Initiate pipeline runs\n",
         "\n",
-        "Next you'll kick ofg **two** runs of our pipeline. First, define a timestamp to use for our pipeline job IDs:"
+        "First, define a timestamp to use as your pipeline job IDs.\n",
+        "\n",
+        "Then for each run, create an instance of `PipelineJob` from the `pipeline_jobs` module.\n",
+        " For each instance provide the following details:\n",
+        "\n",
+        "* `display_name` : Human-readable name for the pipeline job.\n",
+        "* `template_path` : This specifies the path to the pipeline template file in JSON format, which contains the pipeline's configuration and structure created in the previous steps.\n",
+        "* `job_id` : This sets a unique identifier for the job.\n",
+        "* `parameter_values` : This dictionary contains key-value pairs for the parameters required by the pipeline which are metioned during pipeline definition.\n",
+        "    * `bq_table` :  Specifies the BigQuery table to use.\n",
+        "    * `output_data_path` : Defines the path for the output data file.\n",
+        "    * `project` : Specifies the Google Cloud project ID.\n",
+        "    * `region` : Defines the region where the pipeline will run.\n",
+        "* enable_caching : When set to `True`, caching is enabled for the pipeline run. This lets the system reuse previous results, if the same job has been executed before with identical parameters, thereby saving time and resources"
       ]
     },
     {
@@ -803,7 +787,7 @@
         "id": "3d380ed72490"
       },
       "source": [
-        "The pipeline takes one parameter when you run it: the `bq_table` we want to use for training data you’ll use for training data. This pipeline run uses a smaller version of the beans dataset:"
+        "Create a pipeline run using the smaller version beans dataset."
       ]
     },
     {
@@ -818,7 +802,12 @@
         "    display_name=\"mlmd-pipeline\",\n",
         "    template_path=\"mlmd_pipeline.json\",\n",
         "    job_id=\"mlmd-pipeline-small-{}\".format(TIMESTAMP),\n",
-        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\"},\n",
+        "    parameter_values={\n",
+        "        \"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\",\n",
+        "        \"output_data_path\": \"data.csv\",\n",
+        "        \"project\": PROJECT_ID,\n",
+        "        \"region\": REGION,\n",
+        "    },\n",
         "    enable_caching=True,\n",
         ")"
       ]
@@ -844,7 +833,12 @@
         "    display_name=\"mlmd-pipeline\",\n",
         "    template_path=\"mlmd_pipeline.json\",\n",
         "    job_id=\"mlmd-pipeline-large-{}\".format(TIMESTAMP),\n",
-        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\"},\n",
+        "    parameter_values={\n",
+        "        \"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\",\n",
+        "        \"output_data_path\": \"data.csv\",\n",
+        "        \"project\": PROJECT_ID,\n",
+        "        \"region\": REGION,\n",
+        "    },\n",
         "    enable_caching=True,\n",
         ")"
       ]
@@ -895,7 +889,7 @@
         "id": "cc15017be48e"
       },
       "source": [
-        "After running this cell, you'll see a link to view each pipeline in the Google Cloud console. Open that link to see more details on your pipeline.\n",
+        "After running this cell, there is a link to view each pipeline in the Google Cloud console. Open that link to get more details about your pipeline.\n",
         "\n",
         "**These pipeline runs will take 10-15 minutes to complete.**"
       ]
@@ -915,9 +909,9 @@
         "id": "A1PqKxlpOZa2"
       },
       "source": [
-        "Now that you have two pipeline completed pipeline runs, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
+        "Once both the pipelines run successfully, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
         "\n",
-        "**For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, see [this codelab](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5).**"
+        "For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, check out this codelab: [Understanding pipeline artifacts and lineage](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5)\n"
       ]
     },
     {
@@ -926,7 +920,8 @@
         "id": "jbRf1WoH_vbY"
       },
       "source": [
-        "You can use the `aiplatform.get_pipeline_df()` method to access run metadata. Here, you'll get metadata for the last two runs of the same pipeline and load it into a Pandas DataFrame. The `mlmd-pipeline` parameter here refers to the name you gave your pipeline in the pipeline definition:"
+        "Use `aiplatform.get_pipeline_df()` method to retrieve the metadata for the last two runs of the pipeline. Then, load it into a Pandas DataFrame. \n",
+        "The `pipeline` parameter specifies the name of your pipeline as defined in the pipeline configuration, which in this case is *mlmd-pipeline*."
       ]
     },
     {
@@ -947,7 +942,7 @@
         "id": "d23e2cb66265"
       },
       "source": [
-        "You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model's accuracy and the amount of data used for training. Run the following to generate a graph:"
+        "You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model accuracy and the amount of data used for training. Run the following to generate a graph:"
       ]
     },
     {
@@ -979,7 +974,7 @@
         "id": "4431b5d062f3"
       },
       "source": [
-        "In addition to getting a DataFrame of all pipeline metrics, you may want to programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
+        "In addition to creating a DataFrame of all pipeline metrics, you can programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
       ]
     },
     {
@@ -990,7 +985,7 @@
       "source": [
         "### Getting all Model artifacts\n",
         "\n",
-        "To query artifacts in this way, you'll create a `MetadataServiceClient`:"
+        "To query artifacts in this way, create a `MetadataServiceClient`:"
       ]
     },
     {
@@ -1056,7 +1051,7 @@
         "id": "F19_5lw0MqXv"
       },
       "source": [
-        "Next, get all artifacts created after August 10, 2021 with a `LIVE` state. After you run this request, display the results in a pandas DataFrame. First, execute the request:"
+        "Next, get all artifacts created after August 10, 2021 that are in `LIVE` state. "
       ]
     },
     {
@@ -1122,7 +1117,9 @@
         "\n",
         "* The pipeline runs you executed deployed endpoints in Vertex AI. Navigate to the [Google Cloud console](https://console.cloud.google.com/vertex-ai/endpoints) to delete those endpoints.\n",
         "\n",
-        "* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created."
+        "* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created.\n",
+        "\n",
+        "Alternatively, you can execute the below cell to clean up the resources used in this notebook."
       ]
     },
     {
@@ -1133,16 +1130,36 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
+        "# delete pipelines\n",
         "try:\n",
         "    run1.delete()\n",
         "    run2.delete()\n",
         "except Exception as e:\n",
         "    print(e)\n",
         "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# undeploy model from endpoints\n",
+        "endpoints = aiplatform.Endpoint.list(\n",
+        "    filter='display_name=\"beans-model-pipeline_endpoint\"'\n",
+        ")\n",
+        "for endpoint in endpoints:\n",
+        "    deployed_models = endpoint.list_models()\n",
+        "    for deployed_model in deployed_models:\n",
+        "        endpoint.undeploy(deployed_model_id=deployed_model.id)\n",
+        "    # delete endpoint\n",
+        "    endpoint.delete()\n",
+        "\n",
+        "# delete model\n",
+        "model_ids = aiplatform.Model.list(filter='display_name=\"beans-model-pipeline\"')\n",
+        "for model_id in model_ids:\n",
+        "    model = aiplatform.Model(model_name=model_id.resource_name)\n",
+        "    model.delete()\n",
+        "\n",
+        "# delete locally generated files\n",
+        "! rm -rf beans_deploy_component.yaml beans_model_component.yaml create_dataset.yaml mlmd_pipeline.json\n",
+        "\n",
+        "# delete cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -rf {BUCKET_URI}"
       ]
     }

notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb

@@ -32,24 +32,26 @@
         "# Vertex AI Pipelines: Evaluating batch prediction results from an AutoML Tabular classification model\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_evaluation%2Fautoml_tabular_classification_model_evaluation.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -121,7 +123,7 @@
         "- `PhotoAmt`: Total uploaded photos for this pet\n",
         "- `Adopted`: Whether or not the pet was adopted (Yes/No).\n",
         "\n",
-        "**Note**: This dataset is moved to a public Cloud Storage bucket from where it is accessed in this notebook."
+        "**Note**: This dataset is moved to a public Cloud Storage bucket from where it's accessed in this notebook."
       ]
     },
     {
@@ -143,15 +145,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -163,48 +172,87 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-pipeline-components==1.0.26 \\\n",
+        "                                 google-cloud-pipeline-components \\\n",
         "                                 matplotlib"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f82e28c631cc"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "191d1345e064"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -216,89 +264,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -344,7 +310,9 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = (\n",
+        "    f\"gs://model-evaluation-bucket-{PROJECT_ID}-unique\"  # @param {type:\"string\"}\n",
+        ")"
       ]
     },
     {
@@ -364,7 +332,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
+        "! gsutil mb -l $LOCATION $BUCKET_URI"
       ]
     },
     {
@@ -375,7 +343,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -386,7 +354,7 @@
       },
       "outputs": [],
       "source": [
-        "SERVICE_ACCOUNT = \"[your-service-account]\"  # @param {type:\"string\"}"
+        "SERVICE_ACCOUNT = \"\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -488,7 +456,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -985,8 +953,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "# Delete model resource\n",
         "model.delete()\n",
         "\n",
@@ -999,10 +965,10 @@
         "# Delete the evaluation pipeline\n",
         "job.delete()\n",
         "\n",
-        "# Delete Cloud Storage objects\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil rm -rf {BUCKET_URI}"
       ]
     }
   ],

notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb

@@ -29,27 +29,29 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Vertex AI Pipelines: Evaluating batch prediction results from AutoML Tabular regression model\n",
+        "# Vertex AI Pipelines: Evaluating batch prediction results from AutoML tabular regression model\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_evaluation%2Fautoml_tabular_regression_model_evaluation.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -61,7 +63,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to use Vertex AI regression model evaluation component to evaluate an AutoML Tabular regression model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. \n",
+        "This notebook demonstrates how to use Vertex AI regression model evaluation component to evaluate an AutoML tabular regression model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. \n",
         "\n",
         "Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction). Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
       ]
@@ -76,10 +78,10 @@
         "\n",
         "In this tutorial, you learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
-        "- Vertex AI Datasets (Tabular)\n",
-        "- Vertex AI Training (AutoML Tabular Training)\n",
+        "- Vertex AI datasets (tabular)\n",
+        "- Vertex AI Training (AutoML tabular training)\n",
         "- Vertex AI Batch predictions\n",
         "- Vertex AI Pipelines\n",
         "- Vertex AI Model Registry\n",
@@ -87,13 +89,13 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a Vertex AI Dataset\n",
-        "- Configure a `AutoMLTabularTrainingJob`\n",
-        "- Run the `AutoMLTabularTrainingJob` which returns a model\n",
-        "- Import a pre-trained `AutoML model resource` into the pipeline\n",
-        "- Run a `batch prediction` job in the pipeline\n",
-        "- Evaluate the AutoML model using the `regression evaluation component`\n",
-        "- Import the Regression Metrics to the AutoML model resource"
+        "- Create a Vertex AI dataset.\n",
+        "- Configure an `AutoMLTabularTrainingJob` class.\n",
+        "- Run the `AutoMLTabularTrainingJob` which returns a model.\n",
+        "- Import a pre-trained `AutoML model resource` into the pipeline.\n",
+        "- Run a `batch prediction` job in the pipeline.\n",
+        "- Evaluate the AutoML model using the `regression evaluation component`.\n",
+        "- Import the generated regression metrics into the AutoML model resource."
       ]
     },
     {
@@ -104,22 +106,22 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset being used in this notebook is a part of the PetFinder Dataset, available [here](https://www.kaggle.com/c/petfinder-adoption-prediction) on Kaggle. The current dataset is only a part of the original dataset considered for the problem of predicting age of the pet. It consists of the following fields:\n",
+        "The dataset you use in this notebook is a part of the [PetFinder Dataset](https://www.kaggle.com/c/petfinder-adoption-prediction) available on Kaggle. The current dataset is only a part of the original dataset considered for the problem of predicting the age of a pet. The dataset consists of the following fields:\n",
         "\n",
-        "- `Type`: Type of animal (1 = Dog, 2 = Cat)\n",
-        "- `Age`: Age of pet when listed, in months\n",
-        "- `Breed1`: Primary breed of pet\n",
-        "- `Gender`: Gender of pet\n",
-        "- `Color1`: Color 1 of pet \n",
-        "- `Color2`: Color 2 of pet\n",
-        "- `MaturitySize`: Size at maturity (1 = Small, 2 = Medium, 3 = Large, 4 = Extra Large, 0 = Not Specified)\n",
-        "- `FurLength`: Fur length (1 = Short, 2 = Medium, 3 = Long, 0 = Not Specified)\n",
-        "- `Vaccinated`: Pet has been vaccinated (1 = Yes, 2 = No, 3 = Not Sure)\n",
-        "- `Sterilized`: Pet has been spayed / neutered (1 = Yes, 2 = No, 3 = Not Sure)\n",
-        "- `Health`: Health Condition (1 = Healthy, 2 = Minor Injury, 3 = Serious Injury, 0 = Not Specified)\n",
-        "- `Fee`: Adoption fee (0 = Free)\n",
-        "- `PhotoAmt`: Total uploaded photos for this pet\n",
-        "- `Adopted`: Whether or not the pet was adopted (Yes/No).\n",
+        "- **Type**: Type of animal (1 = Dog, 2 = Cat).\n",
+        "- **Age**: Age of pet when listed, in months.\n",
+        "- **Breed1**: Primary breed of pet.\n",
+        "- **Gender**: Gender of pet.\n",
+        "- **Color1**: Color 1 of pet.\n",
+        "- **Color2**: Color 2 of pet.\n",
+        "- **MaturitySize**: Size at maturity (1 = Small, 2 = Medium, 3 = Large, 4 = Extra Large, 0 = Not Specified).\n",
+        "- **FurLength**: Fur length (1 = Short, 2 = Medium, 3 = Long, 0 = Not Specified).\n",
+        "- **Vaccinated**: Pet has been vaccinated (1 = Yes, 2 = No, 3 = Not Sure).\n",
+        "- **Sterilized**: Pet has been spayed / neutered (1 = Yes, 2 = No, 3 = Not Sure).\n",
+        "- **Health**: Health Condition (1 = Healthy, 2 = Minor Injury, 3 = Serious Injury, 0 = Not Specified).\n",
+        "- **Fee**: Adoption fee (0 = Free).\n",
+        "- **PhotoAmt**: Total uploaded photos for this pet.\n",
+        "- **Adopted**: Whether or not the pet was adopted (Yes/No).\n",
         "\n",
         "**Note**: This dataset is moved to a public Cloud Storage bucket and is accessed from there in this notebook."
       ]
@@ -143,15 +145,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. \n"
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -173,7 +182,9 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -184,11 +195,53 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ee775571c2b5"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -197,14 +250,9 @@
         "id": "before_you_begin:nogpu"
       },
       "source": [
-        "## Before you begin\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -216,90 +264,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"\n",
-        "DATA_REGION = \"US\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -330,7 +295,7 @@
         "id": "autoset_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -341,7 +306,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -352,7 +317,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -452,7 +417,9 @@
       "source": [
         "### Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+        "To use Vertex AI SDK for Python, you must [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your project.\n",
+        "\n",
+        "Now, initialize the Vertex AI SDK for Python using the project, location and bucket details."
       ]
     },
     {
@@ -463,7 +430,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -472,7 +439,7 @@
         "id": "BiVlyW5OUnjK"
       },
       "source": [
-        "## Create Vertex AI Dataset\n",
+        "## Create a Vertex AI tabular dataset\n",
         "\n",
         "Create a managed tabular dataset resource in Vertex AI using the dataset source."
       ]
@@ -485,6 +452,7 @@
       },
       "outputs": [],
       "source": [
+        "# Define the data source\n",
         "DATA_SOURCE = \"gs://cloud-samples-data/ai-platform-unified/datasets/tabular/petfinder-tabular-classification.csv\""
       ]
     },
@@ -496,7 +464,7 @@
       },
       "outputs": [],
       "source": [
-        "# Create the Vertex AI Dataset resource\n",
+        "# Create the Vertex AI dataset resource\n",
         "dataset = aiplatform.TabularDataset.create(\n",
         "    display_name=\"petfinder-tabular-dataset\",\n",
         "    gcs_source=DATA_SOURCE,\n",
@@ -511,9 +479,9 @@
         "id": "A-QQkeUnq8Xt"
       },
       "source": [
-        "## Train AutoML model\n",
+        "## Train an AutoML model\n",
         "\n",
-        "Train a simple regression model using the created dataset using `Age` as the target column. \n",
+        "Train a simple regression model using the created dataset resource and using `Age` as the target column. \n",
         "\n",
         "**Set a display name and create the `AutoMLTabularTrainingJob` with appropriate data types specified for column transformations.**"
       ]
@@ -526,6 +494,7 @@
       },
       "outputs": [],
       "source": [
+        "# Set a display name for your training job\n",
         "TRAINING_JOB_DISPLAY_NAME = \"[your-train-job-display-name]\"  # @param {type:\"string\"}"
       ]
     },
@@ -554,17 +523,17 @@
       "source": [
         "### Define AutoML Tabular training job\n",
         "\n",
-        "An AutoML training job is created with the `AutoMLTabularTrainingJob` class, with the following parameters:\n",
+        "An AutoML training job is created with the `AutoMLTabularTrainingJob` class using the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
         "- `optimization_prediction_type`: The type of prediction the AutoML Model is to produce. Ex: regression, classification.\n",
-        "- `column_transformations`: Transformations to apply to the input columns (i.e. columns other than the targetColumn). Each transformation may produce multiple result values from the column's value, and all are used for training. \n",
+        "- `column_transformations`: Transformations to apply to the input columns (i.e., columns other than the targetColumn). Each transformation may produce multiple result values from the column's value, and all are used for training. \n",
         "- `optimization_objective`: The optimization objective to minimize or maximize.\n",
         "    - `minimize-rmse`\n",
         "    - `minimize-mae`\n",
         "    - `minimize-rmsle`\n",
         "\n",
-        "Learn about [AutoMLTabularTrainingJob](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.AutoMLTabularTrainingJob)    "
+        "Learn more about [AutoMLTabularTrainingJob](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.AutoMLTabularTrainingJob)."
       ]
     },
     {
@@ -575,6 +544,7 @@
       },
       "outputs": [],
       "source": [
+        "# Define the training job\n",
         "train_job = aiplatform.AutoMLTabularTrainingJob(\n",
         "    display_name=TRAINING_JOB_DISPLAY_NAME,\n",
         "    optimization_prediction_type=\"regression\",\n",
@@ -644,17 +614,17 @@
       "source": [
         "#### Run the training job\n",
         "\n",
-        "Next, you start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, start the training job by invoking the `run` method with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Dataset` resource to train the model.\n",
+        "- `dataset`: The Vertex AI dataset resource to train the model.\n",
         "- `target_column`: The name of the column, whose values the model is to predict.\n",
         "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
         "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
         "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
         "- `model_display_name`: The human readable name for the trained model.\n",
-        "- `budget_milli_node_hours`: The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. \n",
+        "- `budget_milli_node_hours`: The train budget of creating this Model, expressed in milli node hours i.e., 1,000 value in this field means 1 node hour. \n",
         "\n",
-        "The training job takes roughly 3 hours to finish."
+        "**The training job takes about 2 hours to finish.**"
       ]
     },
     {
@@ -741,7 +711,7 @@
         "\n",
         "Specify the required parameters to run `evaluate` function. \n",
         "\n",
-        "The following is the instruction of `evaluate` function paramters:\n",
+        "The following parameters are passed as arguments to the `evaluate` function:\n",
         "\n",
         "- `prediction_type`: The problem type being addressed by this evaluation run. 'classification' and 'regression' are the currently supported problem types.\n",
         "- `target_field_name`: Name of the column to be used as the target for regression.\n",
@@ -759,6 +729,7 @@
       },
       "outputs": [],
       "source": [
+        "# Define and run the evaluation job\n",
         "job = model.evaluate(\n",
         "    prediction_type=\"regression\",\n",
         "    target_field_name=\"Age\",\n",
@@ -776,9 +747,9 @@
         "id": "U2zocUvk2YVs"
       },
       "source": [
-        "In the results from last step, click on the generated link to see your run in the Cloud Console.\n",
+        "In the results from last step, click on the generated link to see your run details in the Google Cloud console.\n",
         "\n",
-        "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n",
+        "In the Cloud console, many of the pipeline DAG nodes expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n",
         "<img src=\"images/automl_tabular_regression_evaluation_pipeline.PNG\" style=\"height:622px;width:726px\"></img>"
       ]
     },
@@ -788,7 +759,7 @@
         "id": "XcKaONSsGNC4"
       },
       "source": [
-        "### Get the Model Evaluation Results\n",
+        "### Get the model evaluation results\n",
         "\n",
         "After the evalution pipeline is finished, run the below cell to print the evaluation metrics."
       ]
@@ -801,6 +772,7 @@
       },
       "outputs": [],
       "source": [
+        "# Fetch the evaluation metrics\n",
         "model_evaluation = job.get_model_evaluation()"
       ]
     },
@@ -825,9 +797,7 @@
         "            or task.state == aiplatform_v1.types.PipelineTaskDetail.State.SKIPPED\n",
         "        )\n",
         "    ):\n",
-        "        evaluation_metrics = task.outputs.get(\"evaluation_metrics\").artifacts[\n",
-        "            0\n",
-        "        ]  # ['artifacts']\n",
+        "        evaluation_metrics = task.outputs.get(\"evaluation_metrics\").artifacts[0]\n",
         "        evaluation_metrics_gcs_uri = evaluation_metrics.uri\n",
         "\n",
         "print(evaluation_metrics)\n",
@@ -849,7 +819,7 @@
         "id": "14MBD57k0Fng"
       },
       "source": [
-        "After the evalution pipeline is finished, run the below cell to visualize the evaluation metrics."
+        "After the evalution pipeline has finished, run the below cell to visualize the evaluation metrics."
       ]
     },
     {
@@ -863,9 +833,9 @@
         "metrics = []\n",
         "values = []\n",
         "for i in evaluation_metrics.metadata.items():\n",
-        "    if (\n",
-        "        i[0] == \"meanAbsolutePercentageError\"\n",
-        "    ):  # we are not considering MAPE as it is infinite. MAPE is infinite if groud truth is 0 as in our case Age is 0 for some instances.\n",
+        "    # you aren't considering MAPE as it's infinite.\n",
+        "    # MAPE is infinite if groud truth is 0 as in our case Age is 0 for some instances.\n",
+        "    if i[0] == \"meanAbsolutePercentageError\":\n",
         "        continue\n",
         "    metrics.append(i[0])\n",
         "    values.append(i[1])\n",
@@ -882,11 +852,11 @@
         "id": "c26ad3958895"
       },
       "source": [
-        "### Get the Feature Attributions\n",
+        "### Get the feature attributions\n",
         "\n",
-        "Feature attributions indicate how much each feature in your model contributed to the predictions for each given instance.\n",
+        "Feature attributions indicate how much each feature in your model has contributed to the predictions for a given instance.\n",
         "\n",
-        "Learn more about [Feature Attributions](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#feature_attributions)\n",
+        "Learn more about [feature attributions](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#feature_attributions) in Vertex AI.\n",
         "\n",
         "Run the below cell to get the feature attributions. "
       ]
@@ -946,7 +916,7 @@
         "id": "77151be8d776"
       },
       "source": [
-        "### Visualize the Feature Attributions\n",
+        "### Visualize the feature attributions\n",
         "\n",
         "Visualize the obtained attributions for each feature using a bar-chart."
       ]
@@ -998,8 +968,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "# Delete model resource\n",
         "model.delete()\n",
         "\n",
@@ -1013,8 +981,8 @@
         "job.delete()\n",
         "\n",
         "# Delete Cloud Storage objects\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb

@@ -32,25 +32,28 @@
         "# Vertex AI Pipelines: Evaluating batch prediction results from AutoML video classification model\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>"
       ]
     },
     {
@@ -74,23 +77,23 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to train a Vertex AI AutoML Video classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:\n",
+        "In this tutorial, you learn how to train a Vertex AI AutoML video classification model and learn how to evaluate it through a Vertex AI pipeline job using google_cloud_pipeline_components:\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI `Datasets`\n",
-        "- Vertex AI `Training`(AutoML Video Classification) \n",
-        "- Vertex AI `Model Registry`\n",
-        "- Vertex AI `Pipelines`\n",
-        "- Vertex AI `Batch Predictions`\n",
+        "- Vertex AI dataset\n",
+        "- Vertex AI Training(AutoML video Classification) \n",
+        "- Vertex AI Model Registry\n",
+        "- Vertex AI Pipelines\n",
+        "- Vertex AI batch prediction\n",
         "\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a `Vertex AI Dataset`.\n",
-        "- Train a Automl Video Classification model on the `Vertex AI Dataset` resource.\n",
-        "- Import the trained `AutoML Vertex AI Model resource` into the pipeline.\n",
+        "- Create a Vertex AI dataset.\n",
+        "- Train a Automl video Classification model on the Vertex AI dataset resource.\n",
+        "- Import the trained AutoML Vertex AI Model resource into the pipeline.\n",
         "- Run a batch prediction job inside the pipeline.\n",
         "- Evaluate the AutoML model using the classification evaluation component.\n",
         "- Import the classification metrics to the AutoML Vertex AI Model resource."
@@ -120,11 +123,9 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and \n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -133,9 +134,8 @@
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started\n",
+        "Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -147,143 +147,97 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-pipeline-components==1.0.26 \\\n",
-        "                                 google-cloud-storage"
+        "                                 google-cloud-pipeline-components \\\n",
+        "                                 google-cloud-storage \\\n",
+        "                                 matplotlib"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "5eec42e37bcf"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "dcc98768955f"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "56e219dbcb9a"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "c97be6a73155"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fdaaecbb2a27"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e33244c6e6b5"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"\n",
-        "DATA_REGION = \"US\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -314,7 +268,7 @@
         "id": "autoset_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -325,7 +279,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -336,7 +290,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -457,7 +411,7 @@
       "source": [
         "### Location of training data\n",
         "\n",
-        "Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
+        "Now set the variable IMPORT_FILE to the location of the CSV index file in Cloud Storage."
       ]
     },
     {
@@ -483,7 +437,7 @@
         "\n",
         "This tutorial uses a version of the MIT Human Motion dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
         "\n",
-        "Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows."
+        "Start by doing a quick peek at the data. Count the number of examples by counting the number of rows in the CSV index file  (wc -l) and then peek at the first few rows."
       ]
     },
     {
@@ -509,10 +463,10 @@
       "source": [
         "### Create the Dataset\n",
         "\n",
-        "Next, create the `Vertex AI Dataset` resource using the `create` method for the `VideoDataset` class, which takes the following parameters:\n",
+        "Next, create the Vertex AI dataset resource using the `create` method for the VideoDataset class, which takes the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Vertex AI Dataset` resource.\n",
-        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Vertex AI Dataset` resource.\n",
+        "- display_name: The human readable name for the Vertex AI dataset resource.\n",
+        "- gcs_source: A list of one or more dataset index files to import the data items into the Vertex AI Dataset resource.\n",
         "\n",
         "This operation may take several minutes."
       ]
@@ -551,13 +505,13 @@
         "\n",
         "#### Create the training pipeline\n",
         "\n",
-        "An AutoML training pipeline is created with the `AutoMLVideoTrainingJob` class, with the following parameters:\n",
+        "An AutoML training pipeline is created with the AutoMLVideoTrainingJob class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
-        "- `prediction_type`: The type task to train the model for.\n",
-        "  - `classification`: A video classification model.\n",
-        "  - `object_tracking`: A video object tracking model.\n",
-        "  - `action_recognition`: A video action recognition model.\n"
+        "- display_name: The human readable name for the TrainingJob resource.\n",
+        "- prediction_type: The type task to train the model for.\n",
+        "  - classification: A video classification model.\n",
+        "  - object_tracking: A video object tracking model.\n",
+        "  - action_recognition: A video action recognition model.\n"
       ]
     },
     {
@@ -584,14 +538,14 @@
       "source": [
         "#### Run the training pipeline\n",
         "\n",
-        "Next, you run the job to start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next,run the job to start the training job by invoking the `run` method with the following parameters:\n",
         "\n",
-        "- `dataset`: The `Vertex AI Dataset` resource to train the model.\n",
-        "- `model_display_name`: The human readable name for the trained model.\n",
-        "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
-        "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
+        "- dataset: The Vertex AI dataset resource to train the model.\n",
+        "- model_display_name: The human readable name for the trained model.\n",
+        "- training_fraction_split: The percentage of the dataset to use for training.\n",
+        "- test_fraction_split: The percentage of the dataset to use for testing (holdout data).\n",
         "\n",
-        "The `run` method when completed returns the `Model` resource.\n",
+        "The `run` method when completed returns the Model resource.\n",
         "\n",
         "The execution of the training pipeline can take over 24 hours to complete."
       ]
@@ -739,11 +693,11 @@
         "\n",
         "Now, make an input file for your evaluation pipeline and store it in the Cloud Storage bucket. The input file is stored in JSONL format for this tutorial. In the JSONL file, you make one dictionary entry per line for each video file. The dictionary contains the following key-value pairs:\n",
         "\n",
-        "- `content`: The Cloud Storage path to the video.\n",
-        "- `mimeType`: The content type. In our example, it is a `avi` file.\n",
-        "- `timeSegmentStart`: The start timestamp in the video to do prediction on. *Note*, the timestamp must be specified as a string and followed by s (second), m (minute) or h (hour).\n",
-        "- `timeSegmentEnd`: The end timestamp in the video to do prediction on.\n",
-        "- `outputLabel`: The batch prediction labels."
+        "- content: The Cloud Storage path to the video.\n",
+        "- mimeType: The content type. In our example, it's an avi file.\n",
+        "- timeSegmentStart: The start timestamp in the video to do prediction on. *Note*, the timestamp must be specified as a string and followed by s (second), m (minute) or h (hour).\n",
+        "- timeSegmentEnd: The end timestamp in the video to do prediction on.\n",
+        "- outputLabel: The batch prediction labels."
       ]
     },
     {
@@ -787,7 +741,7 @@
       },
       "source": [
         "### Check input content\n",
-        "Check the contents of the `ground_truth.jsonl`."
+        "Check the contents of the ground_truth.jsonl."
       ]
     },
     {
@@ -811,7 +765,7 @@
         "\n",
         "Now, you run a Vertex AI batch prediction job and generate evaluations and feature attributions on its results using a pipeline. \n",
         "\n",
-        "To do so, you create a Vertex AI pipeline by calling `evaluate` function. Learn more about [evaluate function](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L5127)."
+        "To do so, create a Vertex AI pipeline by calling the `evaluate` function. Learn more about [evaluate function](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L5127)."
       ]
     },
     {
@@ -822,15 +776,15 @@
       "source": [
         "### Define parameters to run the evaluate function\n",
         "\n",
-        "Specify the required parameters to run `evaluate` function. \n",
+        "Specify the required parameters to run the `evaluate` function. \n",
         "\n",
-        "The following is the instruction of `evaluate` function paramters:\n",
+        "The `evaluate` function  parameters are as follows:\n",
         "\n",
-        "- `prediction_type`: The problem type being addressed by this evaluation run. 'classification' and 'regression' are the currently supported problem types.\n",
-        "- `target_field_name`: Name of the column to be used as the target for classification.\n",
-        "- `gcs_source_uris`: List of the Cloud Storage bucket uris of input instances for batch prediction.\n",
-        "- `class_labels`: List of class labels in the target column.\n",
-        "- `generate_feature_attributions`: Optional. Whether the model evaluation job should generate feature attributions. Defaults to False if not specified."
+        "- prediction_type: The problem type being addressed by this evaluation run. 'classification' and 'regression' are the currently supported problem types.\n",
+        "- target_field_name: Name of the column to be used as the target for classification.\n",
+        "- gcs_source_uris: List of the Cloud Storage bucket uris of input instances for batch prediction.\n",
+        "- class_labels: List of class labels in the target column.\n",
+        "- generate_feature_attributions: Optional. Whether the model evaluation job should generate feature attributions. Defaults to False ."
       ]
     },
     {
@@ -978,7 +932,7 @@
         "job.delete()\n",
         "\n",
         "# Delete the Cloud storage bucket\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb

@@ -32,24 +32,26 @@
         "# Vertex AI Pipelines: Evaluating batch prediction results from custom tabular regression model\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_evaluation%2Fcustom_tabular_regression_model_evaluation.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -74,7 +76,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:\n",
+        "In this tutorial, you learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using google cloud pipeline components.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
@@ -86,14 +88,14 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create a Vertex AI `CustomTrainingJob` for training a model.\n",
-        "- Run the `CustomTrainingJob` \n",
+        "- Create a Vertex AI Custom Training Job to train a TensorFlow model.\n",
+        "- Run the custom training job. \n",
         "- Retrieve and load the model artifacts.\n",
         "- View the model evaluation.\n",
-        "- Upload the model as a Vertex AI Model resource.\n",
-        "- Import a pre-trained `Vertex AI model resource` into the pipeline.\n",
-        "- Run a `batch prediction` job in the pipeline.\n",
-        "- Evaluate the model using the `regression evaluation component`.\n",
+        "- Upload the model as a Vertex AI model resource.\n",
+        "- Import a pre-trained Vertex AI model resource into the pipeline.\n",
+        "- Run a batch prediction job in the pipeline.\n",
+        "- Evaluate the model using the regression evaluation component.\n",
         "- Import the Regression Metrics to the Vertex AI model resource."
       ]
     },
@@ -105,7 +107,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset you use in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
+        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset you use in this tutorial is the one that's available from TensorFlow SDK. The trained model predicts the median price of a house in units of 1K USD."
       ]
     },
     {
@@ -131,12 +133,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. \n"
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -148,7 +157,7 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet    google-cloud-aiplatform \\\n",
-        "                                    tensorflow \\\n",
+        "                                    tensorflow==2.15.1 \\\n",
         "                                    google-cloud-pipeline-components==1.0.26 \\\n",
         "                                    matplotlib \\\n",
         "                                    google-cloud-storage "
@@ -157,136 +166,92 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"\n",
-        "DATA_REGION = \"US\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -328,7 +293,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -339,7 +304,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -427,11 +392,10 @@
         "import json\n",
         "import os\n",
         "\n",
-        "import google.cloud.aiplatform as aip\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import tensorflow as tf\n",
-        "from google.cloud import aiplatform_v1\n",
+        "from google.cloud import aiplatform, aiplatform_v1\n",
         "from tensorflow.keras.datasets import boston_housing"
       ]
     },
@@ -454,7 +418,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI, location=LOCATION)"
       ]
     },
     {
@@ -467,16 +431,16 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you'd specify:\n",
         "\n",
-        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
         "Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) \n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "*Note*: TF releases before 2.3 for GPU support fail to load the custom model in this tutorial. It's a known issue and fixed in TF 2.3 which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -489,7 +453,7 @@
       "source": [
         "if os.getenv(\"IS_TESTING_TRAIN_GPU\"):\n",
         "    TRAIN_GPU, TRAIN_NGPU = (\n",
-        "        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
+        "        aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4,\n",
         "        int(os.getenv(\"IS_TESTING_TRAIN_GPU\")),\n",
         "    )\n",
         "else:\n",
@@ -497,7 +461,7 @@
         "\n",
         "if os.getenv(\"IS_TESTING_DEPLOY_GPU\"):\n",
         "    DEPLOY_GPU, DEPLOY_NGPU = (\n",
-        "        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
+        "        aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4,\n",
         "        int(os.getenv(\"IS_TESTING_DEPLOY_GPU\")),\n",
         "    )\n",
         "else:\n",
@@ -577,12 +541,12 @@
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -620,7 +584,7 @@
       "source": [
         "## Training a custom model\n",
         "\n",
-        "Now you are ready to start creating your own custom model and training for Boston Housing. \n",
+        "Now you're ready to start creating your own custom model and training for Boston Housing. \n",
         "\n",
         "Learn more about [custom model training on Vertex AI](https://cloud.google.com/vertex-ai/docs/training/custom-training)\n",
         "\n",
@@ -628,7 +592,7 @@
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the training, you look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -642,7 +606,7 @@
         "\n",
         "The file `trainer/task.py` is the Python script for executing the custom training job. \n",
         "\n",
-        "**Note:** When you refer to it in the worker pool specification, you replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "**Note:** when `trainer/task.py` is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (.py) is dropped (trainer.task).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
@@ -686,10 +650,12 @@
       "source": [
         "#### Create task.py\n",
         "\n",
-        "In the next cell, you write the contents of the training script task.py.  In summary:\n",
+        "In the next cell, write the contents of the training script *task.py*.\n",
         "\n",
-        "- Get the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
-        "- Loads Boston Housing dataset from TF.Keras builtin datasets\n",
+        "To summarize, the script performs the following steps:\n",
+        "\n",
+        "- Gets the directory for where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
+        "- Loads Boston Housing dataset from TF.Keras built-in datasets.\n",
         "- Builds a simple deep neural network model using TF.Keras model API.\n",
         "- Compiles the model (`compile()`).\n",
         "- Sets a training distribution strategy according to the argument `args.distribute`.\n",
@@ -825,9 +791,9 @@
         "id": "tarball_training_script"
       },
       "source": [
-        "**Store the training script on your Cloud Storage bucket.**\n",
+        "### Store the training script in your Cloud Storage bucket.\n",
         "\n",
-        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the training folder into a compressed tar ball, and then store the folder in your Cloud Storage bucket."
       ]
     },
     {
@@ -857,11 +823,11 @@
         "\n",
         "1) Create a custom training job\n",
         "\n",
-        "2) Run the job\n",
+        "2) Specify your training parameters and run the job.\n",
         "\n",
         "#### Create a custom training job\n",
         "\n",
-        "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
+        "A custom training job is created using the `CustomTrainingJob` class, with the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the custom training job\n",
         "- `container_uri`: The training container image\n",
@@ -877,7 +843,7 @@
       },
       "outputs": [],
       "source": [
-        "train_job = aip.CustomTrainingJob(\n",
+        "train_job = aiplatform.CustomTrainingJob(\n",
         "    display_name=\"boston\",\n",
         "    script_path=\"custom/trainer/task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
@@ -893,16 +859,16 @@
         "id": "prepare_custom_cmdargs"
       },
       "source": [
-        "#### Prepare your command-line arguments\n",
+        "#### Prepare your training parameters\n",
         "\n",
         "Now define the command-line arguments for your custom training container:\n",
         "\n",
-        "- `args`: The command-line arguments to pass to the executable that is set as the entry point into the container.\n",
-        "  - `--model-dir` : For our examples, we use this command-line argument to specify where to store the model artifacts.\n",
-        "      - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
-        "      - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
-        "  - `\"--epochs=\" + EPOCHS`: The number of epochs for training.\n",
-        "  - `\"--steps=\" + STEPS`: The number of steps per epoch."
+        "- `args`: The command-line arguments to pass to the executable that's set as the entry point into the container.\n",
+        "    - `--model-dir`: Command-line argument to specify where to store the model artifacts. You can use either of the following methods to specify the storage location for artifacts.\n",
+        "        - **method-1**(set `DIRECT` to `True`): You pass the Cloud Storage location as a command line argument to your training script.\n",
+        "        - **method-2**(set `DIRECT` to `False`): The service passes the Cloud Storage location as the environment variable AIP_MODEL_DIR to your training script. In this case, you tell the service the model artifact location in the job specification.\n",
+        "    - `--epochs`: The number of epochs for training.\n",
+        "    - `--steps`: The number of steps per epoch."
       ]
     },
     {
@@ -947,8 +913,8 @@
         "- `machine_type`: The machine type for the compute instances.\n",
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
-        "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
-        "- `sync`: Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed."
+        "- `base_output_dir`: The Cloud Storage location to write the model artifacts.\n",
+        "- `sync`: Set **True** to wait until the completion of the job."
       ]
     },
     {
@@ -989,9 +955,9 @@
       "source": [
         "#### Load the saved model\n",
         "\n",
-        "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket.  Now load it from the Cloud Storage bucket, and then you can perform tasks such as model evaluation and make prediction requests.\n",
+        "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Once you load the model from the Cloud Storage bucket you can run model evaluation and prepare it for prediction requests.\n",
         "\n",
-        "To load the model, you pass the Cloud Storage path \"MODEL_DIR\" to the `tf.saved_model.load()` method."
+        "To load the model, pass the Cloud Storage path \"MODEL_DIR\" to the `tf.saved_model.load()` method."
       ]
     },
     {
@@ -1052,10 +1018,10 @@
         "\n",
         "### Explanation Specification\n",
         "\n",
-        "To get explanations when doing a prediction, you must enable the explanation feature and set corresponding settings when you upload your custom model to Vertex AI Model registry. These settings are referred to as the explanation metadata, which consists of:\n",
+        "To get explanations for the predictions, you must enable the explanation feature and set corresponding settings when you upload your custom model to Vertex AI Model Registry. These settings are referred to as the explanation metadata, which consists of:\n",
         "\n",
         "- `parameters`: Specification for the explainability algorithm to use for explanations on your model. You can choose between:\n",
-        "  - Shapley (not recommended for image data as the computation can take long)\n",
+        "  - Shapley (**Note**: not recommended for image data since can involve a long-running operation)\n",
         "  - XRAI\n",
         "  - Integrated Gradients\n",
         "- `metadata`: Specification for how the algoithm is applied on your custom model\n",
@@ -1064,7 +1030,7 @@
         "\n",
         "\n",
         "\n",
-        "In the next code cell, you define the parameters."
+        "In the next code cell, set the variable `XAI` to the explainabilty algorithm that you use on your custom model."
       ]
     },
     {
@@ -1084,7 +1050,7 @@
         "elif XAI == \"xrai\":\n",
         "    PARAMETERS = {\"xrai_attribution\": {\"step_count\": 50}}\n",
         "\n",
-        "parameters = aip.explain.ExplanationParameters(PARAMETERS)"
+        "parameters = aiplatform.explain.ExplanationParameters(PARAMETERS)"
       ]
     },
     {
@@ -1093,7 +1059,7 @@
         "id": "781989a46a3b"
       },
       "source": [
-        "**In the next code cell, you define the metadata**"
+        "In the next code cell, define the metadata."
       ]
     },
     {
@@ -1127,10 +1093,10 @@
         "\n",
         "OUTPUT_METADATA = {\"output_tensor_name\": serving_output}\n",
         "\n",
-        "input_metadata = aip.explain.ExplanationMetadata.InputMetadata(INPUT_METADATA)\n",
-        "output_metadata = aip.explain.ExplanationMetadata.OutputMetadata(OUTPUT_METADATA)\n",
+        "input_metadata = aiplatform.explain.ExplanationMetadata.InputMetadata(INPUT_METADATA)\n",
+        "output_metadata = aiplatform.explain.ExplanationMetadata.OutputMetadata(OUTPUT_METADATA)\n",
         "\n",
-        "metadata = aip.explain.ExplanationMetadata(\n",
+        "metadata = aiplatform.explain.ExplanationMetadata(\n",
         "    inputs={\"features\": input_metadata}, outputs={\"medv\": output_metadata}\n",
         ")"
       ]
@@ -1141,21 +1107,20 @@
         "id": "ed414a2f945a"
       },
       "source": [
-        "### Make instance schema and prediction schema yaml files\n",
+        "### Create instance schema and prediction schema yaml files\n",
         "\n",
-        "In next cells, you write the contents of **instance_schema.yaml** and **prediction_schema.yaml** files. Content structure is same for both files.\n",
+        "In next cells, write the contents of *instance_schema.yaml* and *prediction_schema.yaml* files. Content structure is the same for both files.\n",
         "\n",
         "\n",
-        "#### Make instance schema yaml file\n",
+        "#### Create instance schema yaml file\n",
         "\n",
-        "In the next cell, you write the contents of the instance_schema.yaml . You write the structure about the prediction instances you give to your batch prediction .\n",
+        "The *instance_schema.yaml* file defines the structure of the prediction instances you provide to your batch predictions. \n",
         "\n",
         "- Specify the title and description.\n",
         "- Specify type of the input. In your case input layer to batch prediction is \n",
         "**{\"dense_input\": [0.02715405449271202, 0.0, 0.027177177369594574, 0.0, 0.0010195195209234953, 0.009660660289227962, 0.1501501500606537, 0.0027548049110919237, 0.036036036908626556, 1.0, 0.03033033013343811, 0.04091591760516167, 0.043618619441986084]}**\n",
         "which is an object. Inside object, there are properties like dense_input.\n",
-        "- Specify description about property\n",
-        "- For each property, mention its type. \n",
+        "- For each property, provide description and mention its type. \n",
         "- If type of the property is an array, mention the information about array items in `items` key.\n"
       ]
     },
@@ -1188,10 +1153,9 @@
         "id": "53f324aaf19a"
       },
       "source": [
-        "#### Make prediction schema yaml file\n",
-        "\n",
-        "In the next cell, you write the contents of the prediction_schema.yaml . You write the structure about the prediction output you get from your batch prediction job.\n",
+        "#### Create prediction schema yaml file\n",
         "\n",
+        "The *prediction_schema.yaml* file defines the structure of the prediction output you get from your batch prediction job. \n",
         "\n",
         "Output of batch prediction job is \"prediction\": [value], which is of type array."
       ]
@@ -1228,8 +1192,8 @@
       },
       "outputs": [],
       "source": [
-        "!gsutil cp instance_schema.yaml $BUCKET_URI/instance_schema.yaml\n",
-        "!gsutil cp prediction_schema.yaml $BUCKET_URI/prediction_schema.yaml"
+        "!gsutil cp instance_schema.yaml {BUCKET_URI}/instance_schema.yaml\n",
+        "!gsutil cp prediction_schema.yaml {BUCKET_URI}/prediction_schema.yaml"
       ]
     },
     {
@@ -1238,22 +1202,22 @@
         "id": "upload_model:mbsdk"
       },
       "source": [
-        "### Upload the model\n",
+        "## Upload the model\n",
         "\n",
-        "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
+        "Next, upload your model to Vertex AI Model Registry using `Model.upload()` method, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Model` resource.\n",
+        "- `display_name`: The human readable name for the model resource.\n",
         "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
         "- `serving_container_image_uri`: The serving container image.\n",
         "- `instance_schema_uri`: Points to a YAML file stored on Google Cloud Storage describing the format of a single instance.\n",
         "- `prediction_schema_uri`: Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this model.\n",
         "- `sync`: Whether to execute the upload asynchronously or synchronously.\n",
-        "- `explanation_parameters`: Parameters to configure explaining for `Model`'s predictions.\n",
-        "- `explanation_metadata`: Metadata describing the `Model`'s input and output for explanation.\n",
+        "- `explanation_parameters`: Parameters to configure explaining for model's predictions.\n",
+        "- `explanation_metadata`: Metadata describing the model's input and output for explanation.\n",
         "\n",
         "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method.\n",
         "\n",
-        "**Note:** If you want to configure explanations for the model, set `explanation_parameters`,  `explanation_metadata` parameters. Otherwise do not set them."
+        "**Note:** If you want to configure explanations for the model, set `explanation_parameters`,  `explanation_metadata` parameters. Otherwise don't set them."
       ]
     },
     {
@@ -1264,7 +1228,7 @@
       },
       "outputs": [],
       "source": [
-        "model = aip.Model.upload(\n",
+        "model = aiplatform.Model.upload(\n",
         "    display_name=\"boston_new_model\",\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
@@ -1286,14 +1250,11 @@
       "source": [
         "### Load data for the pipeline\n",
         "\n",
-        "You load the Boston Housing test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the feature data, and the corresponding labels (median value of owner-occupied home).\n",
+        "Load the Boston Housing test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the feature data, and the corresponding labels (median value of owner-occupied home).\n",
         "\n",
-        "You don't need the training data, and hence you load it as `(_, _)`.\n",
+        "You don't need the training data, and therefore you load it as `(_, _)`.\n",
         "\n",
-        "Before you can run the data through the pipeline, you need to preprocess it:\n",
-        "\n",
-        "`x_test`:\n",
-        "1. Normalize (rescale) the data in each column by dividing each value by the maximum value of that column. This replaces each single value with a 32-bit floating point number between 0 and 1."
+        "Before you can run the data through the pipeline, you need to preprocess it. Normalize (rescale) the data in each column by dividing each value by the maximum value of that column. This replaces each single value with a 32-bit floating point number between 0 and 1."
       ]
     },
     {
@@ -1327,9 +1288,9 @@
         "id": "make_batch_file:custom,tabular"
       },
       "source": [
-        "### Make the input file to the pipeline\n",
+        "### Prepare the input file for the pipeline\n",
         "\n",
-        "Now make a input file, which you store in your local Cloud Storage bucket.  Each instance in the file is a dictionary entry of the form:\n",
+        "Prepare an input file and store it in your Cloud Storage bucket. Each instance in the file is a dictionary entry of the form:\n",
         "\n",
         "                        {serving_input: content, grount_truth_column:value}\n",
         "\n",
@@ -1364,7 +1325,7 @@
       "source": [
         "## Model Evaluation\n",
         "\n",
-        "Now, you run a Vertex AI BatchPrediction job and generate evaluations and feature-attributions on its results by creating a Vertex AI pipeline using `evaluate` function. Learn more about [evaluate function](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L5127)."
+        "Now, run a Vertex AI Batch Prediction job and generate evaluations and feature-attributions on its results by creating a Vertex AI pipeline using `evaluate` function. Learn more about [evaluate function](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L5127)."
       ]
     },
     {
@@ -1382,7 +1343,7 @@
         "- `prediction_type`: The problem type being addressed by this evaluation run. 'classification' and 'regression' are the currently supported problem types.\n",
         "- `target_field_name`: Name of the column to be used as the target for regression.\n",
         "- `gcs_source_uris`: List of the Cloud Storage bucket uris of input instances for batch prediction.\n",
-        "- `generate_feature_attributions`: Optional. Whether the model evaluation job should generate feature attributions. Defaults to False if not specified.\n",
+        "- `generate_feature_attributions`: (**Optional**) Whether the model evaluation job should generate feature attributions. Defaults to False if not specified.\n",
         "\n",
         "**The pipeline takes about 1 hour to complete.**"
       ]
@@ -1423,7 +1384,7 @@
       "source": [
         "##### Runtime Graph of Model Evaluation pipeline\n",
         "\n",
-        "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n",
+        "In the UI, you can click on the DAG nodes to expand or collapse them. Here's a partially-expanded view of the DAG (click image to see larger version).\n",
         "\n",
         "<img src=\"images/custom_tabular_regression_evaluation_pipeline.PNG\" style=\"height:622px;width:726px\"></img>\n",
         "\n",
@@ -1633,8 +1594,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "# Delete model resource\n",
         "model.delete()\n",
         "\n",
@@ -1644,9 +1603,24 @@
         "# Delete the evaluation pipeline\n",
         "job.delete()\n",
         "\n",
+        "# Delete the batch prediction jobs\n",
+        "batch_prediction_jobs = aiplatform.BatchPredictionJob.list()\n",
+        "for batch_prediction_job in batch_prediction_jobs:\n",
+        "    if any(\n",
+        "        keyword in batch_prediction_job.display_name\n",
+        "        for keyword in [\n",
+        "            \"model-registry-batch-predict-evaluation\",\n",
+        "            \"model-registry-batch-explain-evaluation\",\n",
+        "        ]\n",
+        "    ):\n",
+        "        batch_prediction_job.delete()\n",
+        "\n",
+        "# Delete locally generated files\n",
+        "! rm -rf custom custom.tar.gz instance_schema.yaml prediction_schema.yaml\n",
+        "\n",
         "# Delete Cloud Storage objects\n",
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/model_evaluation/model_based_llm_evaluation/autosxs_llm_evaluation_for_summarization_task.ipynb

@@ -65,7 +65,7 @@
         "\n",
         "This notebook demonstrates how to use Vertex AI automatic side-by-side (AutoSxS) to evaluate the performance between a generative AI model in Vertex AI Model Registry and a third-party language model.\n",
         "\n",
-        "AutoSxS is a model-assisted evaluation tool that helps you compare two large language models (LLMs) side by side. As part of AutoSxS's preview release, we only support comparing models for summarization and question answering tasks. We will support more tasks and customization in the future.\n",
+        "AutoSxS is a model-assisted evaluation tool that helps you compare two large language models (LLMs) side by side. AutoSxS GA currently supports comparing models for summarization and question answering tasks only. We will support more tasks and customization in the future.\n",
         "\n",
         "Learn more about [Vertex AI AutoSxS Model Evaluation](https://cloud.google.com/vertex-ai/docs/generative-ai/models/side-by-side-eval#autosxs)."
       ]
@@ -83,9 +83,9 @@
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
         "- Cloud Storage\n",
-        "- Vertex AI PaLM API\n",
+        "- Vertex AI Gemini API\n",
         "- Vertex AI Pipelines\n",
-        "- Vertex AI Batch Prediction\n",
+        "- Vertex AI batch prediction\n",
         "\n",
         "\n",
         "The steps performed include:\n",
@@ -454,7 +454,7 @@
         "import datasets\n",
         "\n",
         "# Download the dataset.\n",
-        "raw_datasets = datasets.load_dataset(\"xsum\", split=\"train\")\n",
+        "raw_datasets = datasets.load_dataset(\"xsum\", split=\"train\", trust_remote_code=True)\n",
         "\n",
         "# Fetch 10 examples from the original dataset.\n",
         "datasets_10 = raw_datasets.select(range(40, 50))\n",
@@ -555,7 +555,9 @@
         "  - **model_a:** A fully-qualified model resource name. This parameter is optional\n",
         "      if Model A responses are specified.\n",
         "  - **model_a_prompt_parameters:** Map of Model A prompt template parameters to\n",
-        "      columns or templates. In the case of [text-bison](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#request_body), the only parameter needed is `prompt`.\n",
+        "      columns or templates.\n",
+        "      - For [text-bison](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#request_body), the only parameter needed is `prompt`.\n",
+        "      - For [gemini](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#request), the valid parameters are `contents` and `system_instruction`.\n",
         "  - **model_a_parameters:** The parameters that govern the predictions from model A such as the model temperature.\n",
         "\n",
         "**Model Parameters if bringing your own predictions (assuming Model A):**\n",
@@ -577,7 +579,7 @@
         "id": "veq26QZ7OMoC"
       },
       "source": [
-        "In this notebook, we will evaluate a third-party model's predictions (located in the `summary` column of `DATASET`) against the output of `text-bison@001` using a built-in summarization instruction. The task being performed is summarization.\n",
+        "In this notebook, we will evaluate a third-party model's predictions (located in the `summary` column of `DATASET`) against the output of `gemini-1.5-pro` using a built-in summarization instruction. The task being performed is summarization.\n",
         "\n",
         "First, compile the AutoSxS pipeline locally."
       ]
@@ -628,11 +630,10 @@
         "        \"inference_instruction\": {\"template\": \"{{ default_instruction }}\"},\n",
         "    },\n",
         "    \"task\": \"summarization\",\n",
-        "    \"model_a\": \"publishers/google/models/text-bison@001\",\n",
+        "    \"model_a\": \"publishers/google/models/gemini-1.5-pro-001\",\n",
         "    \"model_a_prompt_parameters\": {\n",
-        "        \"prompt\": {\n",
-        "            \"template\": \"{{ default_instruction }}: {{\" + prompt_column + \"}}.\",\n",
-        "            # 'template': 'Summarize the following: {{' + prompt_column + \"}}.\",  - This is also okay.\n",
+        "        \"contents\": {\n",
+        "            \"column\": prompt_column,\n",
         "        },\n",
         "    },\n",
         "    \"response_column_b\": response_column_b,\n",

notebooks/official/model_monitoring/get_started_with_model_monitoring_automl.ipynb

@@ -298,7 +298,7 @@
         "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
         "\n",
         "\n",
-        "1. Go to [IAM console](https://console.cloud.google.com/iam-admin/iam).\n",
+        "1. Go to the [IAM console](https://console.cloud.google.com/iam-admin/iam).\n",
         "2. Check the **Include Google-provided role grants** checkbox.\n",
         "3. Find the above emails.\n",
         "4. Grant the corresponding roles.\n",
@@ -363,7 +363,7 @@
       "source": [
         "### Create BigQuery client\n",
         "\n",
-        "In this tutorial, you use data from the public BigQuery table. You create a client interface, which you subsequently use to access the data."
+        "In this tutorial, you use data from the public BigQuery table. You create a client interface, which you then use to access the data."
       ]
     },
     {
@@ -385,7 +385,7 @@
       "source": [
         "## Introduction to Vertex AI Model Monitoring\n",
         "\n",
-        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular models. You can monitor for skewness and drift detection of the features in the inbound prediction requests or the feature attributions (Explainable AI) in the outbound prediction response. In other words, you monitor the distribution of the attributions that quantify feature contributions to the output (predictions).\n",
+        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular models. You can monitor for skewness and drift detection of the features in the inbound prediction requests or the feature attributions (Explainable AI) in the outbound prediction response. That is, you monitor the distribution of the attributions that quantify feature contributions to the output (predictions).\n",
         "\n",
         "The following are the basic steps to enable model monitoring:\n",
         "\n",
@@ -394,7 +394,7 @@
         "3. Upload the model monitoring specification to the Vertex AI endpoint.\n",
         "4. Upload schema or use automatic generation of the *input schema* for parsing.\n",
         "5. For feature skewness detection, upload the training data. This enables automatic generation of the feature distributions.\n",
-        "6. For feature attributions, upload corresponding *Vertex Explainable AI* specification.\n",
+        "6. For feature attributions, upload the corresponding *Vertex Explainable AI* specification.\n",
         "\n",
         "Once configured, you can enable or disable monitoring, change alerts and update the model monitoring configuration. \n",
         "\n",
@@ -404,7 +404,7 @@
         "\n",
         "For skewness detection, the monitoring service requires a baseline for the statistical distribution of values in the training data. For AutoML tabular models this is automatically derived. For custom tabular models, you upload the training data to the service, and have the service automatically derive the distribution.\n",
         "\n",
-        "For feature attribution skewness and drift detection in feature attributions, you're required to enable Vertex Explainable AI feature for your deployed custom tabular models. For AutoML models, Vertex Explainable AI is automatically enabled.\n",
+        "For feature attribution skewness and drift detection in feature attributions, you're required to enable the Vertex Explainable AI feature for your deployed custom tabular models. For AutoML models, Vertex Explainable AI is automatically enabled.\n",
         "\n",
         "Learn more about [Introduction to Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring/overview)."
       ]
@@ -552,7 +552,7 @@
         "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
         "- `target_column`: The name of the column to train as the label.\n",
         "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
-        "- `disable_early_stopping`: If `False`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
+        "- `disable_early_stopping`: If `False`, training may be completed before using the entire budget if the service detects that it can't further improve the model objective measurements.\n",
         "\n",
         "The `run` method when completed returns the model resource.\n",
         "\n",
@@ -563,7 +563,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "run_automl_pipeline:tabular"
+        "id": "cbedbbde33ca"
       },
       "outputs": [],
       "source": [
@@ -981,13 +981,15 @@
       },
       "outputs": [],
       "source": [
-        "for instance in instances:\n",
+        "responses = []\n",
+        "for i, instance in enumerate(instances):\n",
         "    response = endpoint.predict(instances=[instance])\n",
-        "\n",
-        "prediction = response[0]\n",
+        "    responses.append(response)\n",
+        "    if i % 100 == 0:\n",
+        "        print(f\"Completed {i} rows.\")\n",
         "\n",
         "# print the prediction for the first instance\n",
-        "print(prediction[0])"
+        "print(responses[0])"
       ]
     },
     {
@@ -1000,7 +1002,7 @@
         "\n",
         "Once the monitoring service has started, the sampled prediction requests are logged to Cloud Storage. On the next monitoring interval, the sampled predictions are copied to the BigQuery logging table. Once the entries are logged, the monitoring service analyzes the sampled data.\n",
         "\n",
-        "Next, you wait for the first logged entries to appear in the BigQuery logging table for prediction samples. Since you sent 1000 prediction requests, with 50% sampling, you should see around 500 entries.s.g, you should see around 500 entries."
+        "Next, you wait for the first logged entries to appear in the BigQuery logging table for prediction samples. Since you sent 1000 prediction requests, with 50% sampling, you should see around 500 entries."
       ]
     },
     {
@@ -1144,13 +1146,15 @@
       },
       "outputs": [],
       "source": [
-        "for instance in instances:\n",
+        "responses = []\n",
+        "for i, instance in enumerate(instances):\n",
         "    response = endpoint.predict(instances=[instance])\n",
-        "\n",
-        "prediction = response[0]\n",
+        "    responses.append(response)\n",
+        "    if i % 100 == 0:\n",
+        "        print(f\"Completed {i} rows.\")\n",
         "\n",
         "# print the prediction for the first instance\n",
-        "print(prediction[0])"
+        "print(responses[0])"
       ]
     },
     {
@@ -1163,7 +1167,7 @@
         "\n",
         "On the next monitoring interval, the sampled predictions are copied to the BigQuery logging table. Once the entries are logged, the monitoring service analyzes the sampled data.\n",
         "\n",
-        "Next, you wait for the first logged entries to appear in the BigQuery logging table for prediction samples. Since you sent 1000 prediction requests, with 50% sampling, you should see around 1000 entries."
+        "Next, you wait for the first logged entries to appear in the BigQuery logging table for prediction samples. Since you sent 1000 prediction requests, with 50% sampling, you should see around 500 entries."
       ]
     },
     {

notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb

@@ -32,36 +32,27 @@
         "# Vertex AI Model Monitoring for online prediction in AutoML image models\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "\n",
-        "  <td>\n",
-        "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "        </a>\n",
-        "  </td>\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_monitoring%2Fget_started_with_model_monitoring_automl_image_online.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "\n",
-        "</table>\n",
-        "<br/><br/><br/>\n",
-        "\n",
-        "**This feature is in experimental**. You can request access to model monitoring for AutoML image models with this [Form](https://docs.google.com/forms/d/1aTitFrlNRlUAF_gvbMFQWDY0Oq8T_fUrN1m79atF8xQ/edit?resourcekey=0-psKsTGtVUFFUBDL2timIQA).\n",
-        "\n",
-        "If after access is granted and your monitoring job fails to start for this reason `Training Datasets for AutoML Deployed Models no longer available, please explicitly configure Analysis Instance Schema`, you need to request that your project be added to the group `VISION_MM_EXP`.\n",
-        "\n",
-        "## LEGAL NOTICE\n",
-        "\n",
-        "This is an Experimental release. Experiments are focused on validating a prototype and are not guaranteed to be released. Experiments are covered by the [Pre-GA Offerings Terms](https://cloud.google.com/terms/service-terms) of the Google Cloud Platform Terms of Service. They are not intended for production use or covered by any SLA,  support obligation, or deprecation policy and might be subject to backward-incompatible changes."
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -160,15 +151,22 @@
         "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "install_mlops"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the following packages for further running this notebook."
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -185,51 +183,90 @@
         "    ! rm -rf /workspace/workspace/env/lib/python3.9/site-packages/protobuf-4.23.0.dist-info/\n",
         "\n",
         "! pip3 install --quiet -U   google-cloud-aiplatform \"shapely<2\" \\\n",
-        "                            tensorflow==2.7 \\\n",
+        "                            tensorflow==2.15.1 \\\n",
         "                            pandas-gbq\n",
         "\n",
-        "! pip3 install --quiet --force-reinstall google-api-core==2.10 \\\n",
-        "                                         protobuf==3.20.3 "
+        "! pip3 install --quiet --force-reinstall google-api-core \\\n",
+        "                                         protobuf"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f82e28c631cc"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91842ef41bbd"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -241,31 +278,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\""
+        "LOCATION = \"us-central1\""
       ]
     },
     {
@@ -295,64 +308,6 @@
         "    USER_EMAIL = \"noreply@google.com\""
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -392,7 +347,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -439,7 +394,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -461,9 +416,7 @@
       },
       "outputs": [],
       "source": [
-        "IMPORT_FILE = (\n",
-        "    \"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\"\n",
-        ")"
+        "IMPORT_FILE = \"gs://cloud-samples-data/ai-platform/flowers/flowers.csv\""
       ]
     },
     {
@@ -657,7 +610,7 @@
         "endpoint = aiplatform.Endpoint.create(\n",
         "    display_name=\"flowers\",\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    labels={\"your_key\": \"your_value\"},\n",
         ")\n",
         "\n",
@@ -725,7 +678,7 @@
         "- `logging_sampling_strategy`: The rate for sampling prediction requests. \n",
         "- `objective_config`: What is being monitored.\n",
         "\n",
-        "*Note:* This feature is currently on available in private preview (v1alpha1). As such it is not accessible via the Vertex AI SDK, but is accessible via the REST interface."
+        "*Note:* This feature is currently on available in private preview (v1alpha1). As such it's not accessible via the Vertex AI SDK for Python, but is accessible via the REST interface."
       ]
     },
     {
@@ -790,11 +743,11 @@
         "\n",
         "import requests\n",
         "\n",
-        "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n",
+        "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(LOCATION)\n",
         "\n",
         "model_deployment_monitoring_job_resp = requests.post(\n",
         "    f\"https://{API_ENDPOINT}/v1alpha1/projects/\"\n",
-        "    f\"{PROJECT_ID}/locations/{REGION}/modelDeploymentMonitoringJobs\",\n",
+        "    f\"{PROJECT_ID}/locations/{LOCATION}/modelDeploymentMonitoringJobs\",\n",
         "    headers=headers,\n",
         "    data=json.dumps(MODEL_DEPLOYMENT_MONITORING_JOB_PAYLOAD),\n",
         ")\n",
@@ -905,18 +858,47 @@
       },
       "outputs": [],
       "source": [
-        "def predict_image_classification(image_path):\n",
+        "from google.cloud.aiplatform.gapic.schema import predict\n",
+        "\n",
+        "\n",
+        "def predict_image_classification(image_path, endpoint):\n",
+        "\n",
+        "    api_endpoint = \"{}-aiplatform.googleapis.com\".format(LOCATION)\n",
+        "    client_options = {\"api_endpoint\": api_endpoint}\n",
+        "    # Initialize client that will be used to create and send requests.\n",
+        "    # This client only needs to be created once, and can be reused for multiple requests.\n",
+        "    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)\n",
+        "\n",
         "    with tf.io.gfile.GFile(image_path, \"rb\") as f:\n",
         "        file_content = f.read()\n",
+        "\n",
         "    decoded_uint8 = tf.io.decode_image(file_content, channels=3)\n",
         "    # Max size of decoded image < 1.5MB\n",
         "    decoded_uint8 = tf.image.resize(decoded_uint8, (289, 289))\n",
+        "\n",
         "    encoded_content = base64.b64encode(\n",
         "        tf.image.encode_jpeg(tf.cast(decoded_uint8, tf.uint8)).numpy()\n",
         "    ).decode(\"utf-8\")\n",
-        "    INSTANCE = {\"content\": {\"b64\": encoded_content}}\n",
         "\n",
-        "    response = endpoint.predict(instances=[INSTANCE])\n",
+        "    instance = predict.instance.ImageObjectDetectionPredictionInstance(\n",
+        "        content=encoded_content,\n",
+        "    ).to_value()\n",
+        "\n",
+        "    instances = [instance]\n",
+        "\n",
+        "    parameters = predict.params.ImageObjectDetectionPredictionParams(\n",
+        "        confidence_threshold=0.5,\n",
+        "        max_predictions=5,\n",
+        "    ).to_value()\n",
+        "\n",
+        "    endpoint_id = endpoint.name.split(\"/\")[-1]\n",
+        "\n",
+        "    endpoint = client.endpoint_path(\n",
+        "        project=PROJECT_ID, location=LOCATION, endpoint=endpoint_id\n",
+        "    )\n",
+        "    response = client.predict(\n",
+        "        endpoint=endpoint, instances=instances, parameters=parameters\n",
+        "    )\n",
         "    print(response)\n",
         "\n",
         "\n",
@@ -928,7 +910,7 @@
         "for line in lines:\n",
         "    image_path = json.loads(line)[\"content\"]\n",
         "    try:\n",
-        "        response = predict_image_classification(image_path)\n",
+        "        response = predict_image_classification(image_path, endpoint)\n",
         "    except Exception as e:\n",
         "        print(e)\n",
         "    print(\"finish prediction. image path: \" + image_path)"
@@ -1053,7 +1035,7 @@
         "        f\"\"\" LIMIT {limits}\"\"\"\n",
         "    )\n",
         "    print(query_string)\n",
-        "    return pd.read_gbq(query_string, project_id=PROJECT_ID, location=REGION)"
+        "    return pd.read_gbq(query_string, project_id=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -1356,21 +1338,28 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = True\n",
+        "# Undeploy the model from the endpoint\n",
+        "endpoint.undeploy_all()\n",
         "\n",
-        "try:\n",
-        "    dataset.delete()\n",
-        "    model.delete()\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()\n",
-        "except:\n",
-        "    pass\n",
+        "# Delete the endpoint\n",
+        "endpoint.delete()\n",
+        "\n",
+        "# Delete model resource\n",
+        "model.delete()\n",
+        "\n",
+        "# Delete the dataset resource\n",
+        "dataset.delete()\n",
+        "\n",
+        "# Delete the training job\n",
+        "dag.delete()\n",
         "\n",
         "# delete BQ table\n",
-        "! bq rm -r -f {PROJECT_ID}.vertex_ai_model_monitoring\n",
+        "! bq rm -f {PROJECT_ID}.vertex_ai_model_monitoring\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -rf {BUCKET_URI}"
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb

@@ -9,7 +9,6 @@
       },
       "outputs": [],
       "source": [
-        "# @title Copyright & License (click to expand)\n",
         "# Copyright 2022 Google LLC\n",
         "#\n",
         "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
@@ -34,21 +33,24 @@
         "# Vertex AI Model Monitoring for setup for tabular models\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_monitoring%2Fget_started_with_model_monitoring_setup.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -75,26 +77,26 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this notebook, you learn to setup the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests.\n",
+        "In this notebook, you learn to setup the **Vertex AI Model Monitoring** service to detect feature skew and drift in the input predict requests.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
-        "- `Vertex AI Model Monitoring`\n",
-        "- `Vertex AI Prediction`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- Vertex AI Model Monitoring\n",
+        "- Vertex AI Online prediction\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
         "- Download a pre-trained custom tabular model.\n",
-        "- Upload the pre-trained model as a `Model` resource.\n",
-        "- Deploy the `Model` resource to the `Endpoint` resource.\n",
-        "- Configure the `Endpoint` resource for model monitoring.\n",
+        "- Upload the pre-trained model as a model resource.\n",
+        "- Deploy the model resource to the endpoint resource.\n",
+        "- Configure the endpoint resource for model monitoring.\n",
         "    - Skew and drift detection for feature inputs.\n",
         "    - Skew and drift detection for feature attributions.\n",
-        "- Automatic generation of the `input schema` by sending 1000 prediction request.\n",
+        "- Automatic generation of the *input schema* by sending 1000 prediction request.\n",
         "- List, pause, resume and delete monitoring jobs.\n",
-        "- Restart monitoring job with predefined `input schema`.\n",
+        "- Restart monitoring job with predefined *input schema*.\n",
         "- View logged monitored data."
       ]
     },
@@ -108,12 +110,12 @@
         "\n",
         "This tutorial uses a pre-trained model, where the model artifacts are stored in a public Cloud Storage bucket. \n",
         "\n",
-        "The model is based on [the blog post](https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml). The idea behind this model is that your company has extensive log data describing how your game users have interacted with the site. The raw data contains the following categories of information:\n",
+        "The model is based on [a blog post](https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml). The idea behind this model is that your company has extensive log data describing how your game users have interacted with the site. The raw data contains the following categories of information:\n",
         "\n",
-        "- identity - unique player identitity numbers\n",
-        "- demographic features - information about the player, such as the geographic region in which a player is located\n",
-        "- behavioral features - counts of the number of times a  player has triggered certain game events, such as reaching a new level\n",
-        "- churn propensity - this is the label or target feature, it provides an estimated probability that this player may churn, i.e. stop being an active player."
+        "- identity - unique player identity numbers.\n",
+        "- demographic features - information about the player, such as the geographic location in which a player is located.\n",
+        "- behavioral features - counts of the number of times a  player has triggered certain game events, such as reaching a new level.\n",
+        "- churn propensity - this is the label or target feature, it provides an estimated probability that this player may churn, that is, it stops being an active player."
       ]
     },
     {
@@ -140,103 +142,125 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "f3848df1e5b0"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "b24b232ee039"
+        "id": "tFy3H3aPgx12"
       },
       "outputs": [],
       "source": [
         "# Install required packages.\n",
         "! pip3 install --quiet --upgrade google-cloud-aiplatform \\\n",
         "                                 google-cloud-bigquery \\\n",
-        "                                 tensorflow==2.7 \\\n",
-        "                                 protobuf==3.20.3"
+        "                                 tensorflow==2.17.0 \\\n",
+        "                                 protobuf==4.25.3"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To run this tutorial, you must have an existing Google Cloud project.Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\""
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -245,7 +269,7 @@
         "id": "42c8a7c56abd"
       },
       "source": [
-        "#### User Email\n",
+        "### User Email\n",
         "\n",
         "Set your user email address to receive monitoring alerts."
       ]
@@ -266,89 +290,6 @@
         "    USER_EMAIL = \"noreply@google.com\""
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8RJ3_20etd31"
-      },
-      "source": [
-        "### Notes about service account and permission\n",
-        "\n",
-        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts above have the required roles:\n",
-        "\n",
-        "|Service account email|Description|Roles|\n",
-        "|---|---|---|\n",
-        "|PROJECT_NUMBER-compute@developer.gserviceaccount.com|Compute Engine default service account|Dataflow Admin, Dataflow Worker, Storage Admin, BigQuery Admin, Vertex AI User|\n",
-        "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
-        "\n",
-        "\n",
-        "1. Goto https://console.cloud.google.com/iam-admin/iam.\n",
-        "2. Check the \"Include Google-provided role grants\" checkbox.\n",
-        "3. Grant the corresponding roles.\n",
-        "\n",
-        "### Using data source from a different project\n",
-        "- For the BQ data source, grant both service accounts the \"BigQuery Data Viewer\" role.\n",
-        "- For the CSV data source, grant both service accounts the \"Storage Object Viewer\" role."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -377,7 +318,7 @@
         "id": "autoset_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -388,7 +329,58 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must enable the [Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). \n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "outputs": [],
+      "source": [
+        "import google.cloud.aiplatform as aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8RJ3_20etd31"
+      },
+      "source": [
+        "### Notes about service account and permission\n",
+        "\n",
+        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts above have the required roles:\n",
+        "\n",
+        "|Service account email|Description|Roles|\n",
+        "|---|---|---|\n",
+        "|PROJECT_NUMBER-compute@developer.gserviceaccount.com|Compute Engine default service account|Dataflow Admin, Dataflow Worker, Storage Admin, BigQuery Admin, Vertex AI User|\n",
+        "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
+        "\n",
+        "\n",
+        "1. Goto [IAM console](https://console.cloud.google.com/iam-admin/iam).\n",
+        "2. Check the **Include Google-provided role grants** checkbox.\n",
+        "3. Grant the corresponding roles.\n",
+        "\n",
+        "### Using data source from a different project\n",
+        "- For the BQ data source, grant both service accounts the **BigQuery Data Viewer** role.\n",
+        "- For the CSV data source, grant both service accounts the **Storage Object Viewer** role."
       ]
     },
     {
@@ -408,35 +400,12 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aiplatform\n",
         "from google.cloud import bigquery\n",
         "from google.cloud.aiplatform import model_monitoring\n",
         "from google.cloud.aiplatform.explain.metadata.tf.v2 import \\\n",
         "    saved_model_metadata_builder"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -469,9 +438,9 @@
         "\n",
         "You can set hardware accelerators for prediction (e.g., GPUs) or choose not to use any (CPU). Hardware accelertors lower the latency response for a prediction request. When choosing a hardware accelerators, consider the additional cost trade-off over latency.\n",
         "\n",
-        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Tesla K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Tesla T4 GPUs allocated to each VM, you would specify:\n",
         "\n",
-        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "See the [locations where accelerators are available](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
         "\n",
@@ -488,7 +457,7 @@
       "source": [
         "GPU = False\n",
         "if GPU:\n",
-        "    DEPLOY_GPU, DEPLOY_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)\n",
+        "    DEPLOY_GPU, DEPLOY_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 1)\n",
         "else:\n",
         "    DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
       ]
@@ -520,7 +489,7 @@
         "    DEPLOY_VERSION = \"tf2-cpu.2-5\"\n",
         "\n",
         "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        "    LOCATION.split(\"-\")[0], DEPLOY_VERSION\n",
         ")\n",
         "\n",
         "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
@@ -543,7 +512,7 @@
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -575,16 +544,16 @@
       "source": [
         "## Introduction to Vertex AI Model Monitoring\n",
         "\n",
-        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular models. You can monitor for skew and drift detection of the features in the inbound prediction requests or skew and drift detection of the feature attributions (Explainable AI) in the outbound prediction response -- that is, the distribution of the attributions on how they contributed to the output (predictions).\n",
+        "Vertex AI Model Monitoring is supported for AutoML tabular models and custom tabular models. You can monitor for skew and drift detection of the features in the inbound prediction requests or skew and drift detection of the feature attributions (Explainable AI) in the outbound prediction response i.e., the distribution of the attributions on how they contributed to the output (predictions).\n",
         "\n",
         "The following are the basic steps to enable model monitoring:\n",
         "\n",
-        "1. Deploy a `Vertex AI` AutoML or custom tabular model to an `Vertex AI Endpoint`.\n",
+        "1. Deploy an AutoML or custom tabular model to a Vertex AI endpoint.\n",
         "2. Configure a model monitoring specification.\n",
-        "3. Upload the model monitoring specification to the `Vertex AI Endpoint`.\n",
-        "4. Upload or automatic generation of the `input schema` for parsing.\n",
+        "3. Upload the model monitoring specification to the Vertex AI endpoint.\n",
+        "4. Upload or automatic generation of the *input schema* for parsing.\n",
         "5. For feature skew detection, upload the training data for automatic generation of the feature distribution.\n",
-        "6. For feature attributions, upload corresponding `Vertex AI Explainability` specification.\n",
+        "6. For feature attributions, upload corresponding Vertex AI Explainability specification.\n",
         "\n",
         "Once configured, you can enable/disable monitoring, change alerts and update the model monitoring configuration. \n",
         "\n",
@@ -594,7 +563,7 @@
         "\n",
         "For skew detection, the monitoring service requires a baseline for the statistical distribution of values in the training data. For AutoML tabular models this is automatically derived. For custom tabular models, you upload the training data to the service, and have the service automatically derive the distribution.\n",
         "\n",
-        "For feature attribution skew and drift detection, requires enabling your deployed model for `Vertex AI Explainability`\n",
+        "For feature attribution skew and drift detection, requires enabling your deployed model for Vertex AI Explainability.\n",
         "\n",
         "Learn more about [Introduction to Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring/overview)."
       ]
@@ -605,13 +574,13 @@
         "id": "1mhT_d_Bi-Kf"
       },
       "source": [
-        "### Generate explainable metadata for `Vertex Explainable AI`\n",
+        "### Generate explainable metadata for Vertex Explainable AI\n",
         "\n",
-        "If you want to do skew and drift detection on feature attributions of the output predictions (response), you do the additional steps:\n",
+        "If you want to do skew and drift detection on feature attributions of the output predictions (response), you run the following additional steps:\n",
         "\n",
         "- Specify the explainability specification for the model.\n",
-        "- When subsequently uploading the model as a `Vertex AI Model` resource, include the explainability specification.\n",
-        "- When subsequently uploading the model monitoring configuration specification to the corresponding `Vertex AI Endpoint` resource, include the explainability objective configuration.\n",
+        "- When subsequently uploading the model as a Vertex AI model resource, include the explainability specification.\n",
+        "- When subsequently uploading the model monitoring configuration specification to the corresponding Vertex AI endpoint resource, include the explainability objective configuration.\n",
         "\n",
         "As the first step, you create the explainable AI specification for your model using the helper method `SavedModelMetadataBuilder()`.\n",
         "\n",
@@ -643,13 +612,13 @@
         "id": "9bf06cd476e9"
       },
       "source": [
-        "### Upload the model artifacts as a `Vertex AI Model` resource\n",
+        "### Upload the model artifacts as a Vertex AI model resource\n",
         "\n",
-        "Next, you upload the pre-trained custom tabular model artifacts as a `Vertex AI Model` resource using the `upload()` method, with the following parameters:\n",
+        "Next, you upload the pre-trained custom tabular model artifacts as a Vertex AI model resource using the `upload()` method, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the `Model` resource.\n",
+        "- `display_name`: The human readable name for the model resource.\n",
         "- `artifact_uri`: The Cloud Storage location of the model artifacts.\n",
-        "- `serving_container_image`: The serving container image to use when the model is deployed to a `Vertex AI Endpoint` resource.\n",
+        "- `serving_container_image`: The serving container image to use when the model is deployed to a Vertex AI endpoint resource.\n",
         "- `explanation_parameters`: The parameters to configure explaining for the model's predictions.\n",
         "- `explanation_metadata`: The metadata describing the model's input and output for explanation.\n",
         "- `sync`: Whether to wait for the process to complete, or return immediately (async).\n",
@@ -683,9 +652,9 @@
         "id": "1069c6f0eba8"
       },
       "source": [
-        "### Deploy the `Vertex AI Model` resource to a `Vertex AI Endpoint` resource\n",
+        "### Deploy the Vertex AI model resource to the Vertex AI endpoint resource\n",
         "\n",
-        "Next, you deploy your `Vertex AI Model` resource to a `Vertex AI Endpoint` resource using the `deploy()` method, with the following parameters:\n",
+        "Next, you deploy your Vertex AI model resource to a Vertex AI endpoint resource using the `deploy()` method, with the following parameters:\n",
         "\n",
         "- `deploy_model_display`: The human reable name for the deployed model.\n",
         "- `machine_type`: The machine type for each VM node instance.\n",
@@ -787,7 +756,7 @@
         "\n",
         "- `monitor_interval`:  Sets the model monitoring job scheduling interval in hours. Minimum time interval is 1 hour.\n",
         "\n",
-        "*Note:* The REST API specifies the unit in seconds."
+        "**Note:** The REST API specifies the unit in seconds."
       ]
     },
     {
@@ -846,7 +815,7 @@
         "- `drift_thresholds`: A dictionary of key/value pairs where the keys are the input features for monitor for feature input drift. The value is the detection threshold. When not specified, the default drift threshold for a feature is 0.3 (30%).\n",
         "- `attribute_drift_threshold`: A dictionary of key/value pairs where the keys are the input features for monitor for feature attribution drift. The value is the detection threshold. When not specified, the default drift threshold for a feature is 0.3 (30%).\n",
         "\n",
-        "*Note:* Enabling drift detection for either feature inputs or feature attributions is optional."
+        "**Note:** Enabling drift detection for either feature inputs or feature attributions is optional."
       ]
     },
     {
@@ -887,14 +856,14 @@
         "Next, you configure the `skew_config` specification with the following settings:\n",
         "\n",
         "- `data_source`: The source of the dataset of the original training data. The format of the source defaults to a BigQuery table. Otherwise the setting `data_format` must be set to one of the values below. The location of the data must be a Cloud Storage location.\n",
-        "  - `csv`: \n",
-        "  - `jsonl`:\n",
-        "  - `tf-record`:\n",
+        "  - `csv`\n",
+        "  - `jsonl`\n",
+        "  - `tf-record`\n",
         "- `skew_thresholds`: A dictionary of key/value pairs where the keys are the input features for monitor for feature input skew. The value is the detection threshold. When not specified, the default skew threshold for a feature is 0.3 (30%).\n",
         "- `attribute_skew_thresholds`: A dictionary of key/value pairs where the keys are the input features for monitor for feature attribution skew. The value is the detection threshold. When not specified, the default skew threshold for a feature is 0.3 (30%).\n",
         "- `target_field`: The target label for the training dataset\n",
         "\n",
-        "*Note:* Enabling skew detection is optional."
+        "**Note:** Enabling skew detection is optional."
       ]
     },
     {
@@ -968,14 +937,14 @@
         "id": "82c7e7af7163"
       },
       "source": [
-        "### Create the monitoring job\n",
+        "## Create the monitoring job\n",
         "\n",
         "You create a monitoring job, with your monitoring specifications, using the `aiplatform.ModelDeploymentMonitoringJob.create()` method, with the following parameters:\n",
         "\n",
         "- `display_name`: The human readable name for the monitoring job.\n",
         "- `project`: The project ID.\n",
-        "- `region`: The region.\n",
-        "- `endpoint`: The fully qualified resource name of the `Vertex AI Endpoint` to enable monitoring.\n",
+        "- `location`: The location.\n",
+        "- `endpoint`: The fully qualified resource name of the Vertex AI endpoints to enable monitoring.\n",
         "- `logging_sampling_strategy`: The specification for the sampling configuration.\n",
         "- `schedule_config`: The specification for the scheduling configuration.\n",
         "- `alert_config`: The specification for the alerting configuration.\n",
@@ -993,7 +962,7 @@
         "monitoring_job = aiplatform.ModelDeploymentMonitoringJob.create(\n",
         "    display_name=\"churn\",\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    endpoint=endpoint,\n",
         "    logging_sampling_strategy=logging_sampling_strategy,\n",
         "    schedule_config=schedule_config,\n",
@@ -1010,11 +979,11 @@
         "id": "3c6d3b620264"
       },
       "source": [
-        "#### Email notification of the monitoring job.\n",
+        "### Email notification of the monitoring job\n",
         "\n",
         "An email notification is sent to the email address in the alerting configuration, notifying that the model monitoring job is now enabled.\n",
         "\n",
-        "The contents will appear like:\n",
+        "The contents appear like:\n",
         "\n",
         "<blockquote>\n",
         "Hello Vertex AI Customer,\n",
@@ -1031,13 +1000,13 @@
         "id": "dcc4aae9e20f"
       },
       "source": [
-        "#### Monitoring Job State\n",
+        "### Monitoring Job State\n",
         "\n",
-        "After you start the `Vertex AI Model Monitoring` job, it will be in a `PENDING` state until the `input schema` and `skew distribution baselines` are calculated. The process happens sequentially. In this example where you use automatic generation of the `input schema`, the service stays in a `PENDING` state until the 1000 prediction request (discussed subsequently) is sent. \n",
+        "After you start the Vertex AI Model Monitoring job, it remains in a `PENDING` state until the *input schema* and *skew distribution baselines* are calculated. The process happens sequentially. In this example where you use automatic generation of the *input schema*, the service stays in a `PENDING` state until the 1000 prediction requests (discussed subsequently) are sent. \n",
         "\n",
-        "Once the `input schema` has been generated, then a batch job will be initiated to generate the distribution baseline from the training data. Again, the service stays in a `PENDING` state until the baseline distribution is calculated.\n",
+        "Once the *input schema* has been generated, then a batch job is initiated to generate the distribution baseline from the training data. Again, the service stays in a `PENDING` state until the baseline distribution is calculated.\n",
         "\n",
-        "Once the baseline distribution is generated, then the monitoring job will enter `OFFLINE` state. On the per interval basis -- e.g., once an hour, the monitoring job will enter `RUNNING` state while analyzing the sampled data. Once completed, it will return to an `OFFLINE` state while awaiting the next scheduled analysis."
+        "Once the baseline distribution is generated, then the monitoring job enters `OFFLINE` state. On an interval basis, for e.g., once an hour, the monitoring job enters `RUNNING` state while analyzing the sampled data. Once completed, it returns to an `OFFLINE` state while awaiting the next scheduled analysis."
       ]
     },
     {
@@ -1059,15 +1028,15 @@
         "id": "3960076190ab"
       },
       "source": [
-        "## Initialize the parsing for automatically generating the input schema\n",
+        "## Monitoring job with automatically generated input schema\n",
         "\n",
-        "After your `Endpoint` receives a 1000 prediction requests, the modeling service will automatically parse and create the `input schema`.\n",
+        "After your endpoint receives 1000 prediction requests, the modeling service automatically parses and creates the *input schema*.\n",
         "\n",
-        "### Create the 1000 instance data\n",
+        "### Create data with 1000 instances\n",
         "\n",
         "In this example, the first 1000 entries in the BigQuery training data are used as the first 1000 prediction requests. \n",
         "\n",
-        "*Note:* In this context, each instance is a prediction request. In otherwords, sending 1000 prediction requests of a single instance is the same as sending a single prediction request with 1000 instances."
+        "**Note:** In this context, each instance is a prediction request. In other words, sending 1000 prediction requests of a single instance is the same as sending a single prediction request with 1000 instances."
       ]
     },
     {
@@ -1103,9 +1072,9 @@
         "id": "6d002569dadc"
       },
       "source": [
-        "### Make the initial prediction request\n",
+        "### Send the prediction request\n",
         "\n",
-        "Next, you send the the 1000 prediction request to your `Vertex AI Endpoint` resource using the `predict()` method."
+        "Next, you send the 1000 instances to your Vertex AI endpoint resource using the `predict()` method."
       ]
     },
     {
@@ -1130,13 +1099,11 @@
         "id": "e990a8821178"
       },
       "source": [
-        "### Automatic generation of the input schema\n",
+        "### Automatic generation of input schema and baseline distribution\n",
         "\n",
-        "After the model monitoring service receives 1000 instances of prediction requests, the monitoring will start analyzing the prediction requests to automatically generate an `input schema` for the feature inputs.\n",
+        "After the model monitoring service receives 1000 instances as prediction requests, the monitoring job starts analyzing the prediction requests to automatically generate an *input schema* for the feature inputs.\n",
         "\n",
-        "### Automatic generation of the baseline distribution\n",
-        "\n",
-        "After the `input schema` is generated, the monitoring service creates a batch job to analyze the training data to determine the baseline distribution. "
+        "After the *input schema* is generated, the monitoring service creates a batch job to analyze the training data to determine the baseline distribution. "
       ]
     },
     {
@@ -1160,13 +1127,13 @@
         "id": "08f499f7adae"
       },
       "source": [
-        "#### The location of the BigQuery table for monitoring\n",
+        "### Location of the BigQuery table for monitoring\n",
         "\n",
         "The BigQuery table for logging the sampled requests is located at:\n",
         "\n",
         "    `<PROJECT_ID>.model_deployment_monitoring_<ENDPOINT_ID>`.serving_predict, \n",
         "    \n",
-        "Where <ENDPOINT_ID> is the numerical identifier for the `Vertex AI Endpoint` resource."
+        "where <ENDPOINT_ID> is the numerical identifier for the Vertex AI endpoint resource."
       ]
     },
     {
@@ -1239,7 +1206,7 @@
         "id": "a5c889257c9e"
       },
       "source": [
-        "### List monitoring jobs by a filter\n",
+        "#### List monitoring jobs by a filter\n",
         "\n",
         "Alternatively, you can use a `filter` parameter to list a subset of jobs. In this example, you filter the list by the monitoring job's display name."
       ]
@@ -1269,7 +1236,7 @@
         "\n",
         "You can delete the monitoring job using the `delete()` method. \n",
         "\n",
-        "*Note:* You cannot delete a monitoring job when in a state of RUNNING. You must pause the job first."
+        "**Note:** You can't delete a monitoring job when it's in the RUNNING state. To do so, you must pause the job first."
       ]
     },
     {
@@ -1280,7 +1247,10 @@
       },
       "outputs": [],
       "source": [
+        "# Pause the monitoring job first\n",
         "monitoring_job.pause()\n",
+        "\n",
+        "# Delete the monitoring job\n",
         "monitoring_job.delete()"
       ]
     },
@@ -1302,7 +1272,6 @@
       "outputs": [],
       "source": [
         "# Delete the monitoring logged data BigQuery dataset\n",
-        "\n",
         "! bq rm -r -f {PROJECT_ID}.model_deployment_monitoring_{ENDPOINT_ID}"
       ]
     },
@@ -1312,13 +1281,20 @@
         "id": "73fbeb1b68ba"
       },
       "source": [
-        "### Create a monitoring job with a predefined input schema\n",
+        "## Monitoring job with predefined input schema\n",
         "\n",
-        "Next, you create another monitoring job. This time you will load a predefined `input schema`. Once loaded, the monitoring service will use this `input schema` instead of automatically generating one from the first 1000 prediction instances.\n",
+        "Next, you create another monitoring job. This time you load a predefined *input schema*. Once loaded, the monitoring service uses this *input schema* instead of automatically generating one from the first 1000 prediction instances."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1a5077875e4b"
+      },
+      "source": [
+        "### Create the predefined input schema\n",
         "\n",
-        "#### Create the predefined input schema\n",
-        "\n",
-        "The predefined `input schema` is specified as a YAML file. In this example, you retrieve the BigQuery schema for the training data, which includes the feature names and data types, to generate the YAML specification. The predefined `input schema` must be loaded to a Cloud Storage location.\n",
+        "The predefined *input schema* is specified as a YAML file. In this example, you retrieve the BigQuery schema for the training data, which includes the feature names and data types, to generate the YAML specification. The predefined *input schema* must be loaded to a Cloud Storage location.\n",
         "\n",
         "Learn more about [Custom instance schemas for parsing input](https://cloud.google.com/vertex-ai/docs/model-monitoring/overview#custom-input-schemas)."
       ]
@@ -1378,7 +1354,7 @@
         "\n",
         "Finally, you create the monitoring job using the `create()` method, with the following additional parameter:\n",
         "\n",
-        "- `analysis_instance_schema_uri`: The location of the YAML file containing the `input schema`."
+        "- `analysis_instance_schema_uri`: The location of the YAML file containing the *input schema*."
       ]
     },
     {
@@ -1392,7 +1368,7 @@
         "monitoring_job = aiplatform.ModelDeploymentMonitoringJob.create(\n",
         "    display_name=\"churn\",\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    endpoint=endpoint,\n",
         "    logging_sampling_strategy=logging_sampling_strategy,\n",
         "    schedule_config=schedule_config,\n",
@@ -1423,55 +1399,13 @@
       },
       "outputs": [],
       "source": [
+        "# Pause the monitoring job first\n",
         "monitoring_job.pause()\n",
+        "\n",
+        "# Delete the monitoring job\n",
         "monitoring_job.delete()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6280efab664"
-      },
-      "source": [
-        "#### Undeploy and delete the `Vertex AI Endpoint` resource\n",
-        "\n",
-        "Your `Vertex AI Endpoint` resource can be deleted using the `delete()` method. Prior to deleting, any model deployed to your `Vertex AI Endpoint` resource, must first be undeployed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ad0d28b762e5"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint.undeploy_all()\n",
-        "endpoint.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "448f3698d50f"
-      },
-      "source": [
-        "#### Delete the `Vertex AI Model` resource\n",
-        "\n",
-        "Your `Vertex AI Model` resource can be deleted using the `delete()` method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0feab0a0b5d7"
-      },
-      "outputs": [],
-      "source": [
-        "model.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1494,13 +1428,24 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
+        "# Undeploy the model from your endpoint resource\n",
+        "endpoint.undeploy_all()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete the endopint resource\n",
+        "endpoint.delete()\n",
+        "\n",
+        "# Delete the model resource\n",
+        "model.delete()\n",
+        "\n",
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -rf {BUCKET_URI}\n",
         "\n",
+        "# Delete the schema file\n",
         "! rm -f schema.yaml\n",
         "\n",
+        "# Delete the BigQuery dataset\n",
         "! bq rm -r -f {PROJECT_ID}.model_deployment_monitoring_{ENDPOINT_ID}"
       ]
     }

notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb

@@ -32,38 +32,27 @@
         "# Challenger vs Blessed methodology for model deployment into production\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fchallenger_vs_blessed_deployment_method.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/pipelines/official/challenger_vs_blessed_deployment_method.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                  \n",
-        "</table>\n",
-        "\n",
-        "*Note: This notebook uses KFP 1.x and GCPC 1.x. We recommend using 2.x versions*"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "24743cf4a1e1"
-      },
-      "source": [
-        "**_NOTE_**: This notebook has been tested in the following environment:\n",
-        "\n",
-        "* Python version = 3.9"
+        "</table>"
       ]
     },
     {
@@ -74,9 +63,16 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial shows how to use Vertex AI Pipeline for deploying the next version of a model into production using the challenger vs blessed method.\n",
+        "This tutorial shows how to use Vertex AI Pipelines for deploying the next version of a model into production using the challenger vs blessed method.\n",
         "\n",
-        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Model evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction)."
+        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Model evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).\n",
+        "\n",
+        "\n",
+        "**_NOTE_**: This notebook uses KFP 1.x and GCPC 1.x. It's recommended to use 2.x versions.\n",
+        "\n",
+        "**_NOTE_**: This notebook has been tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
       ]
     },
     {
@@ -87,9 +83,9 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to construct a Vertex AI pipeline, which trains a new challenger version of a model, evaluates the model and compares the evaluation to the existing blessed model in production, to determine whether the challenger model becomes the blessed model for replacement in production.\n",
+        "In this tutorial, you learn how to construct a Vertex AI pipeline, which trains a new challenger version of a model, evaluates the model and compares the evaluation to the existing blessed model in production. Then, it determines whether the challenger model becomes the blessed model for replacement in production.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Vertex AI services and resources:\n",
         "\n",
         "- Vertex AI Pipeline\n",
         "- Vertex AI Model Evaluation\n",
@@ -99,17 +95,17 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Import a pretrained (blessed) model to the `Vertex AI Model Registry`.\n",
+        "- Import a pretrained (blessed) model to the Vertex AI Model Registry.\n",
         "- Import synthetic model evaluation metrics to the corresponding (blessed) model.\n",
-        "- Create a `Vertex AI Endpoint` resource\n",
-        "- Deploy the blessed model to the `Endpoint` resource.\n",
-        "- Create a Vertex AI Pipeline\n",
+        "- Create a Vertex AI endpoint resource\n",
+        "- Deploy the blessed model to the endpoint resource.\n",
+        "- Create a Vertex AI Pipeline that runs the following steps:\n",
         "    - Get the blessed model.\n",
         "    - Import another instance (challenger) of the pretrained model.\n",
         "    - Register the pretrained (challenger) model as a new version of the existing blessed model.\n",
         "    - Create a synthetic model evaluation.\n",
         "    - Import the synthetic model evaluation metrics to the corresponding challenger model.\n",
-        "    - Compare the evaluations and set the blessed or challenger as the default.\n",
+        "    - Compare the evaluations and set the blessed or challenger model as the default.\n",
         "    - Deploy the new blessed model.\n",
         "\n",
         "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
@@ -123,7 +119,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
+        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
       ]
     },
     {
@@ -145,15 +141,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -165,13 +168,12 @@
       "outputs": [],
       "source": [
         "# Install the packages\n",
-        "USER=''\n",
-        "! pip3 install {USER} --upgrade google-cloud-aiplatform \\\n",
+        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
         "                                'google-cloud-pipeline-components<2'\n",
-        "! pip3 install {USER}           tensorflow==2.5 \\\n",
-        "                                tensorflow_hub\n",
+        "! pip3 install tensorflow==2.5 \\\n",
+        "                tensorflow_hub\n",
         "  \n",
-        "! pip3 install {USER} --upgrade 'kfp<2'"
+        "! pip3 install --upgrade 'kfp<2'"
       ]
     },
     {
@@ -180,7 +182,9 @@
         "id": "58707a750154"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -191,32 +195,53 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set up your Google Cloud project\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "    from google.colab import auth\n",
         "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -225,12 +250,9 @@
         "id": "WReHDGG5g0XY"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -242,105 +264,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74ccc9e52986"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "IS_COLAB = False\n",
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()\n",
-        "# IS_COLAB = True"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -351,9 +275,7 @@
       "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets.\n",
-        "\n",
-        "- *{Note to notebook author: For any user-provided strings that need to be unique (like bucket names or model ID's), append \"-unique\" to the end so proper testing can occur}*"
+        "Create a storage bucket to store intermediate artifacts such as datasets."
       ]
     },
     {
@@ -373,7 +295,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -384,7 +306,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -397,7 +319,7 @@
         "\n",
         "You use a service account to create Vertex AI Pipeline jobs.\n",
         "\n",
-        "If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -419,6 +341,10 @@
       },
       "outputs": [],
       "source": [
+        "import sys\n",
+        "\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "\n",
         "if (\n",
         "    SERVICE_ACCOUNT == \"\"\n",
         "    or SERVICE_ACCOUNT is None\n",
@@ -445,7 +371,7 @@
       "source": [
         "#### Set service account access for Vertex AI Pipelines\n",
         "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run these once per service account."
       ]
     },
     {
@@ -495,7 +421,9 @@
       "source": [
         "### Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
+        "To get started using Vertex AI, you must [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your Google Cloud project.\n",
+        "\n",
+        "Then, initialize the Vertex AI SDK for Python for your project."
       ]
     },
     {
@@ -506,7 +434,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -519,9 +447,9 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you would specify:\n",
         "\n",
-        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
@@ -546,11 +474,11 @@
         "id": "container:training,prediction"
       },
       "source": [
-        "#### Set pre-built containers\n",
+        "#### Set prebuilt containers\n",
         "\n",
-        "Set the pre-built Docker container image for training and prediction.\n",
+        "Set the prebuilt Docker container image for prediction.\n",
         "\n",
-        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+        "For the latest list, see [Prebuilt containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
       ]
     },
     {
@@ -569,7 +497,7 @@
         "    DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
         "\n",
         "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        "    LOCATION.split(\"-\")[0], DEPLOY_VERSION\n",
         ")\n",
         "\n",
         "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
@@ -583,32 +511,21 @@
       "source": [
         "#### Set machine type\n",
         "\n",
-        "Next, set the machine type to use for prediction.\n",
+        "Next, set the machine type to use for deployment.\n",
         "\n",
-        "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
-        " - `machine type`\n",
-        "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
+        "- Set the variable `DEPLOY_COMPUTE` to configure the compute resources for the VMs you use for deployment.\n",
+        " - `machine_type`\n",
+        "     - `n1-standard`: 3.75GB of memory per vCPU\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following aren't supported.\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "63de49055083"
-      },
-      "source": [
-        "### Save the model artifacts\n",
-        "\n",
-        "At this point, the model is in memory. Next, you save the model artifacts to a Cloud Storage location."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -619,7 +536,9 @@
       },
       "outputs": [],
       "source": [
-        "DEPLOY_COMPUTE = \"n1-standard-4\"\n",
+        "machine_type = \"n1-standard\"\n",
+        "vCPUs = \"4\"\n",
+        "DEPLOY_COMPUTE = f\"{machine_type}-{vCPUs}\"\n",
         "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
       ]
     },
@@ -631,7 +550,7 @@
       "source": [
         "## Get pretrained model from TensorFlow Hub\n",
         "\n",
-        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to a `Vertex AI Model` resource. Once you have a `Vertex AI Model` resource, the model can be deployed to a `Vertex AI Endpoint` resource.\n",
+        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to a Vertex AI model resource. Once you have a Vertex AI model resource, the model can be deployed to a Vertex AI endpoint resource.\n",
         "\n",
         "### Download the pretrained model\n",
         "\n",
@@ -655,6 +574,17 @@
         "tfhub_model.summary()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "63de49055083"
+      },
+      "source": [
+        "### Save the model artifacts\n",
+        "\n",
+        "At this point, the model is in memory. Next, you save the model artifacts to a Cloud Storage location."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -673,17 +603,17 @@
         "id": "e8ce91147c93"
       },
       "source": [
-        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
+        "## Upload the TensorFlow Hub model to Vertex AI Model Registry\n",
         "\n",
-        "Finally, you upload the model artifacts from the TFHub model into a `Vertex AI Model` resource using the method `upload()`, with the following parameters:\n",
+        "Finally, you upload the model artifacts from the TFHub model into Vertex AI Model Registry and get a model resource object using the `upload()` method, with the following parameters:\n",
         "\n",
-        "- `display_name`: A human readable name for the `Model` resource.\n",
+        "- `display_name`: A human readable name for the model resource.\n",
         "- `artifact_uri`: The Cloud Storage location of the model package.\n",
         "- `serving_container_image_uri`: The serving container image.\n",
         "\n",
-        "Uploading a model into a Vertex AI Model resource returns a long running operation, since it may take a few moments. \n",
+        "Uploading a model into Vertex AI Model Registry returns a long running operation, since it may take a few moments. \n",
         "\n",
-        "*Note:* When you upload the model artifacts to a `Vertex AI Model` resource, you specify the corresponding deployment container image."
+        "**Note**: When you upload the model artifacts to Vertex AI Model Registry, you specify the corresponding deployment container image."
       ]
     },
     {
@@ -711,7 +641,7 @@
         "id": "c11e98ef5391"
       },
       "source": [
-        "### Create a model evaluation\n",
+        "## Create a model evaluation\n",
         "\n",
         "First, you create a model evaluation in a format that corresponds to one of the predefined schemas for model evaluations. In this example, you use the schema for a classification metric, and specify the following subset of evaluation metrics as a dictionary:\n",
         "\n",
@@ -724,7 +654,7 @@
         "- `metrics_schema_uri`: The schema for the specific type of evaluation metrics.\n",
         "- `metrics`: The dictionary with the evaluation metrics.\n",
         "\n",
-        "Learn more about [Schemas for evaluation metrics](https://cloud.google.com/vertex-ai/docs/evaluation/introduction#features)"
+        "Learn more about [Schemas for evaluation metrics](https://cloud.google.com/vertex-ai/docs/evaluation/introduction#features)."
       ]
     },
     {
@@ -755,7 +685,7 @@
         "\n",
         "Next, upload the model's evaluation from the custom training job to the corresponding entry in the Vertex AI Model Registry.\n",
         "\n",
-        "Currently, there is not yet support for this method in the SDK. Instead, you use the lower level GAPIC API interface."
+        "Currently, the Vertex AI Python SDK has no support for this method. Instead, you use the lower level GAPIC API interface."
       ]
     },
     {
@@ -766,7 +696,7 @@
       },
       "outputs": [],
       "source": [
-        "API_ENDPOINT = f\"{REGION}-aiplatform.googleapis.com\"\n",
+        "API_ENDPOINT = f\"{LOCATION}-aiplatform.googleapis.com\"\n",
         "client = gapic.ModelServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
         "\n",
         "client.import_model_evaluation(\n",
@@ -780,13 +710,13 @@
         "id": "628de0914ba1"
       },
       "source": [
-        "## Creating an `Endpoint` resource\n",
+        "## Creating an endpoint resource\n",
         "\n",
-        "You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
+        "You create an endpoint resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI Python SDK with the `init()` method.\n",
         "\n",
         "In this example, the following parameters are specified:\n",
         "\n",
-        "- `display_name`: A human readable name for the `Endpoint` resource.\n",
+        "- `display_name`: A human readable name for the endpoint resource.\n",
         "- `project`: Your project ID.\n",
         "- `location`: Your region.\n",
         "\n",
@@ -804,7 +734,7 @@
       "outputs": [],
       "source": [
         "endpoint = aiplatform.Endpoint.create(\n",
-        "    display_name=\"resnet\", project=PROJECT_ID, location=REGION\n",
+        "    display_name=\"resnet\", project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "\n",
         "print(endpoint)"
@@ -816,9 +746,9 @@
         "id": "ca3fa3f6a894"
       },
       "source": [
-        "### Deploy the `Model` resource to the `Endpoint` resource\n",
+        "## Deploy the model resource to the endpoint resource\n",
         "\n",
-        "Next, you deploy the blessed `Vertex AI Model` resource to a `Vertex AI Endpoint` resource. The `Vertex AI Model` resource already has defined for it the deployment container image. To deploy, you specify the following additional configuration settings:\n",
+        "Next, you deploy the blessed Vertex AI model resource to a Vertex AI endpoint resource. The container image defined for the Vertex AI model resource is used for deployment. To deploy, you specify the following additional configuration settings:\n",
         "\n",
         "- The machine type.\n",
         "- The (if any) type and number of GPUs.\n",
@@ -826,11 +756,11 @@
         "\n",
         "In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
         "\n",
-        "- `model`: The `Model` resource.\n",
+        "- `model`: The model resource.\n",
         "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
         "- `machine_type`: The machine type for each VM instance.\n",
         "\n",
-        "Due to the requirements to provision the resource, this may take upto a few minutes."
+        "This may take upto a few minutes due to provisioning of the configured resources."
       ]
     },
     {
@@ -858,17 +788,17 @@
       "source": [
         "## Create custom components for pipeline\n",
         "\n",
-        "Next, you create several custom components you use in your pipeline.\n",
+        "Next, you create several custom components that you use in your pipeline.\n",
         "\n",
         "### Create component to upload the next version of the model\n",
         "\n",
-        "First, you define a component to upload the trained challenger model as a version to the blessed model in the `Vertex AI Model Registry`. The component takes the following arguments:\n",
+        "First, you define a component to upload the trained challenger model as a version to the blessed model in the Vertex AI Model Registry. The component takes the following arguments:\n",
         "\n",
         "- `parent_model`: The full resource name of the blessed model.\n",
         "- `artifact_uri`: The Cloud Storage location of the model artifacts for the challenger model.\n",
         "- `serving_container`: The serving container for the challenger model.\n",
         "- `project`: Your Project ID.\n",
-        "- `region`: Your region."
+        "- `region`: The region where you want to create or use the resources."
       ]
     },
     {
@@ -914,10 +844,10 @@
         "\n",
         "Next, you define a component to import the evaluation metrics for the challenger model to the Model Registry. The component takes the following arguments:\n",
         "\n",
-        "- `display_name`: Human readable name for the evaluation metrics\n",
+        "- `display_name`: Human readable name for the evaluation metrics.\n",
         "- `metrics`: The evaluation metrics formatted for classification.\n",
         "- `parent_model_resource`: The full resource name for the challenger model version.\n",
-        "- `region`: The region."
+        "- `region`: The region where you want to create or use the resources."
       ]
     },
     {
@@ -955,8 +885,7 @@
       "source": [
         "###  Create component to compare metrics\n",
         "\n",
-        "Next, you define a component to compare the `auPrc` metric between two versions of a model. In this case, the current blessed\n",
-        "and new challenger. Whomever of the two has the best `auPrc` value is set as the default model. When you subsequently deploy, the default model is deployed. The component takes the following arguments:\n",
+        "Next, you define a component to compare the `auPrc` metric between two versions of a model. In this case, you compare between the current blessed model and the new challenger model. Whichever of the two has the best `auPrc` value is set as the default model. When you subsequently deploy, the default model is deployed. The component takes the following arguments:\n",
         "\n",
         "- `blessed_model_resource_name`: The full resource name of the blessed model.\n",
         "- `challenger_model_resource_name`: The full resource name of the challenger model."
@@ -1003,9 +932,14 @@
         "id": "8a0cd316dc5e"
       },
       "source": [
-        "## Construct blessed vs challenger pipeline\n",
+        "## Blessed vs challenger pipeline\n",
         "\n",
-        "Next, you construct a pipeline for the following tasks:\n",
+        "In this section, you construct a pipeline to fetch the blessed, and challenger models and compare them to deploy the best one to the production endpoint.\n",
+        "\n",
+        "\n",
+        "### Define the pipeline\n",
+        "\n",
+        "Your pipeline runs the following tasks:\n",
         "\n",
         "- Get the blessed version of a model.\n",
         "- Get the endpoint for the deployed blessed model.\n",
@@ -1033,7 +967,7 @@
         "    endpoint_resource_name: str,\n",
         "    endpoint_resource_uri: str,\n",
         "    project: str = PROJECT_ID,\n",
-        "    region: str = REGION,\n",
+        "    region: str = LOCATION,\n",
         "):\n",
         "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
         "        GetVertexModelOp\n",
@@ -1097,7 +1031,7 @@
       "source": [
         "### Compile the pipeline\n",
         "\n",
-        "Next, you compile the pipeline. "
+        "Next, you compile the pipeline to a JSON file. "
       ]
     },
     {
@@ -1108,6 +1042,7 @@
       },
       "outputs": [],
       "source": [
+        "# Compile the pipeline to a json file\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline, package_path=\"challenger_vs_blessed.json\"\n",
         ")"
@@ -1129,7 +1064,7 @@
         "- `endpoint_resource_name`: The full resource name of the production endpoint.\n",
         "- `endpoint_resource_uri`: The full URI for the production endpoint.\n",
         "- `project`:The project ID.\n",
-        "- `region`: The region"
+        "- `region`: The region where you want to create or use the resources."
       ]
     },
     {
@@ -1140,8 +1075,10 @@
       },
       "outputs": [],
       "source": [
+        "# Define the root folder for your pipeline artifacts\n",
         "PIPELINE_ROOT = \"{}/pipeline_root/control\".format(BUCKET_URI)\n",
         "\n",
+        "# Define the pipeline job\n",
         "job = aiplatform.PipelineJob(\n",
         "    display_name=\"challenger_vs_blessed\",\n",
         "    template_path=\"challenger_vs_blessed.json\",\n",
@@ -1154,14 +1091,13 @@
         "        \"endpoint_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
         "        + endpoint.resource_name,\n",
         "        \"project\": PROJECT_ID,\n",
-        "        \"region\": REGION,\n",
+        "        \"region\": LOCATION,\n",
         "    },\n",
         "    enable_caching=False,\n",
         ")\n",
         "\n",
-        "job.run()\n",
-        "\n",
-        "! rm challenger_vs_blessed.json"
+        "# Run the pipeline job\n",
+        "job.run()"
       ]
     },
     {
@@ -1170,11 +1106,11 @@
         "id": "e1d089d1d0d9"
       },
       "source": [
-        "### Get the latest state of the production endpoint\n",
+        "## Get the latest state of the production endpoint\n",
         "\n",
-        "Now that the pipeline has finished, the challenger (version 2) model has replaced the previous blessed model on the production endpoint.\n",
+        "Once your pipeline execution is finished, notice that the challenger (version 2) model has replaced the previous blessed model on the production endpoint.\n",
         "\n",
-        "Next your display the latest information on the deployed models for the production endpoint, and then display the traffic split. The resource ID for the 100% entry is the resource ID for the challenger (v2) model."
+        "Now, display the latest details of the deployed models from the production endpoint, and then display the traffic split. Notice the resource ID for the 100% entry is the resource ID for the challenger (v2) model."
       ]
     },
     {
@@ -1213,31 +1149,25 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "# Undeploy the model from endpoint\n",
+        "endpoint.undeploy_all()\n",
         "\n",
-        "# Delete endpoint resource\n",
-        "try:\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the endpoint resource\n",
+        "endpoint.delete()\n",
         "\n",
-        "# Delete model resource\n",
-        "try:\n",
-        "    blessed_model.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the model resource(s)\n",
+        "blessed_model.delete()\n",
         "\n",
-        "# Delete the pipeline resource\n",
-        "try:\n",
-        "    job.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the pipeline job\n",
+        "job.delete()\n",
         "\n",
         "# Delete Cloud Storage objects that were created\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "# Delete the locally saved pipeline package file\n",
+        "! rm challenger_vs_blessed.json"
       ]
     }
   ],

notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb

@@ -32,26 +32,27 @@
         "# Vertex AI Pipelines: Custom training with pre-built Google Cloud Pipeline Components\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fcustom_model_training_and_batch_prediction.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -63,7 +64,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI Pipelines with pre-built Google Cloud Pipeline Components for custom training.\n",
+        "This tutorial demonstrates how to use Vertex AI Pipelines with pre-built components from Google Cloud Pipeline Components for custom training.\n",
         "\n",
         "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Custom training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline)."
       ]
@@ -76,13 +77,13 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline components to build a custom model.\n",
+        "In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build a custom model.\n",
         "\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
         "- Vertex AI Pipelines\n",
-        "- Google Cloud Pipeline components\n",
+        "- Google Cloud Pipeline Components\n",
         "- Vertex AI Training\n",
         "- Vertex AI model resource\n",
         "- Vertex AI endpoint resource\n",
@@ -96,7 +97,7 @@
         "    - Deploy the model resource to the endpoint resource.\n",
         "    - Make a batch prediction request.\n",
         "\n",
-        "Learn more about [Google Cloud Pipeline components](https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline)."
+        "Learn more about [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline)."
       ]
     },
     {
@@ -130,15 +131,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4b331e2fd155"
+      },
+      "source": [
+        "## Get Started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -161,25 +169,69 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4a2b7b59bbf7"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f82e28c631cc"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -188,127 +240,21 @@
         "id": "BF1j6f9HApxa"
       },
       "source": [
-        "## Before you begin\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com )\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WReHDGG5g0XY"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "ab779e2d71d3"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -350,7 +296,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -412,7 +358,7 @@
       "source": [
         "#### Set service account access for Vertex AI Pipelines\n",
         "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run these once per service account."
       ]
     },
     {
@@ -434,9 +380,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -448,7 +391,10 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip\n",
+        "import json\n",
+        "\n",
+        "import google.cloud.aiplatform as aiplatform\n",
+        "import tensorflow as tf\n",
         "from google_cloud_pipeline_components.v1.custom_job import utils\n",
         "from kfp import compiler, dsl\n",
         "from kfp.dsl import component"
@@ -462,7 +408,9 @@
       "source": [
         "#### Vertex AI Pipelines constants\n",
         "\n",
-        "Setup up the following constants for Vertex AI Pipelines:"
+        "Setup up the following constants for Vertex AI Pipelines:\n",
+        "\n",
+        "- `PIPELINE_ROOT` : Root folder to store pipeline artifacts in Cloud Storage bucket."
       ]
     },
     {
@@ -482,7 +430,7 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex AI SDK for Python\n",
+        "### Initialize Vertex AI SDK for Python\n",
         "\n",
         "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
@@ -495,7 +443,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -504,20 +452,20 @@
         "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
       },
       "source": [
-        "#### Set hardware accelerators\n",
+        "### Set hardware accelerators\n",
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Tesla T4 GPUs allocated to each VM, you'd specify:\n",
         "\n",
-        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
         "Learn more about [ hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It's a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "**Note**: TF releases before 2.3 for GPU support are expected to fail to load the custom model in this tutorial. It's a known issue and is fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -539,7 +487,7 @@
         "id": "container:training,prediction"
       },
       "source": [
-        "#### Set pre-built containers\n",
+        "### Set pre-built containers\n",
         "\n",
         "Set the pre-built Docker container image for training and prediction.\n",
         "\n",
@@ -558,7 +506,7 @@
       },
       "outputs": [],
       "source": [
-        "TF = \"2-5\"\n",
+        "TF = \"2-13\"\n",
         "\n",
         "if TRAIN_GPU:\n",
         "    TRAIN_VERSION = \"tf-gpu.{}\".format(TF)\n",
@@ -569,8 +517,8 @@
         "else:\n",
         "    DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
         "\n",
-        "TRAIN_IMAGE = \"gcr.io/cloud-aiplatform/training/{}:latest\".format(TRAIN_VERSION)\n",
-        "DEPLOY_IMAGE = \"gcr.io/cloud-aiplatform/prediction/{}:latest\".format(DEPLOY_VERSION)\n",
+        "TRAIN_IMAGE = \"gcr.io/vertex-ai/training/{}:latest\".format(TRAIN_VERSION)\n",
+        "DEPLOY_IMAGE = \"gcr.io/vertex-ai/prediction/{}:latest\".format(DEPLOY_VERSION)\n",
         "\n",
         "print(\"Training:\", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)\n",
         "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
@@ -582,7 +530,7 @@
         "id": "machine:training,prediction"
       },
       "source": [
-        "#### Set machine type\n",
+        "### Set machine type\n",
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
@@ -593,12 +541,12 @@
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -626,9 +574,9 @@
         "id": "tutorial_start:custom"
       },
       "source": [
-        "# Tutorial\n",
+        "## Tutorial\n",
         "\n",
-        "Now you are ready to start creating your own custom model and training for CIFAR10."
+        "Now you're ready to start training on CIFAR10 and create your own custom model."
       ]
     },
     {
@@ -641,7 +589,7 @@
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, you look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -653,7 +601,7 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom training job. **Note**: When referred to the file in the worker pool specification, the file suffix (`.py`) is dropped and the directory slash is replaced with a dot (`trainer.task`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
@@ -712,7 +660,7 @@
         "\n",
         "\n",
         "@component(\n",
-        "    base_image=\"tensorflow/tensorflow:latest\",\n",
+        "    base_image=\"tensorflow/tensorflow:2.13.0\",\n",
         "    packages_to_install=[\"tensorflow_datasets\", \"opencv-python-headless\"],\n",
         ")\n",
         "def custom_train_model(\n",
@@ -729,6 +677,7 @@
         "\n",
         "    import tensorflow as tf\n",
         "    import tensorflow_datasets as tfds\n",
+        "    from tensorflow import keras\n",
         "    from tensorflow.python.client import device_lib\n",
         "\n",
         "    faulthandler.enable()\n",
@@ -774,21 +723,19 @@
         "\n",
         "    # Build the Keras model\n",
         "    def build_and_compile_cnn_model(lr: int = 0.01):\n",
-        "        model = tf.keras.Sequential(\n",
+        "        model = keras.Sequential(\n",
         "            [\n",
-        "                tf.keras.layers.Conv2D(\n",
-        "                    32, 3, activation=\"relu\", input_shape=(32, 32, 3)\n",
-        "                ),\n",
-        "                tf.keras.layers.MaxPooling2D(),\n",
-        "                tf.keras.layers.Conv2D(32, 3, activation=\"relu\"),\n",
-        "                tf.keras.layers.MaxPooling2D(),\n",
-        "                tf.keras.layers.Flatten(),\n",
-        "                tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
+        "                keras.layers.Conv2D(32, 3, activation=\"relu\", input_shape=(32, 32, 3)),\n",
+        "                keras.layers.MaxPooling2D(),\n",
+        "                keras.layers.Conv2D(32, 3, activation=\"relu\"),\n",
+        "                keras.layers.MaxPooling2D(),\n",
+        "                keras.layers.Flatten(),\n",
+        "                keras.layers.Dense(10, activation=\"softmax\"),\n",
         "            ]\n",
         "        )\n",
         "        model.compile(\n",
-        "            loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
-        "            optimizer=tf.keras.optimizers.SGD(learning_rate=lr),\n",
+        "            loss=keras.losses.sparse_categorical_crossentropy,\n",
+        "            optimizer=keras.optimizers.SGD(learning_rate=lr),\n",
         "            metrics=[\"accuracy\"],\n",
         "        )\n",
         "        return model\n",
@@ -806,12 +753,14 @@
         "        model = build_and_compile_cnn_model(lr)\n",
         "\n",
         "    model.fit(x=train_dataset, epochs=epochs, steps_per_epoch=steps)\n",
-        "    model.save(model_dir)\n",
+        "\n",
+        "    # Save the model\n",
+        "    model.save(model_dir + \".keras\")\n",
         "\n",
         "    model_path_to_deploy = model_dir\n",
         "\n",
         "    # Load the saved model\n",
-        "    local_model = tf.keras.models.load_model(model_dir)\n",
+        "    local_model = keras.models.load_model(model_dir + \".keras\")\n",
         "\n",
         "    # Load evaluation data\n",
         "    import numpy as np\n",
@@ -920,7 +869,7 @@
         "\n",
         "Next, use the `create_custom_training_job_op_from_component` method to convert the custom component into a Vertex AI custom job pre-built component.\n",
         "\n",
-        "**replica_count :**  The  number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10."
+        "**replica_count :**  The  number of machine replicas the batch operation may be scaled to. Only used if **machine_type** is set. Default is 10."
       ]
     },
     {
@@ -944,11 +893,11 @@
       "source": [
         "### Define the pipeline for the custom training job\n",
         "\n",
-        "Next, define the pipeline job, consisting of the tasks:\n",
+        "Next, define the pipeline job that runs the following tasks:\n",
         "\n",
-        "- Train the custom model.\n",
-        "- Upload the model to a Verex AI Model resource.\n",
-        "- Execute a batch prediction."
+        "- Trains the custom model.\n",
+        "- Uploads the model to Verex AI Model Registry.\n",
+        "- Execute a batch prediction job."
       ]
     },
     {
@@ -983,7 +932,7 @@
         "        steps=steps,\n",
         "        distribute=distribute,\n",
         "        project=PROJECT_ID,\n",
-        "        location=REGION,\n",
+        "        location=LOCATION,\n",
         "        base_output_directory=PIPELINE_ROOT,\n",
         "    )\n",
         "\n",
@@ -992,7 +941,7 @@
         "        artifact_class=artifact_types.UnmanagedContainerModel,\n",
         "        metadata={\n",
         "            \"containerSpec\": {\n",
-        "                \"imageUri\": \"us-docker.pkg.dev/cloud-aiplatform/prediction/tf2-cpu.2-3:latest\"\n",
+        "                \"imageUri\": \"us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-13:latest\"\n",
         "            }\n",
         "        },\n",
         "    )\n",
@@ -1045,15 +994,13 @@
         "\n",
         "DISPLAY_NAME = \"cifar10\"\n",
         "\n",
-        "job = aip.PipelineJob(\n",
+        "job = aiplatform.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
         "    template_path=\"custom_model_training_spec.yaml\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         ")\n",
         "\n",
-        "job.run(service_account=SERVICE_ACCOUNT)\n",
-        "\n",
-        "! rm custom_model_training_spec.json"
+        "job.run(service_account=SERVICE_ACCOUNT)"
       ]
     },
     {
@@ -1075,12 +1022,8 @@
       },
       "outputs": [],
       "source": [
-        "import json\n",
-        "\n",
-        "import tensorflow as tf\n",
-        "\n",
         "PROJECT_NUMBER = job.gca_resource.name.split(\"/\")[1]\n",
-        "print(PROJECT_NUMBER)\n",
+        "print(\"Project number:\", PROJECT_NUMBER)\n",
         "\n",
         "\n",
         "def print_pipeline_output(job, output_task_name):\n",
@@ -1176,20 +1119,25 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "job.delete()\n",
-        "\n",
-        "model = aip.Model(model_id)\n",
+        "# Delete the model\n",
+        "model = aiplatform.Model(model_id)\n",
         "model.delete()\n",
         "\n",
-        "batch_job = aip.BatchPredictionJob(batch_job_id)\n",
+        "# Delete the batch prediction job\n",
+        "batch_job = aiplatform.BatchPredictionJob(batch_job_id)\n",
         "batch_job.delete()\n",
         "\n",
+        "# Delete the pipeline\n",
+        "job.delete()\n",
         "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -rf {BUCKET_URI}"
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil rm -r $BUCKET_URI\n",
+        "\n",
+        "# Remove the locally generated files\n",
+        "! rm custom_model_training_spec.yaml\n",
+        "! rm -rf custom"
       ]
     }
   ],

notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb

@@ -32,25 +32,28 @@
         "# Vertex AI Pipelines: Training and batch prediction with BigQuery source and destination for a custom tabular classification model \n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>  \n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
         "</table>\n",
+        "<br/><br/><br/><br/>\n",
         "\n",
         "*Note: This notebook uses KFP 1.x and GCPC 1.x. We recommend using 2.x*"
       ]
@@ -76,15 +79,15 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you train a scikit-learn tabular classification model and create batch prediction job for it through a Vertex AI pipeline using `google_cloud_pipeline_components`. The source and destination data for the batch prediction job is served in BigQuery.\n",
+        "In this tutorial, you train a scikit-learn tabular classification model and create a batch prediction job for it through a Vertex AI pipeline using google_cloud_pipeline_components. The source and destination data for the batch prediction job is served in BigQuery.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI `Pipelines`\n",
-        "- Vertex AI `Datasets`\n",
-        "- Vertex AI `Training`\n",
-        "- Vertex AI `Model Registry`\n",
-        "- Vertex AI `Batch Predictions`\n",
+        "- Vertex AI Pipelines\n",
+        "- Vertex AI dataset\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI Model Registry\n",
+        "- Vertex AI batch prediction\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -93,7 +96,7 @@
         "- Create a custom python package for training application.\n",
         "- Upload the python package to Cloud Storage.\n",
         "- Create a Vertex AI Pipeline that:\n",
-        "    - creates a Vertex AI Dataset from the source dataset.\n",
+        "    - creates a Vertex AI dataset from the source dataset.\n",
         "    - trains a scikit-learn RandomForest classification model on the dataset.\n",
         "    - uploads the trained model to Vertex AI Model Registry.\n",
         "    - runs a batch prediction job with the model on the test data.\n",
@@ -109,23 +112,23 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this notebook uses for training is available publicly at the BigQuery location `bigquery-public-data.ml_datasets.census_adult_income`. It consists of the following fields:\n",
+        "The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this notebook uses for training is available publicly at the BigQuery location bigquery-public-data.ml_datasets.census_adult_income. It consists of the following fields:\n",
         "\n",
-        "- `age`: Age.\n",
-        "- `workclass`: Nature of employment.\n",
-        "- `functional_weight`: Sample weight of the individual from the original Census data. How likely they were to be included in this dataset, based on their demographic characteristics vs. whole-population estimates.\n",
-        "- `education`: Level of education completed.\n",
-        "- `education_num`: Estimated years of education completed based on the value of the education field.\n",
-        "- `marital_status`: Marital status.\n",
-        "- `occupation`: Occupation category.\n",
-        "- `relationship`: Relationship to the household.\n",
-        "- `race`: Race.\n",
-        "- `sex`: Gender.\n",
-        "- `capital_gain`: Amount of capital gains.\n",
-        "- `capital_loss`: Amount of capital loss.\n",
-        "- `hours_per_week`: Hours worked per week.\n",
-        "- `native_country`: Country of birth.\n",
-        "- `income_bracket`: Either \" >50K\" or \" <=50K\" based on income."
+        "- age: Age.\n",
+        "- workclass: Nature of employment.\n",
+        "- functional_weight: Sample weight of the individual from the original Census data. How likely they were to be included in this dataset, based on their demographic characteristics vs. whole-population estimates.\n",
+        "- education: Level of education completed.\n",
+        "- education_num: Estimated years of education completed based on the value of the education field.\n",
+        "- marital_status: Marital status.\n",
+        "- occupation: Occupation category.\n",
+        "- relationship: Relationship to the household.\n",
+        "- race: Race.\n",
+        "- sex: Gender.\n",
+        "- capital_gain: Amount of capital gains.\n",
+        "- capital_loss: Amount of capital loss.\n",
+        "- hours_per_week: Hours worked per week.\n",
+        "- native_country: Country of birth.\n",
+        "- income_bracket: Either \" >50K\" or \" <=50K\" based on income."
       ]
     },
     {
@@ -141,11 +144,10 @@
         "* BigQuery\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), \n",
+        "[BigQuery pricing](https://cloud.google.com/bigquery/pricing), \n",
+        "[Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -154,9 +156,10 @@
         "id": "7b887879da0a"
       },
       "source": [
-        "## Installation\n",
+        "## Get started\n",
         "\n",
-        "Install the following packages required to execute this notebook. "
+        "\n",
+        "Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -172,99 +175,86 @@
         "                                 pandas \\\n",
         "                                 pyarrow \\\n",
         "                                 'kfp<2' \\\n",
-        "                                 'google-cloud-pipeline-components<2' \n",
-        "\n",
-        "! pip3 install --quiet db-dtypes "
+        "                                 'google-cloud-pipeline-components<2' \\\n",
+        "                                 db-dtypes "
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "d2f55b22b5fa"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "dcc98768955f"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "57dad372c81b"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9c06fe092778"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
-        "id": "4e166d927e36"
+        "id": "c97be6a73155"
       },
       "outputs": [],
       "source": [
-        "import random\n",
-        "import string\n",
+        "import sys\n",
         "\n",
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "    from google.colab import auth\n",
         "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "    auth.authenticate_user()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "d74b65fa97ed"
       },
       "source": [
-        "## Before you begin\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WReHDGG5g0XY"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -273,100 +263,10 @@
       "metadata": {
         "id": "oM1iC_MfAts1"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Updated property [core/project].\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -397,7 +297,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -408,7 +308,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -419,7 +319,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -505,8 +405,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "from google.cloud import aiplatform, bigquery\n",
         "from kfp.dsl import pipeline\n",
         "from kfp.v2 import compiler"
@@ -532,7 +430,7 @@
       "outputs": [],
       "source": [
         "# Initialize Vertex AI SDK\n",
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n",
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)\n",
         "\n",
         "# Initialize BigQuery client\n",
         "bq_client = bigquery.Client(\n",
@@ -563,11 +461,11 @@
         "# Source of the dataset\n",
         "DATA_SOURCE = \"bq://bigquery-public-data.ml_datasets.census_adult_income\"\n",
         "# Set name for the managed Vertex AI dataset\n",
-        "DATASET_DISPLAY_NAME = f\"adult_census_dataset_{UUID}\"\n",
+        "DATASET_DISPLAY_NAME = \"adult_census_dataset_unique\"\n",
         "# BigQuery Dataset name\n",
-        "BQ_DATASET_ID = f\"income_prediction_{UUID}\"\n",
+        "BQ_DATASET_ID = \"income_prediction_unique1\"\n",
         "# Set name for the BigQuery source table for batch prediction\n",
-        "BQ_INPUT_TABLE = f\"income_test_data_{UUID}\"\n",
+        "BQ_INPUT_TABLE = \"income_test_data_unique\"\n",
         "# Set the size(%) of the train set\n",
         "TRAIN_SPLIT = 0.9\n",
         "# Provide the container for training the model\n",
@@ -575,13 +473,13 @@
         "# Provide the container for serving the model\n",
         "SERVING_CONTAINER = \"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-23:latest\"\n",
         "# Set the display name for training job\n",
-        "TRAINING_JOB_DISPLAY_NAME = f\"income_classify_train_job_{UUID}\"\n",
+        "TRAINING_JOB_DISPLAY_NAME = \"income_classify_train_job_unique\"\n",
         "# Model display name for Vertex AI Model Registry\n",
-        "MODEL_DISPLAY_NAME = f\"income_classify_model_{UUID}\"\n",
+        "MODEL_DISPLAY_NAME = \"income_classify_model_unique\"\n",
         "# Set the name for batch prediction job\n",
-        "BATCH_PREDICTION_JOB_NAME = f\"income_classify_batch_pred_{UUID}\"\n",
+        "BATCH_PREDICTION_JOB_NAME = \"income_classify_batch_pred_unique\"\n",
         "# Dispaly name for the Vertex AI Pipeline\n",
-        "PIPELINE_DISPLAY_NAME = f\"income_classfiy_batch_pred_pipeline_{UUID}\"\n",
+        "PIPELINE_DISPLAY_NAME = \"income_classfiy_batch_pred_pipeline_unique\"\n",
         "# Filename to compile the pipeline to\n",
         "PIPELINE_FILE_NAME = f\"{PIPELINE_DISPLAY_NAME}.json\""
       ]
@@ -620,7 +518,7 @@
         "\n",
         "Query the public dataset source and create a test set in the created BigQuery dataset.\n",
         "\n",
-        "For batch prediction, your test set is created by randomly selecting a small fraction (1-`TRAIN_SPLIT`) of the source dataset."
+        "For batch prediction, test set is created by randomly selecting a small fraction (1-TRAIN_SPLIT) of the source dataset."
       ]
     },
     {
@@ -658,13 +556,13 @@
       "source": [
         "## Create a Python package for your training application\n",
         "\n",
-        "Before you perform the batch prediction task, you train the Random Forest classification model on the income census dataset. You perform the training through using a pre-built container in Vertex AI. For this purpose, you package the training application in the following steps.\n",
+        "Before you perform the batch prediction task, you train the Random Forest classification model on the income census dataset. You perform the training through using a prebuilt container in Vertex AI. For this purpose, you package the training application in the following steps.\n",
         "\n",
-        "Learn more about [creating a Python training application for a pre-built container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container).\n",
+        "Learn more about [creating a Python training application for a prebuilt container](https://cloud.google.com/vertex-ai/docs/training/create-python-prebuilt-container).\n",
         "\n",
         "### Prepare the source directory\n",
         "\n",
-        "Create a source directory named `python_package` with a `trainer` subfolder inside. Next, create a `__init__.py` file in the `trainer` folder to make it a package."
+        "Create a source directory named python_package with a trainer subfolder inside. Next, create a __init__.py file in the trainer folder to make it a package."
       ]
     },
     {
@@ -687,9 +585,9 @@
       },
       "source": [
         "### Create the trainer task\n",
-        "Within `trainer/`, create a module named `task.py` that serves as the entrypoint for your training code.\n",
+        "Within trainer/, create a module named task.py that serves as the entrypoint for your training code.\n",
         "\n",
-        "The trainer code below preprocesses the train set and store the preprocessing transforms in a scikit-learn pipeline. Further, a [Random Forest model is trained](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) on the preprocessed train data and added as an estimator to the pipeline. After saving the model, it is uploaded to the Cloud Storage bucket for deployment.\n",
+        "The trainer code below preprocesses the train set and stores the preprocessing transforms in a scikit-learn pipeline. Further, a [Random Forest model is trained](https://scikitlearn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) on the preprocessed train data and added as an estimator to the pipeline. After saving the model, it's uploaded to the Cloud Storage bucket for deployment.\n",
         "\n",
         "An advantage of using a scikit-learn pipeline is that it saves you from the trouble of writing additional scripts for preprocessing the data while generating predictions. \n",
         "\n",
@@ -803,7 +701,7 @@
         "    # To do this, each categorical column use a pipeline that extracts one feature column via\n",
         "    # SelectKBest(k=1) and a LabelBinarizer() to convert the categorical value to a numerical one.\n",
         "    # A scores array (created below) selects and extracts the feature column. The scores array is\n",
-        "    # created by iterating over the COLUMNS and checking if it is a CATEGORICAL_COLUMN.\n",
+        "    # created by iterating over the COLUMNS and checking if it's a CATEGORICAL_COLUMN.\n",
         "    for i, col in enumerate(COLUMNS):\n",
         "        if col in CATEGORICAL_COLUMNS:\n",
         "            # Create a scores array to get the individual categorical column.\n",
@@ -869,7 +767,7 @@
       },
       "source": [
         "### Create a setup file\n",
-        "Create a `setup.py` file that tells Setuptools how to create the source distribution. You also specify your application's standard dependencies as part of the `setup.py` file. Vertex AI uses pip to install your training application on the replicas that it allocates for your job. \n",
+        "Create a setup.py file that tells Setuptools how to create the source distribution. You also specify your application's standard dependencies as part of the setup.py file. Vertex AI uses pip to install your training application on the replicas that it allocates for your job. \n",
         "\n",
         "Learn more about [Setuptools](https://setuptools.readthedocs.io/en/latest/)."
       ]
@@ -906,7 +804,7 @@
       "source": [
         "### Create the source distribution\n",
         "\n",
-        "Run the following command to create a source distribution, `dist/trainer-0.1.tar.gz`."
+        "Run the following command to create a source distribution, dist/trainer-0.1.tar.gz."
       ]
     },
     {
@@ -928,7 +826,7 @@
       "source": [
         "### Copy the source distribution to Cloud Storage\n",
         "\n",
-        "To train the custom classification model using a pre-built container, copy the source distribution of your training application to a Cloud Storage path. While training you let the Vertex AI SDK locate the package through the `python_package_gcs_uri` parameter."
+        "To train the custom classification model using a prebuilt container, copy the source distribution of your training application to a Cloud Storage path. While training you let the Vertex AI SDK locate the package through the python_package_gcs_uri parameter."
       ]
     },
     {
@@ -952,11 +850,11 @@
         "\n",
         "All the preparations have been done for your pipeline. In the current step, you create a Vertex AI Pipeline that comprises the following components each serving their own purpose in order:\n",
         "\n",
-        "- `TabularDatasetCreateOp`: Creates a new managed tabular dataset in Vertex AI. \n",
-        "- `CustomPythonPackageTrainingJobRunOp`: Creates and runs a custom training job in Vertex AI using a Python package.\n",
-        "- `ModelBatchPredictOp`: Creates a batch prediction job in Vertex AI and waits for it to complete.\n",
+        "- TabularDatasetCreateOp: Creates a new managed tabular dataset in Vertex AI. \n",
+        "- CustomPythonPackageTrainingJobRunOp: Creates and runs a custom training job in Vertex AI using a Python package.\n",
+        "- ModelBatchPredictOp: Creates a batch prediction job in Vertex AI and waits for it to complete.\n",
         "\n",
-        "All the above components are imported from the `google-cloud-pipeline-components` Python library. Learn more about [Google Cloud Pipeline Components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.26/index.html)."
+        "All the above components are imported from the google-cloud-pipeline-components Python library. Learn more about [Google Cloud Pipeline Components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.26/index.html)."
       ]
     },
     {
@@ -1045,7 +943,7 @@
       "source": [
         "### Compile the pipeline\n",
         "\n",
-        "After defining your pipeline, compile it to a file (`PIPELINE_FILE_NAME`) in JSON or YAML format."
+        "After defining your pipeline, compile it to a file (PIPELINE_FILE_NAME) in `JSON` or `YAML` format."
       ]
     },
     {
@@ -1074,27 +972,27 @@
         "Now, define the paramters to run your pipeline.\n",
         "\n",
         "To pass the required arguments to the individual components in your pipeline, you define the following paramters:\n",
-        "- `project`: Project ID for the Google Cloud project where the pipeline needs to run.\n",
-        "- `location`: Region where the pipeline needs to run.\n",
-        "- `dataset_display_name`: Display name for the managed dataset resource in Vertex AI.\n",
-        "- `dataset_bq_source`: BigQuery table URI to serve as a source for the managed dataset in Vertex AI.\n",
-        "- `training_job_dispaly_name`: Display name for the the custom python package training job.\n",
-        "- `gcs_staging_directory`: Staging directory for Vertex AI to store training artifacts.\n",
-        "- `python_package_gcs_uri`: Cloud Storage path to the Python package for training.\n",
-        "- `python_package_module_name`: Module name (trainer task) inside the Python package for training.\n",
-        "- `training_split`: Percentage of the total data to be considered for training.\n",
-        "- `test_split`: Percentage of the total data to be considered for testing. Split percentage parameters provided for the **CustomPythonPackageTrainingJobRunOp** component should always sum up to 1.\n",
-        "- `training_container_uri`: Pre-built container image URI for training the model. \n",
-        "- `serving_container_uri`: Pre-built container image URI for serving the model on Vertex AI.\n",
-        "- `training_bigquery_destination`: The BigQuery project location where the training data is to be written to during training.\n",
-        "- `model_display_name`: Dispaly name for the model to be deployed in Vertex AI Model Registry.\n",
-        "- `batch_prediction_display_name`: Dispaly name for the batch prediction job.\n",
-        "- `batch_prediction_instances_format`: Format of the input instances for batch prediction.\n",
-        "- `batch_prediction_predictions_format`: Format of the results from the batch prediction.\n",
-        "- `batch_prediction_source_uri`: Source URI of the input data.\n",
-        "- `batch_prediction_destination_uri`: Destination URI where the batch prediction results need to be stored.\n",
+        "- project: Project ID for the Google Cloud project where the pipeline needs to run.\n",
+        "- location: Region where the pipeline needs to run.\n",
+        "- dataset_display_name: Display name for the managed dataset resource in Vertex AI.\n",
+        "- dataset_bq_source: BigQuery table URI to serve as a source for the managed dataset in Vertex AI.\n",
+        "- training_job_dispaly_name: Display name for the the custom python package training job.\n",
+        "- gcs_staging_directory: Staging directory for Vertex AI to store training artifacts.\n",
+        "- python_package_gcs_uri: Cloud Storage path to the Python package for training.\n",
+        "- python_package_module_name: Module name (trainer task) inside the Python package for training.\n",
+        "- training_split: Percentage of the total data to be considered for training.\n",
+        "- test_split: Percentage of the total data to be considered for testing. Split percentage parameters provided for the **CustomPythonPackageTrainingJobRunOp** component should always sum up to 1.\n",
+        "- training_container_uri: Prebuilt container image URI for training the model. \n",
+        "- serving_container_uri: Prebuilt container image URI for serving the model on Vertex AI.\n",
+        "- training_bigquery_destination: The BigQuery project location where the training data is to be written to during training.\n",
+        "- model_display_name: Dispaly name for the model to be deployed in Vertex AI Model Registry.\n",
+        "- batch_prediction_display_name: Dispaly name for the batch prediction job.\n",
+        "- batch_prediction_instances_format: Format of the input instances for batch prediction.\n",
+        "- batch_prediction_predictions_format: Format of the results from the batch prediction.\n",
+        "- batch_prediction_source_uri: Source URI of the input data.\n",
+        "- batch_prediction_destination_uri: Destination URI where the batch prediction results need to be stored.\n",
         "\n",
-        "**Note:** Though a test split percentage is provided, test data is not used during the training process. This test data is different from the test data created in earlier steps for batch prediction."
+        "**Note:** Though a test split percentage is provided, test data isn't used during the training process. This test data is different from the test data created in earlier steps for batch prediction."
       ]
     },
     {
@@ -1108,7 +1006,7 @@
         "# Define the parameters for running the pipeline\n",
         "parameters = {\n",
         "    \"project\": PROJECT_ID,\n",
-        "    \"location\": REGION,\n",
+        "    \"location\": LOCATION,\n",
         "    \"dataset_display_name\": DATASET_DISPLAY_NAME,\n",
         "    \"dataset_bq_source\": DATA_SOURCE,\n",
         "    \"training_job_dispaly_name\": TRAINING_JOB_DISPLAY_NAME,\n",
@@ -1139,12 +1037,12 @@
         "\n",
         "Create a Vertex AI Pipeline job and run it using the `PipelineJob` class.\n",
         "\n",
-        "The `PipelineJob` class takes the following parameters:\n",
+        "The PipelineJob class takes the following parameters:\n",
         "\n",
-        "- `display_name`: The display name of the Vertex AI Pipeline.\n",
-        "- `template_path`: The path of PipelineJob or PipelineSpec JSON (or YAML) file.\n",
-        "- `parameter_values`: The mapping from runtime parameter names to its values that control the pipeline run.\n",
-        "- `enable_caching`: Whether to turn on caching for the run.\n",
+        "- display_name: The display name of the Vertex AI pipeline.\n",
+        "- template_path: The path of PipelineJob or PipelineSpec (JSON or YAML) file.\n",
+        "- parameter_values: The mapping from runtime parameter names to its values that control the pipeline run.\n",
+        "- enable_caching: Whether to turn on caching for the run.\n",
         "\n",
         "Learn more about the `PipelineJob` class from [Vertex AI PipelineJob documentation](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.PipelineJob)."
       ]
@@ -1278,11 +1176,11 @@
         "\n",
         "Otherwise, you can delete the individual resources you created in this tutorial:\n",
         "\n",
-        "- Vertex AI Pipeline Job\n",
+        "- Vertex AI Pipeline job\n",
         "- Vertex AI TabularDataset\n",
-        "- Vertex AI Model\n",
+        "- Vertex AI model\n",
         "- Vertex AI Training job\n",
-        "- Vertex AI Batch Prediction job\n",
+        "- Vertex AI batch prediction job\n",
         "- BigQuery dataset\n",
         "- Cloud Storage bucket (Set `delete_bucket` to **True** to delete the Cloud Storage bucket)"
       ]
@@ -1316,8 +1214,10 @@
         "! bq rm -r -f -d $PROJECT_ID:$BQ_DATASET_ID\n",
         "\n",
         "# Delete Cloud Storage objects\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "! rm $PIPELINE_FILE_NAME"
       ]
     }
   ],

notebooks/official/pipelines/get_started_with_machine_management.ipynb

@@ -33,23 +33,26 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/get_started_with_machine_management.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/get_started_with_machine_management.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fget_started_with_machine_management.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>  \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/get_started_with_machine_management.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" aalt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                     \n",
+        "  </td>     \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/get_started_with_machine_management.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>\n"
       ]
     },
@@ -61,7 +64,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to manage machine resources when training as a component in `Vertex AI Pipelines`."
+        "This tutorial demonstrates how to manage machine resources when training as a component in Vertex AI Pipelines."
       ]
     },
     {
@@ -79,7 +82,7 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Pipelines`\n",
+        "- Vertex AI Pipelines\n",
         "\n",
         "The steps performed in this tutorial include:\n",
         "\n",
@@ -119,12 +122,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_mlops"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -139,55 +149,87 @@
         "\n",
         "! pip3 install --upgrade google-cloud-aiplatform \\\n",
         "                         google-cloud-pipeline-components --quiet\n",
-        "! pip3 install --upgrade kfp --quiet\n",
-        "! pip3 install --upgrade tensorflow==2.7 --quiet"
+        "! pip3 install kfp==2.7.0 --quiet\n",
+        "! pip3 install tensorflow==2.15.1 --quiet"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -201,101 +243,9 @@
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "! gcloud config set project {PROJECT_ID}\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -337,7 +287,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
       ]
     },
     {
@@ -479,16 +429,16 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you specify:\n",
         "\n",
-        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
         "Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "*Note*: TF releases before 2.3 for GPU support fails to load the custom model in this tutorial. It's a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -499,7 +449,7 @@
       },
       "outputs": [],
       "source": [
-        "TRAIN_GPU, TRAIN_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)\n",
+        "TRAIN_GPU, TRAIN_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 1)\n",
         "\n",
         "DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
       ]
@@ -529,16 +479,16 @@
       },
       "outputs": [],
       "source": [
-        "TF = \"2.5\".replace(\".\", \"-\")\n",
+        "TF = \"2.13\".replace(\".\", \"-\")\n",
         "TRAIN_VERSION = \"tf-gpu.{}\".format(TF)\n",
         "DEPLOY_VERSION = \"tf2-gpu.{}\".format(TF)\n",
         "\n",
         "\n",
-        "TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], TRAIN_VERSION\n",
+        "TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}.py310:latest\".format(\n",
+        "    LOCATION.split(\"-\")[0], TRAIN_VERSION\n",
         ")\n",
         "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        "    LOCATION.split(\"-\")[0], DEPLOY_VERSION\n",
         ")\n",
         "\n",
         "print(\"Training:\", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)\n",
@@ -555,19 +505,19 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "*Note: The following isn't supported for training:*\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs*."
       ]
     },
     {
@@ -665,7 +615,7 @@
         "    return model_dir\n",
         "\n",
         "\n",
-        "compiler.Compiler().compile(self_contained_training_component, \"demo_componet.yaml\")"
+        "compiler.Compiler().compile(self_contained_training_component, \"demo_component.yaml\")"
       ]
     },
     {
@@ -716,7 +666,7 @@
         "        .set_display_name(\"self-contained-training\")\n",
         "        .set_cpu_limit(CPU_LIMIT)\n",
         "        .set_memory_limit(MEMORY_LIMIT)\n",
-        "        .add_node_selector_constraint(\"NVIDIA_TESLA_K80\")\n",
+        "        .add_node_selector_constraint(\"NVIDIA_TESLA_T4\")\n",
         "        .set_gpu_limit(TRAIN_NGPU)\n",
         "    )\n",
         "\n",
@@ -986,14 +936,14 @@
         "    pipeline_root=PIPELINE_ROOT,\n",
         ")\n",
         "def pipeline(\n",
-        "    epochs: int, model_dir: str, project: str = PROJECT_ID, region: str = REGION\n",
+        "    epochs: int, model_dir: str, project: str = PROJECT_ID, region: str = LOCATION\n",
         "):\n",
         "    from google_cloud_pipeline_components.types import artifact_types\n",
         "    from google_cloud_pipeline_components.v1.model import ModelUploadOp\n",
         "    from kfp.dsl import importer_node\n",
         "\n",
         "    training_job_task = custom_job_op(\n",
-        "        epochs=epochs, model_dir=model_dir, project=project, location=region\n",
+        "        epochs=epochs, model_dir=model_dir, project=project, location=LOCATION\n",
         "    )\n",
         "\n",
         "    import_unmanaged_model_task = importer_node.importer(\n",
@@ -1156,13 +1106,13 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "# Set this to true only if you'd like to delete your bucket\n",
         "delete_bucket = False\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "if delete_bucket:\n",
+        "    ! gsutil rm -r $BUCKET_URI\n",
+        "\n",
+        "!rm -rf demo_component.yaml"
       ]
     }
   ],

notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb

@@ -23,6 +23,16 @@
         "# limitations under the License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "64a4f198313d"
+      },
+      "source": [
+        "Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis models by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text objectives until June 15, 2025. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see                                                             \n",
+        "[Introduction to tuning](https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-gemini-overview)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb

@@ -29,28 +29,30 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines\n",
+        " # Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fgoogle_cloud_pipeline_components_bqml_text.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "     </a>\n",
-        "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "</table>"
       ]
     },
     {
@@ -137,15 +139,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -162,68 +171,88 @@
         "                                    google-api-core \\\n",
         "                                    google-auth\n",
         "\n",
-        "! pip3 install --upgrade --quiet    tensorflow==2.8.0 \\\n",
-        "                                    tensorflow-hub==0.12.0 \\\n",
+        "! pip3 install --upgrade --quiet    tensorflow==2.13.1 \\\n",
+        "                                    tensorflow-hub==0.16.0 \\\n",
         "                                    kfp"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "613b1be08c68"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google "
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "f82e28c631cc"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91842ef41bbd"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -235,31 +264,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -270,7 +275,7 @@
       "source": [
         "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -293,64 +298,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -390,7 +337,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -401,7 +348,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -580,7 +527,7 @@
       "source": [
         "### Define constants\n",
         "\n",
-        "About the model you are going to use in preprocessing, you use the [Swivel](https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1) embedding which was trained on English Google News 130GB corpus and has 20 dimensions."
+        "About the model you're going to use in preprocessing, you use the [Swivel](https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1) embedding which was trained on English Google News 130GB corpus and has 20 dimensions."
       ]
     },
     {
@@ -626,7 +573,7 @@
       },
       "outputs": [],
       "source": [
-        "vertex_ai.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "vertex_ai.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -905,10 +852,10 @@
       "outputs": [],
       "source": [
         "%%writefile requirements.txt\n",
-        "apache-beam[gcp]==2.36.0\n",
-        "bs4==0.0.1\n",
+        "apache-beam[gcp]==2.47.0\n",
+        "bs4==0.0.2\n",
         "nltk==3.7\n",
-        "tensorflow==2.8.0"
+        "tensorflow==2.13.1"
       ]
     },
     {
@@ -950,9 +897,9 @@
         "import setuptools\n",
         "\n",
         "REQUIRED_PACKAGES = [\n",
-        "    'bs4==0.0.1',\n",
+        "    'bs4==0.0.2',\n",
         "    'nltk==3.7',\n",
-        "    'tensorflow==2.8.0']\n",
+        "    'tensorflow==2.13.1']\n",
         "\n",
         "setuptools.setup(\n",
         "    name='ingest',\n",
@@ -1027,7 +974,7 @@
       "source": [
         "#### Create BigQuery Dataset query\n",
         "\n",
-        "With this query, you create the Bigquery dataset schema that you are going to use to train your model."
+        "With this query, you create the Bigquery dataset schema that you're going to use to train your model."
       ]
     },
     {
@@ -1341,7 +1288,7 @@
         "    create_bq_prediction_query: str,\n",
         "    job_config: dict,\n",
         "    project: str = PROJECT_ID,\n",
-        "    region: str = REGION,\n",
+        "    region: str = LOCATION,\n",
         "):\n",
         "\n",
         "    from google_cloud_pipeline_components.v1.bigquery import (\n",
@@ -1528,16 +1475,23 @@
         "# delete the pipeline job\n",
         "pipeline.delete()\n",
         "\n",
-        "delete_bucket = False\n",
-        "delete_dataset = False\n",
-        "\n",
-        "# delete bucket\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete the Cloud Storage bucket\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI\n",
         "\n",
         "# delete dataset\n",
-        "if delete_dataset or os.getenv(\"IS_TESTING\"):\n",
-        "    ! bq rm -r -f -d $PROJECT_ID:$BQ_DATASET"
+        "delete_dataset = False  # Set True for deletion\n",
+        "if delete_dataset:\n",
+        "    ! bq rm -r -f -d $PROJECT_ID:$BQ_DATASET\n",
+        "\n",
+        "# Remove the locally generated files\n",
+        "!rm -r build\n",
+        "!rm -r data\n",
+        "!rm -r components\n",
+        "!rm -r src\n",
+        "!rm requirements.txt\n",
+        "!rm setup.py"
       ]
     }
   ],

notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb

@@ -32,21 +32,23 @@
         "# Vertex AI Pipelines:  Model train, upload, and deploy using Google Cloud Pipeline Components\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fgoogle_cloud_pipeline_components_model_train_upload_deploy.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>\n",
@@ -61,7 +63,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that trains a [custom model](https://cloud.google.com/vertex-ai/docs/training/containers-overview), uploads the model as a `Model` resource, creates an `Endpoint` resource, and deploys the `Model` resource to the `Endpoint` resource.\n",
+        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that trains a [custom model](https://cloud.google.com/vertex-ai/docs/training/containers-overview), uploads the model as a model resource, creates an endpoint resource, and deploys the model resource to the endpoint resource.\n",
         "\n",
         "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Custom training components](https://cloud.google.com/vertex-ai/docs/pipelines/customjob-component)."
       ]
@@ -74,27 +76,27 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build and deploy a custom model.\n",
+        "In this tutorial, you learn how to use Vertex AI Pipelines and Google Cloud pipeline component to build and deploy a custom model.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Pipelines`\n",
-        "- `Google Cloud Pipeline Components`\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- Vertex AI Pipelines\n",
+        "- Google Cloud pipeline components\n",
+        "- Vertex AI training\n",
+        "- Vertex AI model resource\n",
+        "- Vertex AI endpoint resource\n",
         "\n",
         "The steps performed include:\n",
         "\n",
         "- Create a KFP pipeline:\n",
         "    - Train a custom model.\n",
-        "    - Uploads the trained model as a `Model` resource.\n",
-        "    - Creates an `Endpoint` resource.\n",
-        "    - Deploys the `Model` resource to the `Endpoint` resource.\n",
+        "    - Uploads the trained model as a model resource.\n",
+        "    - Creates an endpoint resource.\n",
+        "    - Deploys the model resource to the endpoint resource.\n",
         "- Compile the KFP pipeline.\n",
-        "- Execute the KFP pipeline using `Vertex AI Pipelines`\n",
+        "- Execute the KFP pipeline using Vertex AI Pipelines.\n",
         "\n",
-        "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.39/google_cloud_pipeline_components.aiplatform.html).\n",
+        "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-0.2.0/google_cloud_pipeline_components.aiplatform.html).\n",
         "(From that page, see also the `CustomPythonPackageTrainingJobRunOp` and `CustomContainerTrainingJobRunOp` components, which similarly run 'custom' training, but as with the related `google.cloud.aiplatform.CustomContainerTrainingJob` and `google.cloud.aiplatform.CustomPythonPackageTrainingJob` methods from the [Vertex AI SDK](https://googleapis.dev/python/aiplatform/latest/aiplatform.html), also upload the trained model)."
       ]
     },
@@ -134,12 +136,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip:mbsdk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -152,67 +161,87 @@
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
         "                                 google-cloud-storage \\\n",
-        "                                 'kfp<2' \\\n",
-        "                                 'google-cloud-pipeline-components<2'"
+        "                                 kfp==2.7.0 \\\n",
+        "                                 google-cloud-pipeline-components"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -226,29 +255,9 @@
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "! gcloud config set project {PROJECT_ID}\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -259,7 +268,7 @@
       "source": [
         "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "To avoid name collisions between users on created resources, create a uuid for each session instance. Append these uuids to the respective names of the resources created in this tutorial."
       ]
     },
     {
@@ -282,64 +291,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -381,7 +332,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -414,7 +365,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
         "import sys\n",
         "\n",
         "IS_COLAB = \"google.colab\" in sys.modules\n",
@@ -484,7 +434,7 @@
         "\n",
         "import google.cloud.aiplatform as aip\n",
         "import kfp\n",
-        "from kfp.v2 import compiler  # noqa: F811"
+        "from kfp import compiler  # noqa: F811"
       ]
     },
     {
@@ -557,7 +507,9 @@
       "outputs": [],
       "source": [
         "hp_dict: str = '{\"num_hidden_layers\": 3, \"hidden_size\": 32, \"learning_rate\": 0.01, \"epochs\": 1, \"steps_per_epoch\": -1}'\n",
-        "data_dir: str = \"gs://cloud-samples-data/vertex-ai/pipeline-deployment/datasets/bikes_weather/\"\n",
+        "data_dir: str = (\n",
+        "    \"gs://cloud-samples-data/vertex-ai/pipeline-deployment/datasets/bikes_weather/\"\n",
+        ")\n",
         "TRAINER_ARGS = [\"--data-dir\", data_dir, \"--hptune-dict\", hp_dict]\n",
         "\n",
         "# create working dir to pass to job spec\n",
@@ -579,7 +531,7 @@
         "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
         "                                                              ModelDeployOp)\n",
         "    from google_cloud_pipeline_components.v1.model import ModelUploadOp\n",
-        "    from kfp.v2.components import importer_node\n",
+        "    from kfp.dsl import importer_node\n",
         "\n",
         "    custom_job_task = CustomTrainingJobOp(\n",
         "        project=project,\n",
@@ -593,9 +545,7 @@
         "                },\n",
         "                \"replicaCount\": \"1\",\n",
         "                \"machineSpec\": {\n",
-        "                    \"machineType\": \"n1-standard-16\",\n",
-        "                    \"accelerator_type\": aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
-        "                    \"accelerator_count\": 2,\n",
+        "                    \"machineType\": \"n1-standard-4\",\n",
         "                },\n",
         "            }\n",
         "        ],\n",
@@ -627,7 +577,7 @@
         "        endpoint=endpoint_create_op.outputs[\"endpoint\"],\n",
         "        model=model_upload_op.outputs[\"model\"],\n",
         "        deployed_model_display_name=model_display_name,\n",
-        "        dedicated_resources_machine_type=\"n1-standard-16\",\n",
+        "        dedicated_resources_machine_type=\"n1-standard-4\",\n",
         "        dedicated_resources_min_replica_count=1,\n",
         "        dedicated_resources_max_replica_count=1,\n",
         "    )"
@@ -781,7 +731,7 @@
       "outputs": [],
       "source": [
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb

@@ -32,25 +32,37 @@
         "# Vertex AI Pipelines:  model upload, predict, and evaluate using google-cloud-pipeline-components\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fgoogle_cloud_pipeline_components_model_upload_predict_evaluate.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
+        "      Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "     </a>\n",
-        "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2d983b52ddb0"
+      },
+      "source": [
         "*Note: This notebook uses KFP 1.x and GCPC 1.x. We recommend using 2.x*"
       ]
     },
@@ -62,7 +74,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) in conjunction with an experimental `evaluation` method, to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that uploads a tabular custom model as a `Model` resource, creates a `BatchPredictionJob` resource, and evaluates the `Model` resource with the `BatchPredictionJob` results to create an evaluation `system.Metrics` artifact. \n",
+        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) in conjunction with an experimental `evaluation` method, to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that uploads a tabular custom model as a model resource, creates a BatchPredictionJob resource, and evaluates the model resource with the BatchPredictionJob results to create an evaluation `system.Metrics` artifact. \n",
         "\n",
         "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Vertex AI Model components](https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component)."
       ]
@@ -85,9 +97,9 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Upload a pre-trained model as a `Model` resource.\n",
-        "- Run a `BatchPredictionJob` on the `Model` resource with ground truth data.\n",
-        "- Generate evaluation `Metrics` artifact about the `Model` resource.\n",
+        "- Upload a pretrained model as a model resource.\n",
+        "- Run a BatchPredictionJob on the model resource with ground truth data.\n",
+        "- Generate evaluation Metrics artifact about the model resource.\n",
         "- Compare the evaluation metrics to a threshold.\n"
       ]
     },
@@ -127,209 +139,121 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "d1ea81ac77f0"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e5d353aa47ac"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "QTmIIQ8QBfb6"
+        "id": "5d301b0e12dd"
       },
       "outputs": [],
       "source": [
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 'google-cloud-pipeline-components<2' \\\n",
-        "                                 'kfp<2'"
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform 'google-cloud-pipeline-components<2' 'kfp<2' google-api-python-client 'protobuf==3.20.3'"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "c87a2a5d7e35"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "5dccb1c8feb6"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c2fc3d7b6bfa"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f02130bff721"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "MIe7f62gBfb_"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -362,7 +286,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -373,7 +297,60 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3d5191a94246"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2da7120074be"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI, location=LOCATION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "timestamp"
+      },
+      "source": [
+        "#### UUID\n",
+        "\n",
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MIe7f62gBfb_"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import string\n",
+        "\n",
+        "\n",
+        "# Generate a uuid of a specifed length(default=8)\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -418,7 +395,7 @@
         "):\n",
         "    # Get your service account from gcloud\n",
         "    if not IS_COLAB:\n",
-        "        shell_output = !gcloud auth list 2>/dev/null\n",
+        "        shell_output = ! gcloud auth list 2>/dev/null\n",
         "        SERVICE_ACCOUNT = shell_output[2].replace(\"*\", \"\").strip()\n",
         "\n",
         "    else:  # IS_COLAB:\n",
@@ -504,28 +481,6 @@
         "PIPELINE_ROOT = \"{}/pipeline_root/safe_driver\".format(BUCKET_URI)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "m3atE5jVBfcE"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI, location=REGION)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -563,7 +518,7 @@
         "\n",
         "    import json\n",
         "\n",
-        "    with open(path, \"r\") as f:\n",
+        "    with open(path) as f:\n",
         "        data = json.load(f)\n",
         "\n",
         "    slices = data[\"slicedMetrics\"]\n",

notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb

@@ -32,20 +32,25 @@
         "# Vertex AI Pipelines: Metrics visualization and run comparison using the KFP SDK\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fmetrics_viz_run_compare_kfp.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> \n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> \n",
         "      Open in Vertex AI Workbench\n",
         "     </a>\n",
         "  </td>\n",
@@ -74,11 +79,11 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use the KFP SDK to build pipelines that generate evaluation metrics.\n",
+        "In this tutorial, you learn how to use the KFP SDK for Python to build pipelines that generate evaluation metrics.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
-        "- `Vertex AI Pipelines`\n",
+        "- Vertex AI Pipelines\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -127,11 +132,17 @@
         "* Vertex AI\n",
         "* Cloud Storage\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ea81ac77f0"
+      },
+      "source": [
+        "## Get started"
       ]
     },
     {
@@ -140,9 +151,7 @@
         "id": "install_aip:mbsdk"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -170,7 +179,9 @@
         "id": "58707a750154"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -181,11 +192,25 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b96b39fd4d7b"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
@@ -194,19 +219,26 @@
         "id": "BF1j6f9HApxa"
       },
       "source": [
-        "## Before you begin\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set up your Google Cloud project\n",
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "    from google.colab import auth\n",
         "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -215,12 +247,9 @@
         "id": "WReHDGG5g0XY"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -232,31 +261,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -290,64 +295,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -356,9 +303,7 @@
       "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets.\n",
-        "\n",
-        "- *{Note to notebook author: For any user-provided strings that need to be unique (like bucket names or model ID's), append \"-unique\" to the end so proper testing can occur}*"
+        "Create a storage bucket to store intermediate artifacts such as datasets."
       ]
     },
     {
@@ -389,7 +334,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -398,7 +343,7 @@
         "id": "set_service_account"
       },
       "source": [
-        "#### Service Account\n",
+        "### Service Account\n",
         "\n",
         "**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below."
       ]
@@ -452,7 +397,7 @@
       "source": [
         "#### Set service account access for Vertex AI Pipelines\n",
         "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run these once per service account."
       ]
     },
     {
@@ -474,9 +419,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -499,9 +441,11 @@
         "id": "pipeline_constants"
       },
       "source": [
-        "#### Vertex AI Pipelines constants\n",
+        "#### Vertex AI constants\n",
         "\n",
-        "Setup up the following constants for Vertex AI Pipelines:"
+        "Setup up the following constants for Vertex AI pipelines:\n",
+        "- `PIPELINE_NAME`: Set name for the pipeline.\n",
+        "- `PIPELINE_ROOT`: Cloud Storage bucket path to store pipeline artifacts."
       ]
     },
     {
@@ -512,6 +456,7 @@
       },
       "outputs": [],
       "source": [
+        "PIPELINE_NAME = \"metrics-pipeline-v2\"\n",
         "PIPELINE_ROOT = \"{}/pipeline_root/iris\".format(BUCKET_URI)"
       ]
     },
@@ -523,7 +468,7 @@
       "source": [
         "## Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+        "To get started using Vertex AI, you must [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)."
       ]
     },
     {
@@ -547,16 +492,16 @@
         "\n",
         "In this section, you define some Python function-based components that use scikit-learn to train some classifiers and produce evaluations that can be visualized.\n",
         "\n",
-        "Note the use of the `@component()` decorator in the definitions below. You can optionally set a list of packages for the component to install; the base image to use (the default is a Python 3.7 image); and the name of a component YAML file to generate, so that the component definition can be shared and reused.\n",
+        "Note the use of the `@component()` decorator in the definitions below. Optionally, you can set a list of packages for the component to install. That is, list the base image to use (the default is a Python 3.7 image), and the name of a component YAML file to generate, so that the component definition can be shared and reused.\n",
         "\n",
         "#### Define wine_classification component\n",
         "\n",
         "The first component shows how to visualize an *ROC curve*.\n",
-        "Note that the function definition includes an output called `wmetrics`, of type `Output[ClassificationMetrics]`. You can visualize the metrics in the Pipelines user interface in the Cloud Console.\n",
+        "Note that the function definition includes an output called `wmetrics`, of type `Output[ClassificationMetrics]`. You can visualize the metrics in the pipeline's user interface in the Google Cloud console.\n",
         "\n",
-        "To do this, this example uses the artifact's `log_roc_curve()` method. This method takes as input arrays with the false positive rates, true positive rates, and thresholds, as [generated by the `sklearn.metrics.roc_curve` function](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html).\n",
+        "To do so, this example uses the artifact's `log_roc_curve()` method. This method takes input arrays with the false positive rates, true positive rates, and thresholds, as [generated by the `sklearn.metrics.roc_curve` function](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html).\n",
         "\n",
-        "When you evaluate the cell below, a task factory function called `wine_classification` is created, that is used to construct the pipeline definition.  In addition, a component YAML file is created, which can be shared and loaded via file or URL to create the same task factory function."
+        "When you evaluate the cell below, a task factory function called `wine_classification` is created that is used to construct the pipeline definition.  In addition, a component YAML file is created, which can be shared and loaded via file or URL to create the same task factory function."
       ]
     },
     {
@@ -567,7 +512,9 @@
       },
       "outputs": [],
       "source": [
-        "@component(packages_to_install=[\"scikit-learn==1.2.2\"], base_image=\"python:3.9\")\n",
+        "@component(\n",
+        "    packages_to_install=[\"scikit-learn==1.2\", \"numpy==1.26.4\"], base_image=\"python:3.9\"\n",
+        ")\n",
         "def wine_classification(wmetrics: Output[ClassificationMetrics]):\n",
         "    from sklearn.datasets import load_wine\n",
         "    from sklearn.ensemble import RandomForestClassifier\n",
@@ -612,7 +559,9 @@
       },
       "outputs": [],
       "source": [
-        "@component(packages_to_install=[\"scikit-learn==1.2.2\"], base_image=\"python:3.9\")\n",
+        "@component(\n",
+        "    packages_to_install=[\"scikit-learn==1.2\", \"numpy==1.26.4\"], base_image=\"python:3.9\"\n",
+        ")\n",
         "def iris_sgdclassifier(\n",
         "    test_samples_fraction: float,\n",
         "    metricsc: Output[ClassificationMetrics],\n",
@@ -659,7 +608,7 @@
       "outputs": [],
       "source": [
         "@component(\n",
-        "    packages_to_install=[\"scikit-learn==1.2.2\"],\n",
+        "    packages_to_install=[\"scikit-learn==1.2\", \"numpy==1.26.4\"],\n",
         "    base_image=\"python:3.9\",\n",
         ")\n",
         "def iris_logregression(\n",
@@ -710,7 +659,9 @@
       "source": [
         "### Define the pipeline\n",
         "\n",
-        "Next, define a simple pipeline that uses the components that were created in the previous section."
+        "Next, define a simple pipeline that uses the above components.\n",
+        "\n",
+        "**Note:** In the `@dsl.pipeline` decorator, you define `PIPELINE_ROOT` as the Cloud Storage path that's used as root folder. You can choose to skip it, but you have to provide it when creating the pipeline run."
       ]
     },
     {
@@ -721,9 +672,6 @@
       },
       "outputs": [],
       "source": [
-        "PIPELINE_NAME = \"metrics-pipeline-v2\"\n",
-        "\n",
-        "\n",
         "@dsl.pipeline(\n",
         "    # Default pipeline root. You can override it when submitting the pipeline.\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
@@ -784,7 +732,7 @@
       "source": [
         "DISPLAY_NAME = \"iris_\" + UUID\n",
         "\n",
-        "job = aip.PipelineJob(\n",
+        "job1 = aip.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
         "    template_path=\"tabular_classification_pipeline.yaml\",\n",
         "    job_id=f\"tabular-classification-v2{UUID}-1\",\n",
@@ -792,7 +740,7 @@
         "    parameter_values={\"seed\": 7, \"splits\": 10},\n",
         ")\n",
         "\n",
-        "job.run()"
+        "job1.run()"
       ]
     },
     {
@@ -801,13 +749,13 @@
         "id": "view_pipeline_run:metrics"
       },
       "source": [
-        "Click on the generated link to see your run in the Cloud Console.\n",
+        "Click on the generated link to see your run in the Google Cloud console.\n",
         "\n",
         "<!-- It should look something like this as it is running:\n",
         "\n",
         "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" width=\"40%\"/></a> -->\n",
         "\n",
-        "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them."
+        "In the Google Cloud console, many of the pipeline DAG nodes expand or collapse when you click them."
       ]
     },
     {
@@ -816,22 +764,17 @@
         "id": "compare_pipeline_runs:ui"
       },
       "source": [
-        "## Comparing pipeline runs in the UI\n",
+        "## Comparing pipeline runs in the Google Cloud console\n",
         "\n",
         "Next, generate another pipeline run that uses a different `seed` and `split` for the `iris_logregression` step.\n",
         "\n",
-        "Submit the new pipeline run:\n",
+        "Pass the input parameters required for the pipeline and run it. The defined pipeline takes the following parameters:\n",
         "\n",
-        "\n",
-        "**pipeline_root :**   Specify a Cloud Storage URI that your pipelines service account can access. The artifacts of your pipeline runs are stored within the pipeline root. \n",
-        "\n",
-        "**display_name :**  The name of the pipeline, this will show up in the Google Cloud console. \n",
-        "\n",
-        "**parameter_values :**  The pipeline parameters to pass to this run. For example, create a dict() with the parameter names as the dictionary keys and the parameter values as the dictionary values. \n",
-        "\n",
-        "**job_id :**  A unique identifier for this pipeline run. If the job ID is not specified, Vertex AI Pipelines creates a job ID for you using the pipeline name and the timestamp of when the pipeline run was started. \n",
-        "\n",
-        "**template_path :**  complete pipeline path"
+        "- `pipeline_root`: Specify a Cloud Storage URI that your pipelines service account can access. The artifacts of your pipeline runs are stored within the pipeline root. \n",
+        "- `display_name`: The name of the pipeline, that shows up in the Google Cloud console. \n",
+        "- `parameter_values`: The pipeline parameters to pass to this run. For example, create a dict() with the parameter names as the dictionary keys and the parameter values as the dictionary values. \n",
+        "- `job_id`: A unique identifier for this pipeline run. If the job ID is not specified, Vertex AI Pipelines creates a job ID for you using the pipeline name and the timestamp of when the pipeline run was started. \n",
+        "- `template_path`: Complete pipeline path"
       ]
     },
     {
@@ -842,7 +785,7 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.PipelineJob(\n",
+        "job2 = aip.PipelineJob(\n",
         "    display_name=\"iris_\" + UUID,\n",
         "    template_path=\"tabular_classification_pipeline.yaml\",\n",
         "    job_id=f\"tabular-classification-pipeline-v2{UUID}-2\",\n",
@@ -850,7 +793,7 @@
         "    parameter_values={\"seed\": 5, \"splits\": 7},\n",
         ")\n",
         "\n",
-        "job.run()"
+        "job2.run()"
       ]
     },
     {
@@ -859,7 +802,7 @@
         "id": "compare_pipeline_runs:ui"
       },
       "source": [
-        "When both pipeline runs have finished, compare their results by navigating to the pipeline runs list in the Cloud Console, selecting both of them, and clicking **COMPARE** at the top of the Console panel."
+        "When both pipeline runs have finished, compare their results by navigating to the pipeline runs list in the Google Cloud console, selecting both of them, and clicking **COMPARE** at the top of the Console panel."
       ]
     },
     {
@@ -962,7 +905,7 @@
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:"
+        "Otherwise, you can delete the individual resources you created in this tutorial."
       ]
     },
     {
@@ -973,12 +916,12 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "delete_bucket = False\n",
         "\n",
-        "job.delete()\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "job1.delete()\n",
+        "job2.delete()\n",
+        "\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI\n",
         "\n",
         "! rm -rf tabular_classification_pipeline.yaml"

notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb

@@ -32,38 +32,27 @@
         "# Multicontender vs Champion methodology for model deployment into production\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fmulticontender_vs_champion_deployment_method.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                     \n",
-        "</table>\n",
-        "<br/>\n",
-        "*Note: This notebook uses KFP 1.x and GCPC 1.x. We recommend using 2.x*"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "24743cf4a1e1"
-      },
-      "source": [
-        "**_NOTE_**: This notebook has been tested in the following environment:\n",
-        "\n",
-        "* Python version = 3.9"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -74,7 +63,13 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial shows how to use Vertex AI Pipeline for deploying the next version of a model into production using the multicontender vs champion method."
+        "This tutorial shows how to use Vertex AI Pipelines for deploying the next version of a model into production using the multicontender vs champion method.\n",
+        "\n",
+        "**_NOTE_**: This notebook uses KFP 1.x and GCPC 1.x. It's recommended to use 2.x.\n",
+        "\n",
+        "**_NOTE_**: This notebook has been tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
       ]
     },
     {
@@ -87,7 +82,7 @@
         "\n",
         "In this tutorial, you learn how to construct a Vertex AI pipeline, which evaluates new production data from a deployed (production) model against other versions (contenders) of the model, to determine if a contender model becomes the champion model for replacement in production.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Vertex AI services and resources:\n",
         "\n",
         "- Vertex AI Pipeline\n",
         "- Vertex AI Model Evaluation\n",
@@ -97,19 +92,19 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Import a pretrained (champion) model to the `Vertex AI Model Registry`.\n",
+        "- Import a pretrained (champion) model to the Vertex AI Model Registry.\n",
         "- Import synthetic model training evaluation metrics to the corresponding (champion) model.\n",
-        "- Create a `Vertex AI Endpoint` resource\n",
-        "- Deploy the champion model to the `Endpoint` resource.\n",
+        "- Create a Vertex AI endpoint resource.\n",
+        "- Deploy the champion model to the endpoint resource.\n",
         "- Import additional (contender) versions of the deployed model.\n",
         "- Import synthetic model training evaluation metrics to the corresponding (contender) models.\n",
-        "- Create a Vertex AI Pipeline\n",
+        "- Create a Vertex AI Pipeline that runs the following steps:\n",
         "    - Get the champion model.\n",
-        "    - (Fake) Fine-tune champion model with production data\n",
+        "    - (Fake) Fine-tune champion model with production data.\n",
         "    - Import synthetic train+production evaluation metrics for the champion model.\n",
         "    - Get the contender models.\n",
-        "    - (Fake) Fine-tune contender model with production data\n",
-        "    - Import synthetic train+production evaluation metrics for the contenders modesl.\n",
+        "    - (Fake) Fine-tune contender model with production data.\n",
+        "    - Import synthetic train+production evaluation metrics for the contenders models.\n",
         "    - Compare the evaluations of the contenders to the champion and set the new champion as the default.\n",
         "    - Deploy the new champion model.\n",
         "\n",
@@ -148,15 +143,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ea81ac77f0"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -183,7 +185,9 @@
         "id": "58707a750154"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -194,11 +198,53 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c87a2a5d7e35"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5dccb1c8feb6"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -207,33 +253,9 @@
         "id": "BF1j6f9HApxa"
       },
       "source": [
-        "## Before you begin\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API]\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WReHDGG5g0XY"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -245,103 +267,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74ccc9e52986"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f6b2ccc891ed"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -372,7 +298,7 @@
         "id": "-EcIXiGsCePi"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -383,7 +309,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -396,7 +322,7 @@
         "\n",
         "You use a service account to create Vertex AI Pipeline jobs.\n",
         "\n",
-        "If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -447,7 +373,7 @@
       "source": [
         "#### Set service account access for Vertex AI Pipelines\n",
         "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run these once per service account."
       ]
     },
     {
@@ -497,7 +423,9 @@
       "source": [
         "### Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
+        "To get started using Vertex AI, you must [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your Google Cloud project.\n",
+        "\n",
+        "Then, initialize the Vertex AI SDK for Python for your project."
       ]
     },
     {
@@ -508,7 +436,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -521,9 +449,9 @@
         "\n",
         "You can set hardware accelerators for training and prediction.\n",
         "\n",
-        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you would specify:\n",
         "\n",
-        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
@@ -550,7 +478,7 @@
       "source": [
         "#### Set pre-built containers\n",
         "\n",
-        "Set the pre-built Docker container image for training and prediction.\n",
+        "Set the pre-built Docker container image for prediction.\n",
         "\n",
         "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
       ]
@@ -571,7 +499,7 @@
         "    DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
         "\n",
         "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        "    LOCATION.split(\"-\")[0], DEPLOY_VERSION\n",
         ")\n",
         "\n",
         "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
@@ -585,32 +513,21 @@
       "source": [
         "#### Set machine type\n",
         "\n",
-        "Next, set the machine type to use for prediction.\n",
+        "Next, set the machine type to use for deployment.\n",
         "\n",
-        "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
-        " - `machine type`\n",
-        "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
-        "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
-        "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
-        " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
+        "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for deployment. For this, you specify:\n",
+        "     - `machine_type`\n",
+        "         - `n1-standard`: 3.75GB of memory per vCPU.\n",
+        "         - `n1-highmem`: 6.5GB of memory per vCPU\n",
+        "         - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
+        "     - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following aren't supported.\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "63de49055083"
-      },
-      "source": [
-        "### Save the model artifacts\n",
-        "\n",
-        "At this point, the model is in memory. Next, you save the model artifacts to a Cloud Storage location."
+        "**Note**: You may also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -621,7 +538,9 @@
       },
       "outputs": [],
       "source": [
-        "DEPLOY_COMPUTE = \"n1-standard-4\"\n",
+        "machine_type = \"n1-standard\"\n",
+        "vCPUs = \"4\"\n",
+        "DEPLOY_COMPUTE = f\"{machine_type}-{vCPUs}\"\n",
         "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
       ]
     },
@@ -633,7 +552,7 @@
       "source": [
         "## Get pretrained model from TensorFlow Hub\n",
         "\n",
-        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to a `Vertex AI Model` resource. Once you have a `Vertex AI Model` resource, the model can be deployed to a `Vertex AI Endpoint` resource.\n",
+        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to Vertex AI Model Registry. Once you have a Vertex AI model resource, the model can be deployed to a Vertex AI endpoint resource.\n",
         "\n",
         "### Download the pretrained model\n",
         "\n",
@@ -657,6 +576,17 @@
         "tfhub_model.summary()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dcee58290f19"
+      },
+      "source": [
+        "### Save the model artifacts\n",
+        "\n",
+        "At this point, the model is in memory. Next, save the model artifacts to a Cloud Storage location."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -665,6 +595,7 @@
       },
       "outputs": [],
       "source": [
+        "# Save the model to Cloud Storage path\n",
         "MODEL_DIR = BUCKET_URI + \"/model\"\n",
         "tfhub_model.save(MODEL_DIR)"
       ]
@@ -675,17 +606,17 @@
         "id": "e8ce91147c93"
       },
       "source": [
-        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
+        "## Upload the TensorFlow Hub model to Vertex AI Model Registry\n",
         "\n",
-        "Finally, you upload the model artifacts from the TFHub model into a `Vertex AI Model` resource using the method `upload()`, with the following parameters:\n",
+        "Finally, you upload the model artifacts from the TFHub model into Vertex AI Model Registry and get a model resource object using the method `upload()`, with the following parameters:\n",
         "\n",
-        "- `display_name`: A human readable name for the `Model` resource.\n",
+        "- `display_name`: A human readable name for the model resource.\n",
         "- `artifact_uri`: The Cloud Storage location of the model package.\n",
         "- `serving_container_image_uri`: The serving container image.\n",
         "\n",
-        "Uploading a model into a Vertex AI Model resource returns a long running operation, since it may take a few moments. \n",
+        "Uploading a model into a Vertex AI Model Registry returns a long running operation, since it may take a few moments.\n",
         "\n",
-        "*Note:* When you upload the model artifacts to a `Vertex AI Model` resource, you specify the corresponding deployment container image."
+        "**Note:** When you upload the model artifacts to Vertex AI Model Registry, specify the corresponding deployment container image."
       ]
     },
     {
@@ -713,7 +644,7 @@
         "id": "c11e98ef5391"
       },
       "source": [
-        "### Create a model evaluation\n",
+        "## Create a model evaluation\n",
         "\n",
         "First, you create a model evaluation in a format that corresponds to one of the predefined schemas for model evaluations. In this example, you use the schema for a classification metric, and specify the following subset of evaluation metrics as a dictionary:\n",
         "\n",
@@ -726,7 +657,7 @@
         "- `metrics_schema_uri`: The schema for the specific type of evaluation metrics.\n",
         "- `metrics`: The dictionary with the evaluation metrics.\n",
         "\n",
-        "Learn more about [Schemas for evaluation metrics](https://cloud.google.com/vertex-ai/docs/evaluation/introduction#features)"
+        "Learn more about [Schemas for evaluation metrics](https://cloud.google.com/vertex-ai/docs/evaluation/introduction#features)."
       ]
     },
     {
@@ -757,7 +688,7 @@
         "\n",
         "Next, upload the model's evaluation from the custom training job to the corresponding entry in the Vertex AI Model Registry.\n",
         "\n",
-        "Currently, there is not yet support for this method in the SDK. Instead, you use the lower level GAPIC API interface."
+        "Currently, the Python SDK does not support for this method. Instead, use the lower level GAPIC API interface."
       ]
     },
     {
@@ -768,7 +699,7 @@
       },
       "outputs": [],
       "source": [
-        "API_ENDPOINT = f\"{REGION}-aiplatform.googleapis.com\"\n",
+        "API_ENDPOINT = f\"{LOCATION}-aiplatform.googleapis.com\"\n",
         "client = gapic.ModelServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
         "\n",
         "client.import_model_evaluation(\n",
@@ -782,13 +713,13 @@
         "id": "628de0914ba1"
       },
       "source": [
-        "### Creating an `Endpoint` resource\n",
+        "## Creating an endpoint resource\n",
         "\n",
-        "You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
+        "You create an endpoint resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
         "\n",
         "In this example, the following parameters are specified:\n",
         "\n",
-        "- `display_name`: A human readable name for the `Endpoint` resource.\n",
+        "- `display_name`: A human readable name for the endpoint resource.\n",
         "- `project`: Your project ID.\n",
         "- `location`: Your region.\n",
         "\n",
@@ -806,7 +737,7 @@
       "outputs": [],
       "source": [
         "endpoint = aiplatform.Endpoint.create(\n",
-        "    display_name=\"production\", project=PROJECT_ID, location=REGION\n",
+        "    display_name=\"production\", project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "\n",
         "print(endpoint)"
@@ -818,9 +749,9 @@
         "id": "ca3fa3f6a894"
       },
       "source": [
-        "### Deploy the `Model` resource to the `Endpoint` resource\n",
+        "## Deploy the model resource to the endpoint resource\n",
         "\n",
-        "Next, you deploy the blessed `Vertex AI Model` resource to a `Vertex AI Endpoint` resource. The `Vertex AI Model` resource already has defined for it the deployment container image. To deploy, you specify the following additional configuration settings:\n",
+        "Next, you deploy the blessed Vertex AI model resource to a Vertex AI endpoint resource. The container image defined for the Vertex AI model resource is used for deployment. To deploy, you specify the following additional configuration settings:\n",
         "\n",
         "- The machine type.\n",
         "- The (if any) type and number of GPUs.\n",
@@ -828,11 +759,11 @@
         "\n",
         "In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
         "\n",
-        "- `model`: The `Model` resource.\n",
+        "- `model`: The model resource.\n",
         "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
         "- `machine_type`: The machine type for each VM instance.\n",
         "\n",
-        "Do to the requirements to provision the resource, this may take upto a few minutes."
+        "This may take a few minutes due to the provisioning of the configured resources."
       ]
     },
     {
@@ -958,10 +889,10 @@
         "\n",
         "First, you define a component to import the evaluation metrics for the challenger model to the Model Registry. The component takes the following arguments:\n",
         "\n",
-        "- display_name: Human readable name for the evaluation metrics\n",
-        "- metrics: The evaluation metrics formatted for classification.\n",
-        "- parent_model_resource: The full resource name for the challenger model version.\n",
-        "- region: The region."
+        "- `display_name`: Human readable name for the evaluation metrics\n",
+        "- `metrics`: The evaluation metrics formatted for classification.\n",
+        "- `parent_model_resource`: The full resource name for the challenger model version.\n",
+        "- `region`: The region where you want to create or use the resources."
       ]
     },
     {
@@ -1013,7 +944,7 @@
       "source": [
         "###  Create component to compare metrics\n",
         "\n",
-        "Next, you define a component to compare the `auPrc` metric between the champion and contender versions of the model.  Whomever has the best `auPrc` value is set as the default model. When you subsequently deploy, the default model is deployed. The component takes the following arguments:\n",
+        "Next, you define a component to compare the **auPrc** metric between the champion and contender versions of the model.  Whichever has the best **auPrc** value is set as the default model. When you subsequently deploy, the default model is deployed. The component takes the following arguments:\n",
         "\n",
         "- `champion_resource_name`: The full resource name of the champion model.\n",
         "- `contender_model_resource_names`: A list of the full resource namea of the contender models."
@@ -1064,9 +995,13 @@
         "id": "1a61dc08ba46"
       },
       "source": [
-        "## Construct champion vs multi-contender pipeline\n",
+        "## Champion vs multi-contender pipeline\n",
         "\n",
-        "Next, you construct a pipeline for the following tasks:\n",
+        "In this section, you construct a pipeline to fetch the champion, and the challenger models and compare them to deploy the best one to the production endpoint.\n",
+        "\n",
+        "### Define the pipeline\n",
+        "\n",
+        "Define your pipeline to run the following tasks:\n",
         "\n",
         "- Get the champion version of a model.\n",
         "- Get the endpoint for the deployed champion model.\n",
@@ -1097,7 +1032,7 @@
         "    endpoint_resource_name: str,\n",
         "    endpoint_resource_uri: str,\n",
         "    project: str = PROJECT_ID,\n",
-        "    region: str = REGION,\n",
+        "    region: str = LOCATION,\n",
         "):\n",
         "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
         "        GetVertexModelOp\n",
@@ -1170,7 +1105,7 @@
       "source": [
         "### Compile the pipeline\n",
         "\n",
-        "Next, you compile the pipeline. "
+        "Next, you compile the pipeline to a JSON file."
       ]
     },
     {
@@ -1181,6 +1116,7 @@
       },
       "outputs": [],
       "source": [
+        "# Compile the pipeline to a json file\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline, package_path=\"multicontender_vs_champion.json\"\n",
         ")"
@@ -1202,7 +1138,7 @@
         "- `endpoint_resource_name`: The full resource name of the production endpoint.\n",
         "- `endpoint_resource_uri`: The full URI for the production endpoint.\n",
         "- `project`:The project ID.\n",
-        "- `region`: The region"
+        "- `region`: The region where you want to run your pipeline."
       ]
     },
     {
@@ -1231,14 +1167,12 @@
         "        \"endpoint_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
         "        + endpoint.resource_name,\n",
         "        \"project\": PROJECT_ID,\n",
-        "        \"region\": REGION,\n",
+        "        \"region\": LOCATION,\n",
         "    },\n",
         "    enable_caching=False,\n",
         ")\n",
         "\n",
-        "job.run()\n",
-        "\n",
-        "! rm multicontender_vs_champion.json"
+        "job.run()"
       ]
     },
     {
@@ -1249,9 +1183,9 @@
       "source": [
         "### Get the latest state of the production endpoint\n",
         "\n",
-        "Now that the pipeline has finished, the contender (version 3) model has replaced the previous champion model on the production endpoint.\n",
+        "Once your pipeline execution is finished, notice that the contender (version 3) model has replaced the previous champion model on the production endpoint.\n",
         "\n",
-        "Next you display the latest information on the deployed models for the production endpoint, and then display the traffic split. The resource ID for the 100% entry is the resource ID for the contender (verson 3) model."
+        "Now, display the latest details of the deployed models from the production endpoint, and then display the traffic split. Notice that the resource ID for the 100% entry is the resource ID for the contender (verson 3) model."
       ]
     },
     {
@@ -1290,31 +1224,25 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "# Undeploy the model from endpoint\n",
+        "endpoint.undeploy_all()\n",
         "\n",
-        "# Delete endpoint resource\n",
-        "try:\n",
-        "    endpoint.undeploy_all()\n",
-        "    endpoint.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the endpoint resource\n",
+        "endpoint.delete()\n",
         "\n",
         "# Delete model resource\n",
-        "try:\n",
-        "    champion_model.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "champion_model.delete()\n",
         "\n",
-        "# Delete the pipeline resource\n",
-        "try:\n",
-        "    job.delete()\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the pipeline job resource\n",
+        "job.delete()\n",
         "\n",
         "# Delete Cloud Storage objects that were created\n",
         "delete_bucket = True\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "# Remove the local pipeline package file\n",
+        "! rm multicontender_vs_champion.json"
       ]
     }
   ],

notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb

@@ -29,27 +29,29 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# BQML and AutoML - Experimenting with Vertex AI\n",
+        "# BQML and AutoML - Rapid Prototyping with Vertex AI\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "    </a>\n",
-        "  </td>  \n",
-        "\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Frapid_prototyping_bqml_automl.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>  \n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>"
       ]
     },
@@ -61,10 +63,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BQML, do an evaluation comparison, for a baseline, before progressing to a custom model.\n",
-        "\n",
-        "\n",
-        "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />\n",
+        "This tutorial demonstrates how to use Vertex AI Pipelines to rapidly prototype a model using both AutoML and BQML, evaluate and compare them for a baseline model before progressing to a custom model.\n",
         "\n",
         "Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component) and [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component)."
       ]
@@ -72,19 +71,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "k-82uiXlTjvw"
+        "id": "c6fc06d85572"
       },
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.\n",
+        "In this tutorial, you learn how to use Vertex AI Pipelines for rapid prototyping a model.\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
+        "This tutorial uses the following Vertex AI services:\n",
         "\n",
-        "- `Vertex AI Pipelines`\n",
-        "- `Vertex AI AutoML`\n",
-        "- `Vertex AI BigQuery ML`\n",
-        "- `Google Cloud Pipeline Components`\n",
+        "- Vertex AI Pipelines\n",
+        "- Vertex AI AutoML\n",
+        "- Vertex AI BigQuery ML\n",
+        "- Google Cloud Pipeline Components\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -93,22 +92,23 @@
         "- Extracting evaluation metrics from the BigQueryML and AutoML models.\n",
         "- Selecting the best trained model.\n",
         "- Deploying the best trained model.\n",
-        "- Testing the deployed model infrastructure.\n",
-        "\n",
-        "\n",
+        "- Testing the deployed model infrastructure.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9e66e6624d86"
+      },
+      "source": [
         "## Dataset\n",
         "\n",
-        "#### The Abalone Dataset\n",
-        "\n",
-        "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/dataset.png\" />\n",
-        "\n",
         "<p>Dataset Credits</p>\n",
         "<p>Dua, D. and Graff, C. (2019). UCI Machine Learning Repository <a href=\"http://archive.ics.uci.edu/ml\">http://archive.ics.uci.edu/ml</a>. Irvine, CA: University of California, School of Information and Computer Science.</p>\n",
         "\n",
-        "<p><a href=\"https://archive.ics.uci.edu/ml/datasets/abalone\">Direct link</a></p>\n",
-        "    \n",
-        "    \n",
-        "#### Attribute Information:\n",
+        "<p>Learn more about the <a href=\"https://archive.ics.uci.edu/ml/datasets/abalone\">dataset</a>.</p>\n",
+        "\n",
+        "### Attribute Information\n",
         "\n",
         "<p>Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem.</p>\n",
         "\n",
@@ -175,9 +175,15 @@
         "            <td>+1.5 gives the age in years</td>\n",
         "\t\t</tr>\n",
         "\t</table>\n",
-        "</body>\n",
-        "\n",
-        "\n",
+        "</body>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k-82uiXlTjvw"
+      },
+      "source": [
         "## Costs \n",
         "\n",
         "\n",
@@ -194,6 +200,15 @@
         "to generate a cost estimate based on your projected usage.\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -202,7 +217,7 @@
       "source": [
         "### Set up your local development environment\n",
         "\n",
-        "If you are using Colab or Vertex AI Workbench, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
+        "If you're using Colab or Vertex AI Workbench, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
         "\n",
         "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
         "\n",
@@ -233,9 +248,7 @@
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -257,7 +270,9 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -268,11 +283,53 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c87a2a5d7e35"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5dccb1c8feb6"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -281,14 +338,9 @@
         "id": "before_you_begin:nogpu"
       },
       "source": [
-        "## Before you begin\n",
+        "### Set Google Cloud project information\n",
         "\n",
-        "### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -300,120 +352,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2dw8q9fdQEH5"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "57dad372c81b"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4e166d927e36"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -424,9 +363,7 @@
       "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets.\n",
-        "\n",
-        "- *{Note to notebook author: For any user-provided strings that need to be unique (like bucket names or model ID's), append \"-unique\" to the end so proper testing can occur}*"
+        "Create a storage bucket to store intermediate artifacts such as datasets."
       ]
     },
     {
@@ -446,7 +383,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -457,7 +394,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -519,7 +456,7 @@
       "source": [
         "#### Set service account access for Vertex AI Pipelines\n",
         "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run these once per service account."
       ]
     },
     {
@@ -541,7 +478,7 @@
         "id": "T7aBmVRZGr1d"
       },
       "source": [
-        "### Required imports"
+        "### Import the required libraries"
       ]
     },
     {
@@ -570,10 +507,25 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "63_AplznG3J7"
+        "id": "d239c38c8a38"
       },
       "source": [
-        "### Determine some project and pipeline variables"
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Before you initialize the Vertex AI SDK for Python, you must [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your Google Cloud project.\n",
+        "\n",
+        "Then, initialize Vertex AI using the location and bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "89b254433c2f"
+      },
+      "outputs": [],
+      "source": [
+        "vertex.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -582,11 +534,13 @@
         "id": "Dy-vIuq2yWjw"
       },
       "source": [
-        "Instructions:\n",
-        "- Make sure the GCS bucket and the BigQuery Dataset do not exist. This script may **delete** any existing content.\n",
+        "### Determine project and pipeline variables\n",
+        "\n",
+        "Instructions before you set the variables:\n",
+        "- Make sure that the GCS bucket and the BigQuery dataset don't exist. This notebook may **delete** any existing content.\n",
         "- Your bucket must be on the same region as your Vertex AI resources.\n",
-        "- BQ region can be US or EU;\n",
-        "- Make sure your preferred Vertex AI region is supported [[link]](https://cloud.google.com/vertex-ai/docs/general/locations#americas_1).\n"
+        "- BQ region can be US or EU.\n",
+        "- Make sure your preferred Vertex AI region(LOCATION) is supported. Check the [list of supported regions](https://cloud.google.com/vertex-ai/docs/general/locations#americas_1).\n"
       ]
     },
     {
@@ -602,7 +556,7 @@
         "DATA_FOLDER = f\"{BUCKET_URI[5:]}/data\"\n",
         "\n",
         "RAW_INPUT_DATA = f\"gs://{DATA_FOLDER}/abalone.csv\"\n",
-        "BQ_DATASET = \"vertex_ai_dev_dataset_\" + UUID  # @param {type:\"string\"}\n",
+        "BQ_DATASET = \"vertex_ai_dev_dataset_unique\"  # @param {type:\"string\"}\n",
         "BQ_LOCATION = \"US\"  # @param {type:\"string\"}\n",
         "BQ_LOCATION = BQ_LOCATION.upper()\n",
         "BQML_EXPORT_LOCATION = f\"{BUCKET_URI}/artifacts/bqml\"\n",
@@ -610,7 +564,7 @@
         "DISPLAY_NAME = \"rapid-prototyping\"\n",
         "ENDPOINT_DISPLAY_NAME = f\"{DISPLAY_NAME}_endpoint\"\n",
         "\n",
-        "image_prefix = REGION.split(\"-\")[0]\n",
+        "image_prefix = LOCATION.split(\"-\")[0]\n",
         "BQML_SERVING_CONTAINER_IMAGE_URI = (\n",
         "    f\"{image_prefix}-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest\"\n",
         ")"
@@ -624,8 +578,9 @@
       },
       "outputs": [],
       "source": [
+        "# Set Project Id and location\n",
         "!gcloud config set project $PROJECT_ID\n",
-        "!gcloud config set ai/region $REGION"
+        "!gcloud config set ai/region $LOCATION"
       ]
     },
     {
@@ -636,7 +591,7 @@
       "source": [
         "### Downloading the data\n",
         "\n",
-        "The cell below will download the dataset into a CSV file and save it in GCS"
+        "The cell below downloads the dataset into a CSV file and saves it in your Cloud Storage bucket."
       ]
     },
     {
@@ -656,7 +611,11 @@
         "id": "owlPQF1KF8QO"
       },
       "source": [
-        "## Pipeline Components"
+        "## Define the pipeline components\n",
+        "\n",
+        "Before you run the pipeline, define the individual components for your pipeline.\n",
+        "\n",
+        "**Note**: In this section, you define the custom components that aren't available in the Vertex AI SDK by default."
       ]
     },
     {
@@ -665,9 +624,9 @@
         "id": "79eaa73a"
       },
       "source": [
-        "### Import to BQ\n",
+        "### Import to BigQuery\n",
         "\n",
-        "This component takes the csv file and imports it to a table in BigQuery. If the dataset does not exist, it will be created. If a table with the same name already exists, it will be deleted and recreated"
+        "First, define a component that loads the csv file and imports it to a BigQuery table. If the dataset doesn't exist, it's created. If a table already exists with the same name, it's overwritten."
       ]
     },
     {
@@ -720,7 +679,7 @@
         "\n",
         "    def create_dataset_if_not_exist(bq_dataset_id, bq_location):\n",
         "        print(\n",
-        "            \"Checking for existence of bq dataset. If it does not exist, it creates one\"\n",
+        "            \"Checking for existence of bq dataset. If it doesn't exist, it creates one\"\n",
         "        )\n",
         "        dataset = bigquery.Dataset(bq_dataset_id)\n",
         "        dataset.location = bq_location\n",
@@ -734,7 +693,7 @@
         "    table_id = f\"{project}.{bq_dataset}.{raw_table_name}\"\n",
         "    print(\"Deleting any tables that might have the same name on the dataset\")\n",
         "    client.delete_table(table_id, not_found_ok=True)\n",
-        "    print(\"will load data to table\")\n",
+        "    print(\"Loading data to table...\")\n",
         "    load_dataset(gcs_data_uri, table_id)\n",
         "\n",
         "    raw_dataset_uri = f\"bq://{table_id}\"\n",
@@ -747,9 +706,9 @@
         "id": "637de8be"
       },
       "source": [
-        "## Split Datasets\n",
+        "### Split Datasets\n",
         "\n",
-        "Splits the dataset in 3 slices:\n",
+        "Now, define a component to split the dataset in 3 slices:\n",
         "- TRAIN\n",
         "- EVALUATE\n",
         "- TEST\n",
@@ -757,22 +716,19 @@
         "\n",
         "AutoML and BigQuery ML use different nomenclatures for data splits:\n",
         "\n",
-        "#### BQML\n",
-        "How BQML splits the data: [link](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-hyperparameter-tuning#data_split)\n",
+        "- **BQML**: Learn how [BQML splits the data](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-hyperparameter-tuning#data_split).\n",
         "\n",
-        "#### AutoML\n",
-        "How AutoML splits the data: [link](https://cloud.google.com/vertex-ai/docs/general/ml-use?hl=da&skip_cache=false)\n",
+        "- **AutoML**: Learn how [AutoML splits the data](https://cloud.google.com/vertex-ai/docs/general/ml-use?hl=da&skip_cache=false).\n",
         "\n",
-        "<ul>\n",
-        "    <li>Model trials\n",
-        "<p>The training set is used to train models with different preprocessing, architecture, and hyperparameter option combinations. These models are evaluated on the validation set for quality, which guides the exploration of additional option combinations. The best parameters and architectures determined in the parallel tuning phase are used to train two ensemble models as described below.</p></li>\n",
+        "**Model trials**\n",
+        "<p>The training set is used to train models with different preprocessing, architecture, and hyperparameter option combinations. These models are evaluated on the validation set for quality, which guides the exploration of additional option combinations. The best parameters and architectures determined in the parallel tuning phase are used to train two ensemble models as described in the further sections.</p>\n",
         "\n",
-        "<li>Model evaluation\n",
+        "**Model evaluation**\n",
         "<p>\n",
-        "Vertex AI trains an evaluation model, using the training and validation sets as training data. Vertex AI generates the final model evaluation metrics on this model, using the test set. This is the first time in the process that the test set is used. This approach ensures that the final evaluation metrics are an unbiased reflection of how well the final trained model will perform in production.</p></li>\n",
+        "Vertex AI trains an evaluation model, using the training and validation sets as training data. Vertex AI generates the final evaluation metrics on this model, using the test set. This is the first time in the process that the test set is used. This approach ensures that the final evaluation metrics are an unbiased reflection of how well the final trained model performs in production.</p></li>\n",
         "\n",
-        "<li>Serving model\n",
-        "<p>A model is trained with the training, validation, and test sets, to maximize the amount of training data. This model is the one that you use to request predictions.</p></li>\n"
+        "**Serving model**\n",
+        "<p>A model is trained with the training, validation, and test sets, to maximize the amount of training data. This model is the one that you use to request predictions.</p>\n"
       ]
     },
     {
@@ -893,13 +849,13 @@
         "id": "5e5d1785"
       },
       "source": [
-        "### Train BQML Model\n",
+        "### Train BQML model\n",
         "\n",
-        "For this demo, we use a simple linear regression model on BQML. However, you can be creative with other model architectures, such as Deep Neural Networks, XGboost, Logistic Regression, etc.\n",
+        "Define a component for creating the BQML model. For this demo, you use a simple linear regression model in BQML. However, you can be creative with other model architectures, such as Deep Neural Networks, XGboost, Logistic Regression, etc.\n",
         "\n",
-        "For a full list of models supported by BQML, look here: [End-to-end user journey for each model](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-e2e-journey).\n",
+        "For a full list of models supported by BQML, see [End-to-end user journey for each model](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-e2e-journey).\n",
         "\n",
-        "As pointed out before, BQML and AutoML use different split terminologies, so we do an adaptation of the <i>split_col</i> column directly on the SELECT portion of the CREATE model query:\n",
+        "As pointed out before, BQML and AutoML use different split terminologies. So, you make an adaptation of the <i>split_col</i> column directly on the SELECT portion of the CREATE model query:\n",
         "\n",
         "> When the value of DATA_SPLIT_METHOD is 'CUSTOM', the corresponding column should be of type BOOL. The rows with TRUE or NULL values are used as evaluation data. Rows with FALSE values are used as training data.\n"
       ]
@@ -963,13 +919,12 @@
         "id": "3332263db93e"
       },
       "source": [
-        "### Interpret BQML Model Evaluation\n",
+        "### Interpret BQML model evaluation\n",
         "\n",
-        "When you do Hyperparameter tuning on the model creation query, the output of the pre-built component [BigqueryEvaluateModelJobOp](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.0/google_cloud_pipeline_components.experimental.bigquery.html#google_cloud_pipeline_components.experimental.bigquery.BigqueryEvaluateModelJobOp) will be a table with the metrics obtained by BQML when training the model. In your BigQuery console, they look like the image below. We need to access them programmatically so we can compare them to the AutoML model. \n",
+        "When you do hyperparameter tuning with the model creation query, the output of the prebuilt component [BigqueryEvaluateModelJobOp](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.0/google_cloud_pipeline_components.experimental.bigquery.html#google_cloud_pipeline_components.experimental.bigquery.BigqueryEvaluateModelJobOp) is a table with the metrics obtained by BQML when training the model. To compare them with those obtained for AutoML model, you need to access them programmatically.\n",
         "\n",
-        "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/bqml-evaluate.png?\">\n",
         "\n",
-        "The cell below shows you an example of how this can be done. BQML does not give you a root mean squared error to the list of metrics, so we're manually adding it to the metrics dictionary. For more information aboyt the output, please check [BQML's documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output). "
+        "The cell below defines a pipeline component that helps you access the metrics. Note that BQML doesn't give you a root mean squared error in the list of metrics. So, you're manually adding it to the metrics dictionary. For more information about the output, see [BQML's documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output). "
       ]
     },
     {
@@ -1013,13 +968,11 @@
         "id": "e5bd1715e98a"
       },
       "source": [
-        "### Interpret AutoML Model Evaluation\n",
+        "### Interpret AutoML model evaluation\n",
         "\n",
-        "Similar to BQML, AutoML also generates metrics during its model creation. These can be accessed in the UI, as seen below:\n",
+        "Similar to BQML, AutoML also generates metrics during its model creation that can be accessed from the Google Cloud console.\n",
         "\n",
-        "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl-evaluate.png\" />\n",
-        "\n",
-        "Since we don't have a pre-built-component to access these metrics programmatically, we can use the Vertex AI GAPIC (Google API Compiler), which auto-generates low-level gRPC interfaces to the service.\n"
+        "Since there isn't a prebuilt component to access the AutoML metrics programmatically, you define the below component. The below code uses Vertex AI GAPIC (Google API Compiler) API which auto-generates low-level gRPC interfaces to the specified service.\n"
       ]
     },
     {
@@ -1085,11 +1038,11 @@
         "id": "7421c559"
       },
       "source": [
-        "### Model Selection\n",
+        "### Model selection\n",
         "\n",
-        "Now that we have evaluated the models independently, we are going to move forward with only one of them. This election will be done based on the model evaluation metrics gathered in the previous steps.\n",
+        "After the models are evaluated independently, you're going to only move forward with one of them. The selection is done based on the model evaluation metrics gathered in the previous steps. The selected model is then deployed to an endpoint.\n",
         "\n",
-        "Bear in mind that BQML and AutoML use different evaluation metric names, hence we had to do a mapping of these different nomenclatures."
+        "Define a component to select the best out of the two models that is suitable for deployment. Note that BQML and AutoML use different evaluation metric names, therefore you need to do a mapping of these different nomenclatures."
       ]
     },
     {
@@ -1140,13 +1093,13 @@
         "            metric_bqml = metrics_bqml.metadata[x]\n",
         "            print(f\"Metric bqml: {metric_bqml}\")\n",
         "        except:\n",
-        "            print(f\"{x} does not exist int the BQML dictionary\")\n",
+        "            print(f\"{x} doesn't exist int the BQML dictionary\")\n",
         "\n",
         "        try:\n",
         "            metric_automl = metrics_automl.metadata[x]\n",
         "            print(f\"Metric automl: {metric_automl}\")\n",
         "        except:\n",
-        "            print(f\"{x} does not exist on the AutoML dictionary\")\n",
+        "            print(f\"{x} doesn't exist on the AutoML dictionary\")\n",
         "\n",
         "    # Change condition if higher is better.\n",
         "    print(f\"Comparing BQML ({metric_bqml}) vs AutoML ({metric_automl})\")\n",
@@ -1195,9 +1148,9 @@
         "id": "0f573556"
       },
       "source": [
-        "### Validate Infrastructure\n",
+        "### Validate the infrastructure\n",
         "\n",
-        "Once the best model has been deployed, you validate the endpoint by making a simple prediction to it."
+        "Post selecting the best model, it's deployed to an endpoint. Define a component that validates the endpoint by making prediction requests to that endpoint."
       ]
     },
     {
@@ -1258,7 +1211,7 @@
         "        \"Shell_weight\": 0.055,\n",
         "    }\n",
         "    instance_json = json.dumps(instance)\n",
-        "    print(\"Will use the following instance: \" + instance_json)\n",
+        "    print(\"Using the following instance: \" + instance_json)\n",
         "\n",
         "    endpoint = aiplatform.Endpoint(treated_uri)\n",
         "    prediction = request_prediction(endpoint, instance)\n",
@@ -1275,28 +1228,9 @@
         "id": "hpB_bdDbGGOp"
       },
       "source": [
-        "## The Pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "O5PsR31ysGuj"
-      },
-      "outputs": [],
-      "source": [
-        "pipeline_params = {\n",
-        "    \"project\": PROJECT_ID,\n",
-        "    \"region\": REGION,\n",
-        "    \"gcs_input_file_uri\": RAW_INPUT_DATA,\n",
-        "    \"bq_dataset\": BQ_DATASET,\n",
-        "    \"bq_location\": BQ_LOCATION,\n",
-        "    \"bqml_model_export_location\": BQML_EXPORT_LOCATION,\n",
-        "    \"bqml_serving_container_image_uri\": BQML_SERVING_CONTAINER_IMAGE_URI,\n",
-        "    \"endpoint_display_name\": ENDPOINT_DISPLAY_NAME,\n",
-        "    \"thresholds_dict_str\": '{\"rmse\": 2.5}',\n",
-        "}"
+        "## Define the pipeline\n",
+        "\n",
+        "Now, define the flow of your pipeline using the prebuilt components and the custom components you defined above."
       ]
     },
     {
@@ -1341,13 +1275,13 @@
         "        training_data_uri=split_datasets_op.outputs[\"dataset_uri\"],\n",
         "    )\n",
         "\n",
-        "    # Builds BQML model using pre-built-component.\n",
+        "    # Builds BQML model using prebuilt component.\n",
         "    bqml_create_op = bq_components.BigqueryCreateModelJobOp(\n",
         "        project=project, location=bq_location, query=create_model_query\n",
         "    )\n",
         "    bqml_model = bqml_create_op.outputs[\"model\"]\n",
         "\n",
-        "    # Gathers BQML evaluation metrics using a pre-built-component.\n",
+        "    # Gathers BQML evaluation metrics using a prebuilt component.\n",
         "    bqml_evaluate_op = bq_components.BigqueryEvaluateModelJobOp(\n",
         "        project=project, location=bq_location, model=bqml_model\n",
         "    )\n",
@@ -1359,7 +1293,7 @@
         "    )\n",
         "    bqml_eval_metrics = interpret_bqml_evaluation_metrics_op.outputs[\"metrics\"]\n",
         "\n",
-        "    # Exports the BQML model to a GCS bucket using a pre-built-component.\n",
+        "    # Exports the BQML model to a GCS bucket using a prebuilt component.\n",
         "    bqml_export_op = bq_components.BigqueryExportModelJobOp(\n",
         "        project=project,\n",
         "        location=bq_location,\n",
@@ -1378,7 +1312,7 @@
         "        },\n",
         "    )\n",
         "\n",
-        "    # Uploads the recently exported the BQML model from GCS into Vertex AI using a pre-built-component.\n",
+        "    # Uploads the recently exported the BQML model from GCS into Vertex AI using a prebuilt component.\n",
         "    bqml_model_upload_op = ModelUploadOp(\n",
         "        project=project,\n",
         "        location=region,\n",
@@ -1387,7 +1321,7 @@
         "    )\n",
         "    bqml_vertex_model = bqml_model_upload_op.outputs[\"model\"]\n",
         "\n",
-        "    # Creates a Vertex AI Tabular dataset using a pre-built-component.\n",
+        "    # Creates a Vertex AI Tabular dataset using a prebuilt component.\n",
         "    dataset_create_op = TabularDatasetCreateOp(\n",
         "        project=project,\n",
         "        location=region,\n",
@@ -1395,7 +1329,7 @@
         "        bq_source=split_datasets_op.outputs[\"dataset_bq_uri\"],\n",
         "    )\n",
         "\n",
-        "    # Trains an AutoML Tables model using a pre-built-component.\n",
+        "    # Trains an AutoML Tables model using a prebuilt component.\n",
         "    automl_training_op = AutoMLTabularTrainingJobRunOp(\n",
         "        project=project,\n",
         "        location=region,\n",
@@ -1405,6 +1339,7 @@
         "        predefined_split_column_name=\"split_col\",\n",
         "        dataset=dataset_create_op.outputs[\"dataset\"],\n",
         "        target_column=\"Rings\",\n",
+        "        budget_milli_node_hours=1000,\n",
         "        column_transformations=[\n",
         "            {\"categorical\": {\"column_name\": \"Sex\"}},\n",
         "            {\"numeric\": {\"column_name\": \"Length\"}},\n",
@@ -1434,11 +1369,11 @@
         "    )\n",
         "\n",
         "    # If the deploy condition is True, then deploy the best model.\n",
-        "    with dsl.Condition(\n",
+        "    with dsl.If(\n",
         "        best_model_task.outputs[\"deploy_decision\"] == \"true\",\n",
         "        name=\"deploy_decision\",\n",
         "    ):\n",
-        "        # Creates a Vertex AI endpoint using a pre-built-component.\n",
+        "        # Creates a Vertex AI endpoint using a prebuilt component.\n",
         "        endpoint_create_op = EndpointCreateOp(\n",
         "            project=project,\n",
         "            location=region,\n",
@@ -1447,11 +1382,11 @@
         "        endpoint_create_op.after(best_model_task)\n",
         "\n",
         "        # In case the BQML model is the best...\n",
-        "        with dsl.Condition(\n",
+        "        with dsl.If(\n",
         "            best_model_task.outputs[\"best_model\"] == \"bqml\",\n",
         "            name=\"deploy_bqml\",\n",
         "        ):\n",
-        "            # Deploys the BQML model (now on Vertex AI) to the recently created endpoint using a pre-built component.\n",
+        "            # Deploys the BQML model (now on Vertex AI) to the recently created endpoint using a prebuilt component.\n",
         "            model_deploy_bqml_op = ModelDeployOp(  # noqa: F841\n",
         "                endpoint=endpoint_create_op.outputs[\"endpoint\"],\n",
         "                model=bqml_vertex_model,\n",
@@ -1470,11 +1405,11 @@
         "            ).set_caching_options(False).after(model_deploy_bqml_op)\n",
         "\n",
         "        # In case the AutoML model is the best...\n",
-        "        with dsl.Condition(\n",
+        "        with dsl.If(\n",
         "            best_model_task.outputs[\"best_model\"] == \"automl\",\n",
         "            name=\"deploy_automl\",\n",
         "        ):\n",
-        "            # Deploys the AutoML model to the recently created endpoint using a pre-built component.\n",
+        "            # Deploys the AutoML model to the recently created endpoint using a prebuilt component.\n",
         "            model_deploy_automl_op = ModelDeployOp(  # noqa: F841\n",
         "                endpoint=endpoint_create_op.outputs[\"endpoint\"],\n",
         "                model=automl_model,\n",
@@ -1493,13 +1428,69 @@
         "            ).set_caching_options(False).after(model_deploy_automl_op)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1b01f57507f7"
+      },
+      "source": [
+        "## Compile the pipeline\n",
+        "\n",
+        "Compile and save your pipeline to a local file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d69a2f630792"
+      },
+      "outputs": [],
+      "source": [
+        "compiler.Compiler().compile(\n",
+        "    pipeline_func=train_pipeline,\n",
+        "    package_path=PIPELINE_YAML_PKG_PATH,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8b6af4f85f1"
+      },
+      "source": [
+        "## Specify the pipeline parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "O5PsR31ysGuj"
+      },
+      "outputs": [],
+      "source": [
+        "# Specify the input parameters to your pipeline\n",
+        "pipeline_params = {\n",
+        "    \"project\": PROJECT_ID,\n",
+        "    \"region\": LOCATION,\n",
+        "    \"gcs_input_file_uri\": RAW_INPUT_DATA,\n",
+        "    \"bq_dataset\": BQ_DATASET,\n",
+        "    \"bq_location\": BQ_LOCATION,\n",
+        "    \"bqml_model_export_location\": BQML_EXPORT_LOCATION,\n",
+        "    \"bqml_serving_container_image_uri\": BQML_SERVING_CONTAINER_IMAGE_URI,\n",
+        "    \"endpoint_display_name\": ENDPOINT_DISPLAY_NAME,\n",
+        "    \"thresholds_dict_str\": '{\"rmse\": 2.5}',\n",
+        "}"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "qxfy-pXXGS3R"
       },
       "source": [
-        "### Running the Pipeline"
+        "## Run the pipeline"
       ]
     },
     {
@@ -1510,14 +1501,7 @@
       },
       "outputs": [],
       "source": [
-        "compiler.Compiler().compile(\n",
-        "    pipeline_func=train_pipeline,\n",
-        "    package_path=PIPELINE_YAML_PKG_PATH,\n",
-        ")\n",
-        "\n",
-        "\n",
-        "vertex.init(project=PROJECT_ID, location=REGION)\n",
-        "\n",
+        "# Create a pipeline job\n",
         "pipeline_job = vertex.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
         "    template_path=PIPELINE_YAML_PKG_PATH,\n",
@@ -1525,7 +1509,7 @@
         "    parameter_values=pipeline_params,\n",
         "    enable_caching=False,\n",
         ")\n",
-        "\n",
+        "# Submit your pipeline job\n",
         "response = pipeline_job.submit()"
       ]
     },
@@ -1535,11 +1519,11 @@
         "id": "f49899d5e838"
       },
       "source": [
-        "#### Wait for the pipeline to complete\n",
+        "### Wait for the pipeline to complete\n",
         "\n",
-        "Currently, your pipeline is running asynchronous by using the `submit()` method. To have run it synchronously, you would have invoked the `run()` method.\n",
+        "When you use the `submit()` method, your pipeline runs in asynchronous mode.  To block the execution until your job gets completed, use the `wait()` method.\n",
         "\n",
-        "In this last step, you block on the asynchronously executed waiting for completion using the `wait()` method."
+        "**Note**: To run your pipeline synchronously, you can use the `run()` method."
       ]
     },
     {
@@ -1575,13 +1559,8 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "vertex.init(project=PROJECT_ID, location=REGION)\n",
-        "\n",
-        "delete_bucket = False\n",
-        "\n",
-        "print(\"Will delete endpoint\")\n",
+        "# Delete Vertex AI endpoint\n",
+        "print(\"Deleting endpoint...\")\n",
         "endpoints = vertex.Endpoint.list(\n",
         "    filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
         ")\n",
@@ -1590,12 +1569,13 @@
         "vertex.Endpoint.delete(endpoint)\n",
         "print(\"Deleted endpoint:\", endpoint)\n",
         "\n",
-        "print(\"Will delete models\")\n",
+        "# Delete BQML and AutoML models\n",
+        "print(\"Deleting models...\")\n",
         "suffix_list = [\"bqml\", \"automl\"]\n",
         "for suffix in suffix_list:\n",
         "    try:\n",
         "        model_display_name = f\"{DISPLAY_NAME}_{suffix}\"\n",
-        "        print(\"Will delete model with name \" + model_display_name)\n",
+        "        print(\"Deleting model with name: \" + model_display_name)\n",
         "        models = vertex.Model.list(\n",
         "            filter=f\"display_name={model_display_name}\", order_by=\"create_time\"\n",
         "        )\n",
@@ -1606,35 +1586,35 @@
         "    except Exception as e:\n",
         "        print(e)\n",
         "\n",
-        "\n",
-        "print(\"Will delete Vertex dataset\")\n",
+        "# Delete Vertex AI dataset\n",
+        "print(\"Deleting Vertex AI dataset...\")\n",
         "datasets = vertex.TabularDataset.list(\n",
         "    filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
         ")\n",
-        "\n",
         "dataset = datasets[0]\n",
         "vertex.TabularDataset.delete(dataset)\n",
-        "print(\"Deleted Vertex dataset:\", dataset)\n",
+        "print(\"Deleted Vertex AI dataset:\", dataset)\n",
         "\n",
+        "# Delete Vertex AI pipline job\n",
+        "print(\"Deleting pipeline...\")\n",
+        "pipeline_job.delete()\n",
+        "print(\"Deleted pipeline:\", pipeline_job)\n",
         "\n",
-        "pipelines = vertex.PipelineJob.list(\n",
-        "    filter=f\"pipeline_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        ")\n",
-        "pipeline = pipelines[0]\n",
-        "vertex.PipelineJob.delete(pipeline)\n",
-        "print(\"Deleted pipeline:\", pipeline)\n",
-        "\n",
+        "# Delete BigQuery dataset\n",
         "delete_dataset = True\n",
-        "\n",
-        "# delete dataset\n",
-        "if delete_dataset or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_dataset:\n",
         "    ! bq rm -r -f -d $PROJECT_ID:$BQ_DATASET\n",
         "\n",
         "dataset_id = f\"{PROJECT_ID}.{BQ_DATASET}\"\n",
         "print(f\"Deleted BQ dataset '{dataset_id}' from location {BQ_LOCATION}.\")\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "# Delete Cloud Storage bucket\n",
+        "delete_bucket = True\n",
+        "if delete_bucket:\n",
+        "    ! gsutil rm -r $BUCKET_URI\n",
+        "\n",
+        "# Delete the pipeline package file\n",
+        "! rm PIPELINE_YAML_PKG_PATH"
       ]
     }
   ],

notebooks/official/prediction/sdk_private_endpoint.ipynb

@@ -23,6 +23,17 @@
         "# limitations under the License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ab0c6268f3a5"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ This notebook has been deprecated. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb

@@ -33,23 +33,28 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Freduction_server%2Fpytorch_distributed_training_reduction_server.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>  \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>                                                                                             \n",
         "</table>"
       ]
     },
@@ -124,221 +129,121 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_aip"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "1fd00fa70a2a"
+        "id": "6406a27bfea8"
       },
       "outputs": [],
       "source": [
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform "
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel.\n"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "bzPxhxS5lugp"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "d2qpIurSjmpT"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "project_id"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "wsePm9c4jmpT"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "294fe4e5a671"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a54f9d7c1876"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3aaadaaf9b30"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R7eelnCv6EWn"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dHsjsyb76HaN"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specified length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5c0404984792"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "x2n5SeAAjmpU"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6FDh38swjmpU"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nt8cEM2GjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XUSL_JcpjmpU"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_2zemfGvjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TCPJ38n7jmpU"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -375,7 +280,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -386,27 +291,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "v3sbyPBU75CR"
-      },
-      "source": [
-        "### Import libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "x8qQuI1377Jr"
-      },
-      "outputs": [],
-      "source": [
-        "from google.cloud import aiplatform"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -428,7 +313,40 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R7eelnCv6EWn"
+      },
+      "source": [
+        "#### UUID\n",
+        "\n",
+        "If you're in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dHsjsyb76HaN"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import string\n",
+        "\n",
+        "\n",
+        "# Generate a uuid of a specified length(default=8)\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -437,9 +355,9 @@
         "id": "tutorial_start:custom"
       },
       "source": [
-        "# Tutorial\n",
+        "## Tutorial\n",
         "\n",
-        "Now you are ready to start creating the PyTorch distributed training job."
+        "Now you're ready to start creating the PyTorch distributed training job."
       ]
     },
     {
@@ -664,7 +582,7 @@
         "  padding = \"max_length\"\n",
         "  max_seq_length = 128\n",
         "\n",
-        "  datasets = load_dataset(\"imdb\")\n",
+        "  datasets = load_dataset(\"imdb\", verification_mode='no_checks')\n",
         "  label_list = datasets[\"train\"].unique(\"label\")\n",
         "  label_to_id = {1: 1, 0: 0, -1: 0}\n",
         "\n",

notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb

@@ -32,25 +32,27 @@
         "# Train a Prophet Model using Vertex AI Tabular Workflows\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fprophet_on_vertex_pipelines.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>\n"
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -139,15 +141,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "install_aip"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "### Install Vertex AI SDK for Python and other required packages "
       ]
     },
     {
@@ -159,68 +168,88 @@
       "outputs": [],
       "source": [
         "! (pip3 install --upgrade --quiet \\\n",
-        "    google-cloud-aiplatform==1.40.0 \\\n",
-        "    google-cloud-bigquery[pandas]==3.17.1 \\\n",
-        "    google-cloud-pipeline-components==2.9.0)"
+        "    google-cloud-aiplatform \\\n",
+        "    google-cloud-bigquery[pandas] \\\n",
+        "    google-cloud-pipeline-components)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel.\n"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "bzPxhxS5lugp"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "d2qpIurSjmpT"
+        "id": "4a2b7b59bbf7"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "project_id"
+        "id": "f82e28c631cc"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91842ef41bbd"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -232,121 +261,8 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a54f9d7c1876"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}\n",
-        "DATA_REGION = \"us\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "84Vdv7R-QEH6"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5c0404984792"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nt8cEM2GjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XUSL_JcpjmpU"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_2zemfGvjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TCPJ38n7jmpU"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}\n",
+        "DATA_LOCATION = \"us\""
       ]
     },
     {
@@ -374,7 +290,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = \"gs://your-bucket-name-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -394,7 +310,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -517,7 +433,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -537,7 +453,7 @@
       "source": [
         "### Location of BigQuery destination table.\n",
         "\n",
-        "#### Create two datasets, one for each model you train. To make things simpler, create the datasets in the same region as the training data."
+        "Create two datasets, one for each model you train. To make things simpler, create the datasets in the same region as the training data."
       ]
     },
     {
@@ -548,16 +464,16 @@
       },
       "outputs": [],
       "source": [
-        "dataset_name = f\"forecasting_demo_prophet_{UUID}\"\n",
+        "dataset_name = \"forecasting_demo_prophet_unique\"\n",
         "\n",
         "dataset_path = \".\".join([PROJECT_ID, dataset_name])\n",
         "\n",
         "# Must be same region as TRAINING_DATASET_BQ_PATH.\n",
         "client = bigquery.Client(project=PROJECT_ID)\n",
         "bq_dataset = bigquery.Dataset(dataset_path)\n",
-        "bq_dataset.location = DATA_REGION\n",
+        "bq_dataset.location = DATA_LOCATION\n",
         "bq_dataset = client.create_dataset(bq_dataset)\n",
-        "print(f\"Created bigquery dataset {dataset_path} in {DATA_REGION}\")"
+        "print(f\"Created bigquery dataset {dataset_path} in {DATA_LOCATION}\")"
       ]
     },
     {
@@ -726,7 +642,7 @@
         "id": "ySuRn2i0rjLH"
       },
       "source": [
-        "You can take a look at the sales data that was generated. Later in this tutorial, we visualize the time series along with our forecast.\n",
+        "You can take a look at the sales data that was generated. Later in this tutorial, visualize the time series along with the forecast.\n",
         "\n",
         "The model is trained with data from January 2017 to October 2019 inclusive.\n",
         "\n",
@@ -839,7 +755,7 @@
         "    train_parameter_values,\n",
         ") = utils.get_prophet_train_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    root_dir=os.path.join(BUCKET_URI, \"pipeline_root\"),\n",
         "    time_column=time_column,\n",
         "    time_series_identifier_column=time_series_identifier_column,\n",
@@ -869,7 +785,7 @@
         "\n",
         "Use the Vertex AI Python SDK to kick off a training pipeline run. Once the run has started, the following cell outputs a link that allows you to monitor the run. The link should look like this:\n",
         "\n",
-        "`https://console.cloud.google.com/vertex-ai/locations/[REGION]/pipelines/runs/[DISPLAY_NAME]`"
+        "`https://console.cloud.google.com/vertex-ai/locations/[LOCATION]/pipelines/runs/[DISPLAY_NAME]`"
       ]
     },
     {
@@ -881,7 +797,9 @@
       "outputs": [],
       "source": [
         "# The display name should be unique even if this cell is rerun.\n",
-        "DISPLAY_NAME = f\"forecasting-demo-train-{generate_uuid()}\"\n",
+        "DISPLAY_NAME = \"forecasting-demo-train-unique\"\n",
+        "\n",
+        "training_pipeline_job_name = DISPLAY_NAME\n",
         "\n",
         "job = aiplatform.PipelineJob(\n",
         "    job_id=DISPLAY_NAME,\n",
@@ -903,7 +821,7 @@
         "If you ever want to reuse an existing run, the above command can be replaced with:\n",
         "\n",
         "```\n",
-        "job = aiplatform.PipelineJob.get('projects/[PROJECT_NUMBER]/locations/[REGION]/pipelineJobs/[PIPELINE_RUN_NAME]')\n",
+        "job = aiplatform.PipelineJob.get('projects/[PROJECT_ID]/locations/[LOCATION]/pipelineJobs/[PIPELINE_RUN_NAME]')\n",
         "\n",
         "```"
       ]
@@ -993,7 +911,7 @@
         "    prediction_parameter_values,\n",
         ") = utils.get_prophet_prediction_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    model_name=model,\n",
         "    time_column=time_column,\n",
         "    time_series_identifier_column=time_series_identifier_column,\n",
@@ -1015,7 +933,7 @@
         "\n",
         "Use the Vertex AI Python SDK to kick off a prediction pipeline run. Once the run has started, the following cell outputs a link that allows you to monitor the run. The link should look like this:\n",
         "\n",
-        "`https://console.cloud.google.com/vertex-ai/locations/[REGION]/pipelines/runs/[DISPLAY_NAME]`"
+        "`https://console.cloud.google.com/vertex-ai/locations/[LOCATION]/pipelines/runs/[DISPLAY_NAME]`"
       ]
     },
     {
@@ -1027,7 +945,9 @@
       "outputs": [],
       "source": [
         "# The display name should be unique even if this cell is rerun.\n",
-        "DISPLAY_NAME = f\"forecasting-demo-predict-{generate_uuid()}\"\n",
+        "DISPLAY_NAME = \"forecasting-demo-predict-unique\"\n",
+        "\n",
+        "prediction_pipeline_job_name = DISPLAY_NAME\n",
         "\n",
         "job = aiplatform.PipelineJob(\n",
         "    job_id=DISPLAY_NAME,\n",
@@ -1192,6 +1112,8 @@
         "\n",
         "Otherwise, you can delete the individual resources you created in this tutorial:\n",
         "\n",
+        "- Pipeline jobs\n",
+        "- Batch prediction job\n",
         "- Model\n",
         "- Cloud Storage Bucket\n",
         "- BigQuery tables"
@@ -1205,14 +1127,44 @@
       },
       "outputs": [],
       "source": [
+        "# Get the training pipeline object\n",
+        "training_pipeline_job = aiplatform.PipelineJob.get(\n",
+        "    f\"projects/{PROJECT_ID}/locations/{LOCATION}/pipelineJobs/{training_pipeline_job_name}\"\n",
+        ")\n",
+        "\n",
+        "# Delete the training pipeline\n",
+        "training_pipeline_job.delete()\n",
+        "\n",
+        "# Get the prediction pipeline object\n",
+        "prediction_pipeline_job = aiplatform.PipelineJob.get(\n",
+        "    f\"projects/{PROJECT_ID}/locations/{LOCATION}/pipelineJobs/{prediction_pipeline_job_name}\"\n",
+        ")\n",
+        "\n",
+        "# Delete the training pipeline\n",
+        "prediction_pipeline_job.delete()\n",
+        "\n",
+        "model = aiplatform.Model(model)\n",
+        "\n",
+        "# List batch prediction jobs linked to the model\n",
+        "batch_jobs = aiplatform.BatchPredictionJob.list(\n",
+        "    filter=f\"model={model.resource_name}\", order_by=\"create_time\"\n",
+        ")\n",
+        "\n",
+        "# Delete batch prediction job\n",
+        "if batch_jobs:\n",
+        "    batch_job = batch_jobs[0]\n",
+        "    batch_job.delete()\n",
+        "    print(\"Deleted batch prediction job:\", batch_job)\n",
+        "\n",
         "# Delete the model\n",
-        "aiplatform.Model(model).delete()\n",
+        "model.delete()\n",
         "\n",
         "# Delete output datasets\n",
-        "client.delete_dataset(dataset_path, delete_contents=True, not_found_ok=True)\n",
+        "! bq rm -f PROJECT_ID.dataset_name\n",
         "\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb

@@ -32,25 +32,27 @@
         "# Tabular Workflows: TabNet Pipeline\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Ftabnet_on_vertex_pipelines.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>\n"
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
       ]
     },
     {
@@ -138,12 +140,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "2b9e4bcab250"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get Started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "be898f74332d"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages"
       ]
     },
     {
@@ -161,60 +170,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel.\n"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "bzPxhxS5lugp"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "d2qpIurSjmpT"
+        "id": "b96b39fd4d7b"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "project_id"
+        "id": "ff666ce4051c"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b02382a1fea6"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -226,89 +255,7 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a54f9d7c1876"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5c0404984792"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nt8cEM2GjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XUSL_JcpjmpU"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_2zemfGvjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TCPJ38n7jmpU"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -356,7 +303,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -378,7 +325,7 @@
       "source": [
         "#### Service Account\n",
         "\n",
-        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+        "You use a service account to create Vertex AI Pipeline jobs. If you don't want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
       ]
     },
     {
@@ -489,7 +436,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -532,7 +479,7 @@
         "    task = get_task_detail(task_details, \"model-upload\")\n",
         "    # in format https://<location>-aiplatform.googleapis.com/v1/projects/<project_number>/locations/<location>/models/<model_id>\n",
         "    model_id = task.outputs[\"model\"].artifacts[0].uri.split(\"/\")[-1]\n",
-        "    return f\"https://console.cloud.google.com/vertex-ai/locations/{REGION}/models/{model_id}?project={PROJECT_ID}\"\n",
+        "    return f\"https://console.cloud.google.com/vertex-ai/locations/{LOCATION}/models/{model_id}?project={PROJECT_ID}\"\n",
         "\n",
         "\n",
         "# Get the bucket name and path.\n",
@@ -641,6 +588,7 @@
         "  * Full automatic transformations: FTE automatically configures a set of built-in transformations for each input column based on its data statistics. This can be set via `tf_auto_transform_features` in the training pipeline.\n",
         "  * Fully specified transformations: All transformations on input columns are explicitly specified with FTE's built-in transformations. Chaining of multiple transformations on a single column is also supported. These transformations can be saved to JSON configuration file and specified via `tf_transformations_path` argument of the training pipeline.\n",
         "  * Custom transformations: Custom, bring-your-own transform function, where you can define and import your own transform function and use it with other FTE's built-in transformations. You can specify custom transformations as an array of JSON object and pass through the `tf_custom_transformation_definitions` argument of the training pipeline.\n",
+        "  \n",
         "\n",
         "* **BigQuery-based dataset-level transformations**:\n",
         "  * Fully specified transformations: All transformations on input columns are explicitly specified with FTE's built-in transformations. These transformations can be specified as an array of JSON objects via `dataset_level_transformations` argument of the training pipeline.\n",
@@ -870,7 +818,7 @@
         "    parameter_values,\n",
         ") = automl_tabular_utils.get_tabnet_trainer_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    root_dir=pipeline_job_root_dir,\n",
         "    max_steps=max_steps,\n",
         "    max_train_secs=max_train_secs,\n",
@@ -944,7 +892,7 @@
       "source": [
         "## Customize TabNet HyperparameterTuningJob configuration and create pipeline\n",
         "\n",
-        "To get the best set of hyperparameters on your dataset, it is recommended to run a HyperparameterTuningJob.\n",
+        "To get the best set of hyperparameters on your dataset, it's recommended that you run a HyperparameterTuningJob.\n",
         "\n",
         "Hyperparameters that can be tuned are set with the optional `study_spec_parameters_override` parameter. You provide a helper function named `get_tabnet_study_spec_parameters_override` to get these hyperparameters. To this helper function, you provide:\n",
         "\n",
@@ -1025,7 +973,7 @@
         "    parameter_values,\n",
         ") = automl_tabular_utils.get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    root_dir=pipeline_job_root_dir,\n",
         "    target_column=target_column,\n",
         "    prediction_type=prediction_type,\n",
@@ -1137,9 +1085,9 @@
         "custom_job_model.delete()\n",
         "hpt_job_model.delete()\n",
         "\n",
-        "# Delete bucket\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False  # Set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb

@@ -32,23 +32,28 @@
         "# Tabular Workflows: Wide & Deep Pipeline\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fwide_and_deep_on_vertex_pipelines.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br>\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br>\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
         "</table>\n",
         "<br/><br/><br/>"
       ]
@@ -76,7 +81,7 @@
         "\n",
         "In this tutorial, you learn how to create two classification models using Vertex AI Wide & Deep Tabular Workflows. Each workflow is a managed instance of [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Vertex AI services and resources:\n",
         "\n",
         "- Vertex AI Training\n",
         "- Vertex Pipelines\n",
@@ -125,177 +130,126 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "f0316df526f8"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "2b4ef9b72d43"
+        "id": "954112d46a6b"
       },
       "outputs": [],
       "source": [
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 \"google-cloud-pipeline-components<2.0\""
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform \"google-cloud-pipeline-components<2.0\""
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel.\n"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "bzPxhxS5lugp"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "d2qpIurSjmpT"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "project_id"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "wsePm9c4jmpT"
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b49704f4eeca"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a54f9d7c1876"
-      },
-      "source": [
-        "#### Region\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5c0404984792"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
+        "from google.cloud import aiplatform\n",
         "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nt8cEM2GjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XUSL_JcpjmpU"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_2zemfGvjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TCPJ38n7jmpU"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -323,7 +277,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = \"gs://your-bucket-name-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -332,7 +286,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -343,7 +297,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -379,33 +333,11 @@
         "import uuid\n",
         "from typing import Any, Dict, List\n",
         "\n",
-        "from google.cloud import aiplatform, storage\n",
+        "from google.cloud import storage\n",
         "from google_cloud_pipeline_components.experimental.automl.tabular import \\\n",
         "    utils as automl_tabular_utils"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c0423f260423"
-      },
-      "source": [
-        "## Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ad69f2590268"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -442,7 +374,7 @@
         "    task = get_task_detail(task_details, \"model-upload\")\n",
         "    # in format https://<location>-aiplatform.googleapis.com/v1/projects/<project_number>/locations/<location>/models/<model_id>\n",
         "    model_id = task.outputs[\"model\"].artifacts[0].uri.split(\"/\")[-1]\n",
-        "    return f\"https://console.cloud.google.com/vertex-ai/locations/{REGION}/models/{model_id}?project={PROJECT_ID}\"\n",
+        "    return f\"https://console.cloud.google.com/vertex-ai/locations/{LOCATION}/models/{model_id}?project={PROJECT_ID}\"\n",
         "\n",
         "\n",
         "def get_bucket_name_and_path(uri: str) -> str:\n",
@@ -514,7 +446,7 @@
         "- `data_source_csv_filenames`: The CSV data source.\n",
         "- `data_source_bigquery_table_path`: The BigQuery data source.\n",
         "\n",
-        "***Notes***: Please note that the dataset's location has to be the same as the same as the service location (i.e., `REGION`) set for launching the training pipeline.\n"
+        "***Notes***: Please note that the dataset's location has to be the same as the same as the service location (i.e., `LOCATION`) set for launching the training pipeline.\n"
       ]
     },
     {
@@ -775,7 +707,7 @@
         "    parameter_values,\n",
         ") = automl_tabular_utils.get_wide_and_deep_trainer_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    root_dir=pipeline_job_root_dir,\n",
         "    max_steps=max_steps,\n",
         "    max_train_secs=max_train_secs,\n",
@@ -918,7 +850,7 @@
         "    parameter_values,\n",
         ") = automl_tabular_utils.get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters(\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    root_dir=pipeline_job_root_dir,\n",
         "    target_column=target_column,\n",
         "    prediction_type=prediction_type,\n",

notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb

@@ -32,21 +32,24 @@
         "# Vertex AI TensorBoard custom training with prebuilt container\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Ftensorboard%2Ftensorboard_custom_training_with_prebuilt_container.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td> \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>\n",
@@ -106,7 +109,7 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI Training\n",
+        "- Vertex AI training\n",
         "- Vertex AI TensorBoard\n",
         "\n",
         "The steps performed include:\n",
@@ -147,12 +150,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "s3moH5AexXpk"
+        "id": "f0316df526f8"
       },
       "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a2c2cb2109a0"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -169,60 +179,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel.\n"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "bzPxhxS5lugp"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "d2qpIurSjmpT"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager).\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "project_id"
+        "id": "92e68cfc3a90"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f872cd812d0"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -236,87 +266,9 @@
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a54f9d7c1876"
-      },
-      "source": [
-        "#### Region\n",
+        "! gcloud config set project {PROJECT_ID}\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3aaadaaf9b30"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5c0404984792"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Nt8cEM2GjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XUSL_JcpjmpU"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_2zemfGvjmpU"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TCPJ38n7jmpU"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -364,7 +316,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -382,7 +334,7 @@
         "id": "7qXFUiHLoFRw"
       },
       "source": [
-        "A service account is used to create custom training job. If you do not want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the [instruction](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
+        "A service account is used to create custom training job. If you don't want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the instructions in [Create a service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
       ]
     },
     {
@@ -493,7 +445,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -503,7 +455,7 @@
       },
       "source": [
         "## Write your training code\n",
-        "Your training code must be configured to write TensorBoard logs to the Cloud Storage bucket, the location of which the Vertex AI Training service will automatically make available via a predefined environment variable `AIP_TENSORBOARD_LOG_DIR`.\n",
+        "Your training code must be configured to write TensorBoard logs to the Cloud Storage bucket, the location of which the Vertex AI training service automatically make available via a predefined environment variable `AIP_TENSORBOARD_LOG_DIR`.\n",
         "\n",
         "This can usually be done by providing `os.environ['AIP_TENSORBOARD_LOG_DIR']` as the log directory to the open source TensorBoard log writing APIs.\n",
         "\n",
@@ -514,9 +466,9 @@
         "  histogram_freq=1)\n",
         "```\n",
         "\n",
-        "`AIP_TENSORBOARD_LOG_DIR` will be in the `BASE_OUTPUT_DIR` that you provided below when creating the custom training job.\n",
+        "`AIP_TENSORBOARD_LOG_DIR` is in the `BASE_OUTPUT_DIR` that you provided below when creating the custom training job.\n",
         "\n",
-        "We'll use the following sample code as an example:"
+        "We use the following sample code as an example:"
       ]
     },
     {
@@ -751,7 +703,7 @@
         "    TENSORBOARD_NAME = PROJECT_ID + \"-tb\"\n",
         "\n",
         "tensorboard = aiplatform.Tensorboard.create(\n",
-        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=REGION\n",
+        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "TENSORBOARD_RESOURCE_NAME = tensorboard.gca_resource.name\n",
         "print(\"TensorBoard resource name:\", TENSORBOARD_RESOURCE_NAME)"
@@ -784,7 +736,7 @@
         "    container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest\",\n",
         "    python_package_gcs_uri=f\"{GCS_BUCKET_TRAINING}hello-custom-training-3.0.tar.gz\",\n",
         "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
+        "    location=LOCATION,\n",
         "    staging_bucket=BASE_OUTPUT_DIR,\n",
         ")\n",
         "\n",
@@ -826,14 +778,18 @@
       },
       "outputs": [],
       "source": [
-        "# Delete GCS bucket.\n",
-        "! gsutil -m rm -r {BUCKET_URI}\n",
-        "\n",
         "# Delete TensorBoard instance.\n",
         "! gcloud ai tensorboards delete {TENSORBOARD_RESOURCE_NAME}\n",
         "\n",
         "# Delete custom job.\n",
-        "job.delete()"
+        "job.delete()\n",
+        "\n",
+        "# Delete GCS bucket.\n",
+        "delete_bucket = False\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "!rm -rf ../hello-custom-sample/"
       ]
     }
   ],

notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb

@@ -32,43 +32,29 @@
         "# Vertex AI TensorBoard hyperparameter tuning with the HParams Dashboard\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> <br> Open in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftensorboard%2Ftensorboard_hyperparameter_tuning_with_hparams.ipynb\">\n",
-        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"> <br> Open in Colab Enterprise\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"> <br>\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td style=\"text-align: center\">\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> <br>\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
         "</table>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "24743cf4a1e1"
-      },
-      "source": [
-        "**_NOTE_**: This notebook has been tested in the following environments:\n",
-        "\n",
-        "* Python version = 3.8"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -77,36 +63,11 @@
       "source": [
         "## Overview\n",
         "\n",
-        "### What is Vertex AI TensorBoard\n",
+        "In this tutorial, you learn how to log hyperparameter experiment results in TensorFlow and visualize the results in TensorBoard's Hparams dashboard.\n",
         "\n",
-        "Vertex AI TensorBoard is an enterprise-ready managed\n",
-        "version of [Open source TensorBoard](https://www.tensorflow.org/tensorboard/get_started)\n",
-        "(TB), which is a Google open source project for machine learning experiment\n",
-        "visualization.\n",
+        "**_NOTE_**: This notebook is tested in the following environments:\n",
         "\n",
-        "Vertex AI TensorBoard provides various detailed visualizations, including the following:\n",
-        "\n",
-        "*   tracking and visualizing metrics, such as loss and accuracy over time,\n",
-        "*   visualizing model computational graphs (ops and layers),\n",
-        "*   viewing histograms of weights, biases, or other tensors as they change over time,\n",
-        "*   projecting embeddings to a lower dimensional space,\n",
-        "*   displaying image, text, and audio samples.\n",
-        "\n",
-        "In addition to the powerful visualizations from\n",
-        "TensorBoard, Vertex AI TensorBoard provides the following benefits:\n",
-        "\n",
-        "*  a persistent, shareable link to your experiment's dashboard,\n",
-        "\n",
-        "*  a searchable list of all experiments in a project,\n",
-        "\n",
-        "*  integrations with Vertex AI services for model training,\n",
-        "\n",
-        "*  enterprise-grade security, privacy, and compliance.\n",
-        "\n",
-        "With Vertex AI TensorBoard, you can track, visualize, and compare\n",
-        "ML experiments and share them with your team.\n",
-        "\n",
-        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-introduction)."
+        "* Python version = 3.9"
       ]
     },
     {
@@ -117,11 +78,12 @@
       "source": [
         "### Objective\n",
         "\n",
-        "This tutorial shows you how to log hyperparameter experiment results in TensorFlow and visualize the results in TensorBoard's Hparams dashboard.\n",
+        "In this notebook, you train a model and perform hyperparameter tuning using tensorflow. You also log the hyperparameters and metrics in Vertex AI TensorBoard.\n",
         "\n",
         "This tutorial uses the following Vertex AI services and resources:\n",
         "\n",
         "- Vertex AI TensorBoard\n",
+        "- Vertex AI Experiments\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -158,15 +120,22 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "i7EUnXsZhAGF"
       },
       "source": [
-        "## Install dependencies\n",
-        "\n",
-        "Install the following packages required to run this tutorial notebook."
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -184,46 +153,69 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "ff555b32bab8"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "f09b4dff629a"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "ee775571c2b5"
       },
       "source": [
-        "## Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92e68cfc3a90"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "### Set up your Google Cloud project\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "46604f70e831"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
+        "if \"google.colab\" in sys.modules:\n",
         "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "    from google.colab import auth\n",
         "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "    auth.authenticate_user()"
       ]
     },
     {
@@ -232,12 +224,9 @@
         "id": "WReHDGG5g0XY"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -249,138 +238,50 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Set the region\n",
         "\n",
-        "**Optional**: Update the 'REGION' variable to specify the region that you want to use. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nsN5NJKSu-GU"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
+        "from google.cloud import aiplatform\n",
         "\n",
-        "To authenticate your Google Cloud account, follow the instructions for your Jupyter environment:"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "74ccc9e52986"
+        "id": "bea5ee30d9ca"
       },
       "source": [
-        "* **Vertex AI Workbench**\n",
-        "<br>You are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "* **Local JupyterLab instance**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "* **Colab**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
+        "## What is Vertex AI TensorBoard?\n",
         "\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "960505627ddf"
-      },
-      "source": [
-        "### Import libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PyQmSRbKA8r-"
-      },
-      "outputs": [],
-      "source": [
-        "from google.cloud import aiplatform"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "source": [
-        "### Initialize the Vertex AI SDK for Python\n",
+        "Vertex AI TensorBoard is an enterprise-ready managed\n",
+        "version of [Open source TensorBoard](https://www.tensorflow.org/tensorboard/get_started)\n",
+        "(TB), which is a Google open source project for machine learning experiment\n",
+        "visualization.\n",
         "\n",
-        "Initialize the Vertex AI SDK for Python for your project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "KllitKlIu-GW"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+        "Vertex AI TensorBoard provides various detailed visualizations, including the following:\n",
+        "\n",
+        "*   tracking and visualizing metrics, such as loss and accuracy over time,\n",
+        "*   visualizing model computational graphs (ops and layers),\n",
+        "*   viewing histograms of weights, biases, or other tensors as they change over time,\n",
+        "*   projecting embeddings to a lower dimensional space,\n",
+        "*   displaying image, text, and audio samples.\n",
+        "\n",
+        "In addition to the powerful visualizations from\n",
+        "TensorBoard, Vertex AI TensorBoard provides the following benefits:\n",
+        "\n",
+        "*  a persistent, shareable link to your experiment's dashboard,\n",
+        "\n",
+        "*  a searchable list of all experiments in a project,\n",
+        "\n",
+        "*  integrations with Vertex AI services for model training,\n",
+        "\n",
+        "*  enterprise-grade security, privacy, and compliance.\n",
+        "\n",
+        "With Vertex AI TensorBoard, you can track, visualize, and compare\n",
+        "ML experiments and share them with your team.\n",
+        "\n",
+        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-introduction)."
       ]
     },
     {
@@ -389,7 +290,7 @@
         "id": "WjWD61gONRkw"
       },
       "source": [
-        "### Load TensorBoard and TensorFlow components\n",
+        "## Load TensorBoard and TensorFlow components\n",
         "\n",
         "Load the TensorBoard notebook extension and import TensorFlow and the TensorBoard HParams plugin.\n"
       ]
@@ -419,7 +320,7 @@
         "id": "KJ4zE7rYfcvb"
       },
       "source": [
-        "### Download dataset\n",
+        "## Download dataset\n",
         "\n",
         "Download the [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and scale it."
       ]
@@ -448,9 +349,9 @@
         "\n",
         "Run an experiment by specifying values for the following hyperparameters:\n",
         "\n",
-        "* number of units in the first dense layer,\n",
-        "* dropout rate in the dropout layer,\n",
-        "* optimizer.\n",
+        "* number of units in the first dense layer\n",
+        "* dropout rate in the dropout layer\n",
+        "* optimizer\n",
         "\n",
         "Specify the hyperparameter values for the experiment in TensorBoard.\n",
         "\n",
@@ -486,7 +387,7 @@
       "source": [
         "## Adapt TensorFlow runs to log hyperparameters and metrics\n",
         "\n",
-        "The model will be quite simple: two dense layers with a dropout layer between them. The training code will look familiar, although the hyperparameters are no longer hardcoded. Instead, the hyperparameters are provided in an `hparams` dictionary and used throughout the training function:"
+        "The model you define is quite simple: two dense layers with a dropout layer between them. Your hyperparameters are provided in an `hparams` dictionary and used throughout the training function."
       ]
     },
     {
@@ -525,7 +426,7 @@
         "id": "Esz3uqqCvLoK"
       },
       "source": [
-        "For each run, log an hparams summary with the hyperparameters and final accuracy:"
+        "For each run, log the summary with hyperparameters and final accuracy."
       ]
     },
     {
@@ -553,9 +454,9 @@
         "\n",
         "You can now try multiple experiments, training each one with a different set of hyperparameters.\n",
         "\n",
-        "For simplicity, use a grid search: try all combinations of the discrete parameters and just the lower and upper bounds of the real-valued parameter. For more complex scenarios, it might be more effective to choose each hyperparameter value randomly (this is called a random search). There are more advanced methods that can be used.\n",
+        "For simplicity, use grid search: try all combinations of the discrete parameters and just the lower and upper bounds of the real-valued parameter. For more complex scenarios, it might be more effective to choose each hyperparameter value randomly (this is called a random search). There are more advanced methods that can be used.\n",
         "\n",
-        "Run a few experiments, which will take a few minutes:"
+        "In the below cell, run a few experiments. This takes a few minutes to complete."
       ]
     },
     {
@@ -583,22 +484,13 @@
         "            session_num += 1"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6FJJwCclvslF"
-      },
-      "source": [
-        "## Visualize the results in Vertex AI TensorBoard's HParams tab"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "BkbB5GEI3Ge3"
       },
       "source": [
-        "### Create Vertex AI Tensorboard\n",
+        "## Create Vertex AI TensorBoard\n",
         "A Vertex AI TensorBoard instance, which is a regionalized resource storing your Vertex AI TensorBoard experiments, must be created before the experiments can be visualized. You can create multiple instances in a project.\n",
         "\n",
         "Learn more see [Create a Vertex AI TensorBoard instance](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-setup#create-tensorboard-instance).\n",
@@ -614,17 +506,11 @@
       },
       "outputs": [],
       "source": [
-        "TENSORBOARD_NAME = \"[your-tensorboard-name]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "if (\n",
-        "    TENSORBOARD_NAME == \"\"\n",
-        "    or TENSORBOARD_NAME is None\n",
-        "    or TENSORBOARD_NAME == \"[your-tensorboard-name]\"\n",
-        "):\n",
-        "    TENSORBOARD_NAME = PROJECT_ID + \"-tb-\"\n",
+        "# Set the display name for your tensorboard instance\n",
+        "TENSORBOARD_NAME = f\"tb-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}\n",
         "\n",
         "tensorboard = aiplatform.Tensorboard.create(\n",
-        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=REGION\n",
+        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "TENSORBOARD_RESOURCE_NAME = tensorboard.gca_resource.name\n",
         "print(\"TensorBoard resource name:\", TENSORBOARD_RESOURCE_NAME)"
@@ -647,16 +533,7 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
-        "\n",
-        "EXPERIMENT_NAME = \"[your-experiment-run-name]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "if (\n",
-        "    EXPERIMENT_NAME == \"\"\n",
-        "    or EXPERIMENT_NAME is None\n",
-        "    or EXPERIMENT_NAME == \"[your-experiment-run-name]\"\n",
-        "):\n",
-        "    EXPERIMENT_NAME = \"experiment\" + datetime.now().strftime(\"%H-%M-%S\")"
+        "EXPERIMENT_NAME = f\"experiment-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -665,7 +542,7 @@
         "id": "f1D2oU3K8Ys0"
       },
       "source": [
-        "Upload the log to your Vertex AI TensorBoard"
+        "Upload the log to your Vertex AI TensorBoard."
       ]
     },
     {
@@ -685,23 +562,25 @@
         "id": "OFe3qRyh9Wjl"
       },
       "source": [
+        "## Visualize the results in Vertex AI TensorBoard's HParams tab\n",
+        "\n",
         "Click the generated TensorBoard link and click on \"HParams\" at the top.\n",
         "\n",
-        "The left pane of the dashboard provides filtering capabilities that are active across all the views in the HParams dashboard:\n",
+        "The left pane of the dashboard provides filtering capabilities that are active across all the views in the HParams dashboard. In this pane, you can:\n",
         "\n",
-        "- Filter which hyperparameters/metrics are shown in the dashboard\n",
-        "- Filter which hyperparameter/metrics values are shown in the dashboard\n",
-        "- Filter on run status (running, success, ...)\n",
-        "- Sort by hyperparameter/metric in the table view\n",
-        "- Number of session groups to show (useful for performance when there are many experiments)\n",
+        "- Filter which hyperparameters/metrics are shown in the dashboard.\n",
+        "- Filter which hyperparameter/metrics values are shown in the dashboard.\n",
+        "- Filter on run status (running, success, etc.).\n",
+        "- Sort by hyperparameter/metric in the table view.\n",
+        "- Select number of session groups to show (useful for performance when there are many experiments).\n",
         "\n",
         "The HParams dashboard has three different views, with various useful information:\n",
         "\n",
         "* The **Table View** lists the runs, their hyperparameters, and their metrics.\n",
-        "* The **Parallel Coordinates View** shows each run as a line going through an axis for each hyperparemeter and metric. Click and drag the mouse on any axis to mark a region which will highlight only the runs that pass through it. This can be useful for identifying which groups of hyperparameters are most important. The axes themselves can be re-ordered by dragging them.\n",
-        "* The **Scatter Plot View** shows plots comparing each hyperparameter/metric with each metric. This can help identify correlations. Click and drag to select a region in a specific plot and highlight those sessions across the other plots.\n",
+        "* The **Parallel Coordinates View** shows each run as a line going through an axis for each hyperparemeter and metric. Click and drag the mouse on any axis to mark a region which highlights only the runs that pass through it. This can be useful for identifying which groups of hyperparameters are most important. The axes themselves can be re-ordered by dragging them.\n",
+        "* The **Scatter Plot Matrix View** shows plots comparing each hyperparameter/metric with each metric. This can help identify correlations. Click and drag to select a region in a specific plot and highlight those sessions across the other plots.\n",
         "\n",
-        "A table row, a parallel coordinates line, and a scatter plot market can be clicked to see a plot of the metrics as a function of training steps for that session (although in this tutorial only one step is used for each run)."
+        "These views help to see the plots of the metrics as a function of training steps for that session (although in this tutorial only one step is used for each run)."
       ]
     },
     {
@@ -722,22 +601,19 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "sx_vKniMq9ZX"
+        "id": "8b0c3e372671"
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "# Delete the Vertex AI Experiment\n",
+        "experiment = aiplatform.Experiment(EXPERIMENT_NAME)\n",
+        "experiment.delete()\n",
         "\n",
-        "# Delete endpoint resource\n",
-        "# e.g. `endpoint.delete()`\n",
+        "# Delete the tensorboard instance\n",
+        "tensorboard.delete()\n",
         "\n",
-        "# Delete model resource\n",
-        "# e.g. `model.delete()`\n",
-        "\n",
-        "# Delete Cloud Storage objects that were created\n",
-        "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "# Delete the locally generated logs folder\n",
+        "! rm -rf logs/"
       ]
     }
   ],

notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb

@@ -8,7 +8,7 @@
       },
       "outputs": [],
       "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2024 Google LLC\n",
         "#\n",
         "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
         "# you may not use this file except in compliance with the License.\n",
@@ -32,22 +32,24 @@
         "# Profile model training performance using Vertex AI TensorBoard Profiler\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftensorboard%2Ftensorboard_profiler_custom_training.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -123,12 +125,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ze4-nDLfK4pw"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -145,158 +154,92 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "aUw6ibN-n5Za"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "FM12wbWhn7w0"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "LgFWLeJfoGQu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). \n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "8ckyxpX_oSzD"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "zY8DKBoVoVy3"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mSQjVQmMosMl"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Se9FWWhLotvB"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IfJRIMBpo5Pg"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "acFN0s3So9-Y"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dQ_mNwuapE5T"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cR_MzpknpGgM"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h-MuVI_ypJfw"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BeaQlCwMpQUT"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -305,9 +248,9 @@
         "id": "3ivZkPUjpaFz"
       },
       "source": [
-        "**4. Setup service account and permissions**\n",
+        "### Setup service account and permissions\n",
         "\n",
-        "A service account is used to create custom training jobs. If you do not want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the [instructions](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
+        "A service account is used to create custom training jobs. If you don't want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the [instructions](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
       ]
     },
     {
@@ -404,7 +347,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $LOCATION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -425,6 +368,7 @@
       "outputs": [],
       "source": [
         "import os\n",
+        "import sys\n",
         "\n",
         "from google.cloud import aiplatform"
       ]
@@ -448,7 +392,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -530,7 +474,7 @@
       "outputs": [],
       "source": [
         "tensorboard = aiplatform.Tensorboard.create(\n",
-        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=REGION\n",
+        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "\n",
         "TENSORBOARD_INSTANCE_NAME = tensorboard.resource_name\n",
@@ -544,7 +488,7 @@
         "id": "yoR29gW2S24w"
       },
       "source": [
-        "## Train a model\n",
+        "### Train a model\n",
         "\n",
         "To train a model using your custom training code, choose one of the following options:\n",
         "\n",
@@ -578,7 +522,7 @@
         "\n",
         "! gcloud artifacts repositories create {DOCKER_REPOSITORY} \\\n",
         "    --repository-format=docker \\\n",
-        "    --location={REGION} \\\n",
+        "    --location={LOCATION} \\\n",
         "    --description=\"Repository for TensorBoard Custom Training Job\" \\\n",
         "    --quiet\n",
         "\n",
@@ -604,10 +548,8 @@
       },
       "outputs": [],
       "source": [
-        "import sys\n",
-        "\n",
         "if not IS_COLAB:\n",
-        "    ! gcloud auth configure-docker {REGION}-docker.pkg.dev --quiet"
+        "    ! gcloud auth configure-docker {LOCATION}-docker.pkg.dev --quiet"
       ]
     },
     {
@@ -817,9 +759,9 @@
       "outputs": [],
       "source": [
         "IMAGE_NAME = \"tensorboard-custom-container\"\n",
-        "IMAGE_URI = f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{DOCKER_REPOSITORY}/{IMAGE_NAME}\"\n",
+        "IMAGE_URI = f\"{LOCATION}-docker.pkg.dev/{PROJECT_ID}/{DOCKER_REPOSITORY}/{IMAGE_NAME}\"\n",
         "\n",
-        "! gcloud builds submit --project {PROJECT_ID} --region={REGION} --tag {IMAGE_URI} --timeout=3600s --quiet"
+        "! gcloud builds submit --project {PROJECT_ID} --region={LOCATION} --tag {IMAGE_URI} --timeout=3600s --quiet"
       ]
     },
     {
@@ -932,21 +874,31 @@
       },
       "outputs": [],
       "source": [
-        "delete_tensorboard = True\n",
-        "delete_bucket = False\n",
-        "\n",
         "# Delete docker repository.\n",
-        "! gcloud artifacts repositories delete $DOCKER_REPOSITORY --project {PROJECT_ID} --location {REGION} --quiet\n",
+        "! gcloud artifacts repositories delete $DOCKER_REPOSITORY --project {PROJECT_ID} --location {LOCATION} --quiet\n",
         "\n",
+        "# delete training job\n",
         "try:\n",
         "    job.delete()\n",
         "except Exception as e:\n",
         "    print(e)\n",
         "\n",
+        "# delete tensorboard instance\n",
+        "delete_tensorboard = True\n",
         "if delete_tensorboard:\n",
         "    tensorboard.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# delete custom job\n",
+        "custom_job = f\"{JOB_NAME}-custom-job\"\n",
+        "custom_job_to_delete = aiplatform.CustomJob.list(filter=f\"display_name={custom_job}\")[0]\n",
+        "custom_job_to_delete.delete()\n",
+        "\n",
+        "# delete locally generated files and folders\n",
+        "! rm -rf {PYTHON_PACKAGE_APPLICATION_DIR} Dockerfile\n",
+        "\n",
+        "# delete cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb

@@ -24,43 +24,39 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "l2mMvIUG9meX"
       },
       "source": [
-        "# Profile model training performance using Vertex AI TensorBoard Profiler in custom training with prebuilt container\n",
+        "# Profile model training performance using Cloud Profiler in custom training with prebuilt container\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> <br> Open in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftensorboard%2Ftensorboard_profiler_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"> <br> Open in Colab Enterprise\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"> <br>\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td style=\"text-align: center\">\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> <br>\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "</table>\n",
+        "  "
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "tvgnzT1CKxrO"
@@ -68,13 +64,12 @@
       "source": [
         "## Overview\n",
         "\n",
-        "The TensorFlow Profiler is a powerful tool that can help you to diagnose and debug performance bottlenecks, and make your model train faster. This tutorial demonstrates how to enable the TensorBoard Profiler in Vertex AI for custom training with a prebuilt container.\n",
+        "The Profiler is a powerful tool that can help you to diagnose and debug performance bottlenecks, and make your model train faster. This tutorial demonstrates how to enable Profiler in Vertex AI for custom training with a prebuilt container.\n",
         "\n",
-        "Learn more about [Vertex AI TensorBoard Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler)."
+        "Learn more about [Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler)."
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "dmfmQL6w84pS"
@@ -82,7 +77,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to enable the TensorBoard Profiler in Vertex AI for custom training jobs with a prebuilt container.\n",
+        "In this tutorial, you learn how to enable Profiler in Vertex AI for custom training jobs with a prebuilt container.\n",
         "\n",
         "This tutorial uses the following Google Cloud AI services:\n",
         "\n",
@@ -92,12 +87,11 @@
         "The steps performed include:\n",
         "\n",
         "- Prepare your custom training code and load your training code as a Python package to a prebuilt container\n",
-        "- Create and run a custom training job that enables the TensorBoard Profiler\n",
-        "- View the TensorBoard Profiler dashboard to debug your model training performance\n"
+        "- Create and run a custom training job that enables Profiler\n",
+        "- View the Profiler dashboard to debug your model training performance\n"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "zfXf0r-K81Y-"
@@ -109,7 +103,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "I3KFLvpq87rs"
@@ -130,15 +123,21 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "ze4-nDLfK4pw"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -153,164 +152,105 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "aUw6ibN-n5Za"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "FM12wbWhn7w0"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "LgFWLeJfoGQu"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "8ckyxpX_oSzD"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "zY8DKBoVoVy3"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mSQjVQmMosMl"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Se9FWWhLotvB"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IfJRIMBpo5Pg"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cR_MzpknpGgM"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h-MuVI_ypJfw"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BeaQlCwMpQUT"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "3ivZkPUjpaFz"
       },
       "source": [
-        "**4. Setup service account and permissions**\n",
+        "**Setup service account and permissions**\n",
         "\n",
-        "A service account will be used to create custom training jobs. If you do not want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the [instructions](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
+        "A service account is used to create custom training jobs. If you do not want to use your project's Compute Engine service account, set SERVICE_ACCOUNT to another service account ID. You can create a service account by following the [instructions](https://cloud.google.com/iam/docs/creating-managing-service-accounts#creating)."
       ]
     },
     {
@@ -378,7 +318,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "OKtKGmr9pfr6"
@@ -401,7 +340,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "GOaOsIjxp0oB"
@@ -418,11 +356,10 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ankcS-vtp7Wv"
@@ -439,13 +376,10 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "from google.cloud import aiplatform"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "OMrAJ8RGqBQu"
@@ -464,11 +398,10 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "-ayTbNdi62_t"
@@ -480,7 +413,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "9c3QrDTZdaxk"
@@ -501,7 +433,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "vJrWKK0mY7H7"
@@ -521,7 +452,7 @@
       "outputs": [],
       "source": [
         "tensorboard = aiplatform.Tensorboard.create(\n",
-        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=REGION\n",
+        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "\n",
         "TENSORBOARD_INSTANCE_NAME = tensorboard.resource_name\n",
@@ -529,7 +460,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "yoR29gW2S24w"
@@ -547,7 +477,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "syw3GabNGgJz"
@@ -614,7 +543,8 @@
         "\n",
         "REQUIRED_PACKAGES = [\n",
         "    'google-cloud-aiplatform[cloud_profiler]>=1.20.0',\n",
-        "    'protobuf==3.20.2',\n",
+        "    'tensorflow==2.9.3',\n",
+        "    'protobuf>=3.9.2,<3.20'\n",
         "]\n",
         "\n",
         "setup(\n",
@@ -629,7 +559,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "hyAwgsoQmaYI"
@@ -646,7 +575,7 @@
         "      histogram_freq=1)\n",
         "`AIP_TENSORBOARD_LOG_DIR` is in the `BASE_OUTPUT_DIR` that you provide when creating the custom training job.\n",
         "\n",
-        "To enable Vertex AI TensorBoard Profiler for your training job, add the following to your training script:\n",
+        "To enable Profiler for your training job, add the following to your training script:\n",
         "\n",
         "Add the cloud_profiler import at your top level imports:\n",
         "\n",
@@ -749,7 +678,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ihYFahRAr6sj"
@@ -776,7 +704,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "k4e6OYmimqTR"
@@ -824,7 +751,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "51hKGTbU32Eg"
@@ -832,7 +758,7 @@
       "source": [
         "#### Run the custom training job\n",
         "\n",
-        "Next, you run the custom job to start the training job by invoking the method `run`.\n",
+        "Next, run the custom job to start the training job by invoking the method `run()`.\n",
         "\n",
         "**NOTE:** When using Vertex AI SDK for Python for submitting a training job, it creates a [training pipeline](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline) which launches the custom job on Vertex AI Training service."
       ]
@@ -856,21 +782,19 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "JkEe2Nb_85UD"
       },
       "source": [
-        "## View the TensorBoard Profiler dashboard\n",
+        "## View the Profiler dashboard\n",
         "\n",
-        "When the custom job state switches to running, you can access the Vertex AI TensorBoard Profiler dashboard through the Custom jobs page or the Experiments page on the Google Cloud console.\n",
+        "When the custom job state switches to running, you can access the Profiler dashboard through the Custom jobs page or the Experiments page on the Google Cloud console.\n",
         "\n",
-        "The Google Cloud guide to [Profile model training performance using profiler](https://cloud.google.com/vertex-ai/docs/training/tensorboard-profiler) provides detailed instructions for accessing the Vertex AI TensorBoard Profiler dashboard and capturing a profiling session.\n"
+        "The Google Cloud guide to [Profile model training performance using Cloud Profiler](https://cloud.google.com/vertex-ai/docs/training/tensorboard-profiler) provides detailed instructions for accessing the Profiler dashboard and capturing a profiling session.\n"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "TpV-iwP9qw9c"
@@ -892,12 +816,23 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
-        "\n",
+        "# delete training job\n",
         "job.delete()\n",
+        "\n",
+        "# delete tensorboard instance\n",
         "tensorboard.delete()\n",
         "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "# delete custom job\n",
+        "custom_job = f\"{JOB_NAME}-custom-job\"\n",
+        "custom_job_to_delete = aiplatform.CustomJob.list(filter=f\"display_name={custom_job}\")[0]\n",
+        "custom_job_to_delete.delete()\n",
+        "\n",
+        "# delete locally generated files\n",
+        "! rm -rf {PYTHON_PACKAGE_APPLICATION_DIR}\n",
+        "\n",
+        "# delete the bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb

@@ -32,30 +32,27 @@
         "# Vertex AI TensorBoard integration with Vertex AI Pipelines\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> <br> Open in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftensorboard%2Ftensorboard_vertex_ai_pipelines_integration.ipynb\">\n",
-        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"> <br> Open in Colab Enterprise\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
         "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"> <br>\n",
-        "      View on GitHub\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td style=\"text-align: center\">\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> <br>\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "</table>\n"
       ]
     },
     {
@@ -129,7 +126,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "Dataset used in this tutorial will be the [flower dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) provided by TensorFlow. No other datasets are required.\n"
+        "Dataset used in this tutorial is the [flower dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) provided by TensorFlow. No other datasets are required.\n"
       ]
     },
     {
@@ -151,12 +148,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "s3moH5AexXpk"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -176,98 +180,97 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nqwi-5ufWp_B"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Set the region\n",
-        "\n",
-        "**Optional**: Update the 'REGION' variable to specify the region that you want to use. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nsN5NJKSu-GU"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -308,81 +311,6 @@
         "UUID = generate_uuid()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "To authenticate your Google Cloud account, follow the instructions for your Jupyter environment:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74ccc9e52986"
-      },
-      "source": [
-        "* **Vertex AI Workbench**\n",
-        "<br>You are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "de775a3773ba"
-      },
-      "source": [
-        "* **Local JupyterLab instance**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "* **Colab**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -422,7 +350,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -516,21 +444,25 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "KIPcg_Xhwvsn"
+        "id": "053d6fc3e714"
       },
       "source": [
-        "### Import aiplatform"
+        "### Import libraries "
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f575a6de11a2"
+        "id": "3z_Z5xhvkmzn"
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aiplatform"
+        "from google.cloud import aiplatform\n",
+        "from google_cloud_pipeline_components.v1.custom_job.utils import \\\n",
+        "    create_custom_training_job_op_from_component\n",
+        "from kfp.v2 import dsl\n",
+        "from kfp.v2.dsl import component"
       ]
     },
     {
@@ -551,7 +483,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -579,29 +511,6 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "K2kY3l8zkgvd"
-      },
-      "source": [
-        "Additional imports.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3z_Z5xhvkmzn"
-      },
-      "outputs": [],
-      "source": [
-        "from google_cloud_pipeline_components.v1.custom_job.utils import \\\n",
-        "    create_custom_training_job_op_from_component\n",
-        "from kfp.v2 import dsl\n",
-        "from kfp.v2.dsl import component"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -638,7 +547,7 @@
         "    TENSORBOARD_NAME = PROJECT_ID + \"-tb-\" + UUID\n",
         "\n",
         "tensorboard = aiplatform.Tensorboard.create(\n",
-        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=REGION\n",
+        "    display_name=TENSORBOARD_NAME, project=PROJECT_ID, location=LOCATION\n",
         ")\n",
         "TENSORBOARD_RESOURCE_NAME = tensorboard.gca_resource.name\n",
         "print(\"TensorBoard resource name:\", TENSORBOARD_RESOURCE_NAME)"
@@ -689,7 +598,7 @@
       "outputs": [],
       "source": [
         "@component(\n",
-        "    base_image=\"tensorflow/tensorflow:latest\",\n",
+        "    base_image=\"tensorflow/tensorflow\",\n",
         "    packages_to_install=[\"tensorflow_datasets\"],\n",
         ")\n",
         "def trainer(tb_log_dir_env_var: str = \"AIP_TENSORBOARD_LOG_DIR\"):\n",
@@ -789,7 +698,7 @@
         "    logging.info(f\"Exporting SavedModel to: {output_directory}\")\n",
         "    # Add softmax layer for intepretability\n",
         "    probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\n",
-        "    probability_model.save(output_directory)"
+        "    tf.saved_model.save(probability_model, output_directory)"
       ]
     },
     {
@@ -826,7 +735,7 @@
         "        base_output_directory=BASE_OUTPUT_DIR,\n",
         "        service_account=SERVICE_ACCOUNT,\n",
         "    )\n",
-        "    custom_job_op(project=PROJECT_ID, location=REGION)"
+        "    custom_job_op(project=PROJECT_ID, location=LOCATION)"
       ]
     },
     {
@@ -882,9 +791,7 @@
         "    pipeline_root=PIPELINE_ROOT,\n",
         ")\n",
         "\n",
-        "job.run()\n",
-        "\n",
-        "! rm tensorboard-pipeline-integration.json"
+        "job.run()"
       ]
     },
     {
@@ -921,14 +828,25 @@
       },
       "outputs": [],
       "source": [
-        "# Delete GCS bucket.\n",
-        "! gsutil -m rm -r {BUCKET_URI}\n",
-        "\n",
         "# Delete TensorBoard instance.\n",
         "! gcloud ai tensorboards delete {TENSORBOARD_RESOURCE_NAME}\n",
         "\n",
-        "# Delete custom job.\n",
-        "job.delete()"
+        "# Delete Vertex AI pipeline.\n",
+        "job.delete()\n",
+        "\n",
+        "# Delete the Custom Job using its display name.\n",
+        "jobs = aiplatform.CustomJob.list(filter='display_name=\"Trainer\"')\n",
+        "if jobs:\n",
+        "    job = jobs[0]\n",
+        "    job.delete()\n",
+        "\n",
+        "# Delete locally generated .json file\n",
+        "! rm tensorboard-pipeline-integration.json\n",
+        "\n",
+        "# Delete GCS bucket.\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r {BUCKET_URI}"
       ]
     }
   ],

notebooks/official/training/pytorch_gcs_data_training.ipynb

@@ -86,7 +86,7 @@
         "The steps performed include:\n",
         "\n",
         "- Writing a custom training script that creates your train & test datasets and trains the model.\n",
-        "- Runing a `CustomTrainingJob` using Vertex AI SDK for Python."
+        "- Running a `CustomTrainingJob` using Vertex AI SDK for Python."
       ]
     },
     {

notebooks/official/training/sdk_pytorch_torchrun_custom_container_training_imagenet.ipynb

@@ -87,7 +87,7 @@
         "The steps performed include:\n",
         "\n",
         "    * Create a shell script to start an ETCD cluster on the master node\n",
-        "    * Create a training script using code from PyTorch Elastic's Github repository\n",
+        "    * Create a training script using code from PyTorch Elastic's GitHub repository\n",
         "    * Create containers that download the data, and start an ETCD cluster on the host\n",
         "    * Train the model using multiple nodes with GPUs"
       ]

notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb

@@ -32,25 +32,27 @@
         "# Optimizing multiple objectives with Vertex AI Vizier\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fvizier%2Fgapic-vizier-multi-objective-optimization.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb\">\n",
-        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>"
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -61,7 +63,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview) multi-objective optimization. Multi-objective optimization is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.\n",
+        "This tutorial demonstrates Vertex AI Vizier multi-objective optimization. Multi-objective optimization is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.\n",
         "\n",
         "Learn more about [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview)."
       ]
@@ -74,7 +76,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Vizier` to optimize a multi-objective study.\n",
+        "In this tutorial, you learn how to use Vertex AI Vizier to optimize a multi-objective study.\n",
         "\n",
         "The goal is to __`minimize`__ the objective metric:\n",
         "   ```\n",
@@ -86,7 +88,7 @@
         "   y2 = r*cos(theta)\n",
         "   ```\n",
         "\n",
-        "that you will evaluate over the parameter space:\n",
+        "that you're going to evaluate over the parameter space:\n",
         "\n",
         "   - __`r`__ in [0,1],\n",
         "\n",
@@ -107,12 +109,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "iMHz63rPbq6P"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -123,46 +132,88 @@
       },
       "outputs": [],
       "source": [
-        "! pip3 install --quiet google-vizier==0.0.4\n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform"
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
+        "                               numpy==1.23.0 \\\n",
+        "                               google-vizier"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "58707a750154"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f200f10a1da3"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WReHDGG5g0XY"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "#### Set your project ID\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -174,94 +225,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Set the region\n",
-        "\n",
-        "**Optional**: Update the 'REGION' variable to specify the region that you want to use. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nsN5NJKSu-GU"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "To authenticate your Google Cloud account, follow the instructions for your Jupyter environment:\n",
-        "\n",
-        "**1. Vertex AI Workbench**\n",
-        "<br>You are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab instance**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "254614fa0c46"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef21552ccea8"
-      },
-      "source": [
-        "**3. Colab**\n",
-        "<br>Uncomment and run the following code:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "603adbbf0532"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -300,7 +269,7 @@
         "id": "KyEjqIdnad0w"
       },
       "source": [
-        "This section defines some parameters and util methods to call Vertex Vizier APIs. Please fill in the following information to get started."
+        "This section defines some parameters and util methods to call Vertex Vizier APIs."
       ]
     },
     {
@@ -315,9 +284,9 @@
         "STUDY_DISPLAY_NAME = \"{}_study_{}\".format(\n",
         "    PROJECT_ID.replace(\"-\", \"\"), datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
         ")\n",
-        "PARENT = \"projects/{}/locations/{}\".format(PROJECT_ID, REGION)\n",
+        "PARENT = \"projects/{}/locations/{}\".format(PROJECT_ID, LOCATION)\n",
         "\n",
-        "print(\"REGION: {}\".format(REGION))\n",
+        "print(\"LOCATION: {}\".format(LOCATION))\n",
         "print(\"PARENT: {}\".format(PARENT))"
       ]
     },
@@ -329,7 +298,7 @@
       "source": [
         "### Create the study configuration\n",
         "\n",
-        "The following is a sample study configuration, built as a hierarchical python dictionary. It is already filled out. Run the cell to configure the study."
+        "The following is a sample study configuration, built as a hierarchical python dictionary. It's already filled out. Run the cell to configure the study."
       ]
     },
     {
@@ -366,7 +335,7 @@
       "source": [
         "### Create the study\n",
         "\n",
-        "Next, create the study, which you will subsequently run to optimize the two objectives."
+        "Next, create the study, which you can subsequently run to optimize the two objectives."
       ]
     },
     {
@@ -377,7 +346,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)\n",
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION)\n",
         "study = Study.create_or_load(display_name=STUDY_DISPLAY_NAME, problem=problem)\n",
         "\n",
         "STUDY_ID = study.name\n",
@@ -445,11 +414,11 @@
       "source": [
         "### Set configuration parameters for running trials\n",
         "\n",
-        "__`client_id`__: The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested trial if the trial is `PENDING`, and provide a new trial if the last suggested trial was completed.\n",
+        "__`client_id`__: The identifier of the client requesting the suggestion. If multiple `SuggestTrialsRequests` have the same `client_id`, the service returns the identical suggested trial if the trial is `PENDING`, and provide a new trial if the last suggested trial is complete.\n",
         "\n",
         "__`suggestion_count_per_request`__: The number of suggestions (trials) requested in a single request.\n",
         "\n",
-        "__`max_trial_id_to_stop`__: The number of trials to explore before stopping. It is set to 4 to shorten the time to run the code, so don't expect convergence. For convergence, it would likely need to be about 20 (a good rule of thumb is to multiply the total dimensionality by 10).\n"
+        "__`max_trial_id_to_stop`__: The number of trials to explore before stopping. It's set to 4 to shorten the time to run the code, so don't expect convergence. For convergence, it would likely need to be about 20 (a good rule of thumb is to multiply the total dimensionality by 10).\n"
       ]
     },
     {
@@ -462,7 +431,7 @@
       "source": [
         "worker_id = \"worker1\"  # @param {type: 'string'}\n",
         "suggestion_count_per_request = 3  # @param {type: 'integer'}\n",
-        "max_trial_id_to_stop = 6  # @param {type: 'integer'}\n",
+        "max_trial_id_to_stop = 4  # @param {type: 'integer'}\n",
         "\n",
         "print(\"worker_id: {}\".format(worker_id))\n",
         "print(\"suggestion_count_per_request: {}\".format(suggestion_count_per_request))\n",
@@ -509,7 +478,7 @@
       "source": [
         "### List the optimal solutions\n",
         "\n",
-        "list_optimal_trials returns the pareto-optimal Trials for multi-objective Study or the optimal Trials for single-objective Study. In the case, we define mutliple-objective in previeous steps, pareto-optimal trials will be returned."
+        "The `optimal_trials()` method returns the Pareto-optimal trials for a multi-objective study or the optimal trials for a single-objective study. If multiple objectives are defined in previous steps, Pareto-optimal trials are returned."
       ]
     },
     {

notebooks/official/vizier/get_started_vertex_vizier.ipynb

@@ -32,25 +32,27 @@
         "# Get started with Vertex AI Vizier\n",
         "\n",
         "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "        </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fvizier%2Fget_started_vertex_vizier.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>           \n",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -75,13 +77,13 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Vizier` for when training with `Vertex AI`.\n",
+        "In this tutorial, you learn how to use Vertex AI Vizier when training with Vertex AI.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Hyperparameter Tuning`\n",
-        "- `Vertex AI Vizier`\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI hyperparameter tuning\n",
+        "- Vertex AI Vizier\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -134,7 +136,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
+        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset used in this tutorial is the one that's available from TensorFlow SDK. The trained model predicts the median price of a house in units of 1K USD."
       ]
     },
     {
@@ -161,12 +163,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "install_mlops"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing this notebook."
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -178,54 +187,87 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-vizier==0.0.4  "
+        "                                 numpy==1.23.0 \\\n",
+        "                                 google-vizier"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "restart"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "2721ef0202d9"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "## Before you begin"
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "dmWOrTJ3gx13"
       },
       "source": [
-        "### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "* Run `gcloud config list`.\n",
-        "* Run `gcloud projects list`.\n",
-        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -237,105 +279,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ad1138a125ea"
-      },
-      "source": [
-        "**2. Local JupyterLab instance, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -355,7 +304,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -364,7 +313,7 @@
         "id": "create_bucket"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -375,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -395,7 +344,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_URI"
+        "! gsutil ls -al {BUCKET_URI}"
       ]
     },
     {
@@ -418,7 +367,7 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aiplatform\n",
+        "from google.cloud import aiplatform\n",
         "from google.cloud.aiplatform.vizier import Study, pyvizier"
       ]
     },
@@ -454,9 +403,9 @@
         "\n",
         "You can set hardware accelerators for training.\n",
         "\n",
-        "Set the variable `TRAIN_GPU/TRAIN_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "Set the variable `TRAIN_GPU/TRAIN_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa T4 GPUs allocated to each VM, you'd specify:\n",
         "\n",
-        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 4)\n",
         "\n",
         "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
         "\n",
@@ -471,7 +420,7 @@
       },
       "outputs": [],
       "source": [
-        "TRAIN_GPU, TRAIN_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)"
+        "TRAIN_GPU, TRAIN_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4, 1)"
       ]
     },
     {
@@ -512,7 +461,7 @@
         "        TRAIN_VERSION = \"tf-cpu.{}\".format(TF)\n",
         "\n",
         "TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}:latest\".format(\n",
-        "    REGION.split(\"-\")[0], TRAIN_VERSION\n",
+        "    LOCATION.split(\"-\")[0], TRAIN_VERSION\n",
         ")\n",
         "\n",
         "print(\"Training:\", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)"
@@ -528,19 +477,19 @@
         "\n",
         "Next, set the machine type to use for training.\n",
         "\n",
-        "- Set the variable `TRAIN_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
+        "- Set the variable `TRAIN_COMPUTE` to configure the compute resources for the VMs used for training.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
         "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
         " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
         "\n",
-        "*Note: The following is not supported for training:*\n",
+        "**Note**: The following isn't supported for training:\n",
         "\n",
         " - `standard`: 2 vCPUs\n",
         " - `highcpu`: 2, 4 and 8 vCPUs\n",
         "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+        "**Note**: You can also use n2 and e2 machine types for training and deployment, but they don't support GPUs."
       ]
     },
     {
@@ -563,11 +512,11 @@
       "source": [
         "## Standalone Vertex AI Vizer service\n",
         "\n",
-        "The `Vizier` service can be used as a standalone service for selecting the next set of parameters for a trial.\n",
+        "The Vizier service can be used as a standalone service for selecting the next set of parameters for a trial.\n",
         "\n",
-        "*Note:* The service does not execute trials. You create your own trial and execution.\n",
+        "**Note:** The service doesn't execute trials. You create your own trial and execution.\n",
         "\n",
-        "Learn more about [Using Vizier](https://cloud.google.com/vertex-ai/docs/vizier/using-vizier)"
+        "Learn more about how to [Create Vertex AI Vizier studies](https://cloud.google.com/vertex-ai/docs/vizier/using-vizier)"
       ]
     },
     {
@@ -578,7 +527,7 @@
       "source": [
         "## Vertex AI Hyperparameter Tuning service\n",
         "\n",
-        "The following example demonstrates how to setup, execute and evaluate trials using the Vertex AI Hyperparameter Tuning service with `random` search algorithm.\n",
+        "The following example demonstrates how to setup, execute and evaluate trials using the Vertex AI hyperparameter tuning service with `random` search algorithm.\n",
         "\n",
         "Learn more about [Overview of hyperparameter tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview)"
       ]
@@ -593,7 +542,7 @@
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the hyperparameter tuning, you will look at how a Python package is assembled for a custom hyperparameter tuning job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the hyperparameter tuning, take a look at how a Python package is assembled for a custom hyperparameter tuning job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -605,11 +554,11 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom hyperparameter tuning job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom hyperparameter tuning job. *Note*, when `trainer/task.py` is referred to in the worker pool specification, the directory slash is replaced with a dot and the file suffix (`.py`) is dropped (`trainer.task`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you will assemble the training package."
+        "In the following cells, assemble the training package."
       ]
     },
     {
@@ -649,15 +598,17 @@
       "source": [
         "#### Task.py contents\n",
         "\n",
-        "In the next cell, you write the contents of the hyperparameter tuning script task.py. I won't go into detail, it's just there for you to browse. In summary:\n",
+        "In the next cell, write the contents of the hyperparameter tuning script *task.py*. \n",
         "\n",
-        "- Parse the command line arguments for the hyperparameter settings for the current trial.\n",
-        " - Get the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
+        "To summarize, perform the following steps in the script:\n",
+        "\n",
+        "- Parse the command line arguments to obtain hyperparameter settings for the current trial.\n",
+        "- Get the directory for saving the model artifacts from the command line (`--model_dir`), and if not specified, use the environment variable `AIP_MODEL_DIR`.\n",
         "- Download and preprocess the Boston Housing dataset.\n",
         "- Build a DNN model.\n",
-        "- The number of units per dense layer and learning rate hyperparameter values are used during the build and compile of the model.\n",
-        "- A definition of a callback `HPTCallback` which obtains the validation loss at the end of each epoch (`on_epoch_end()`) and reports it to the hyperparameter tuning service using `hpt.report_hyperparameter_tuning_metric()`.\n",
-        "- Train the model with the `fit()` method and specify a callback which will report the validation loss back to the hyperparameter tuning service."
+        "- The hyperparameter values for the number of units per dense layer and the learning rate are utilized during the model's build and compile process.\n",
+        "- Define a `HPTCallback` which obtains the validation loss at the end of each epoch (`on_epoch_end()`) and reports it to the hyperparameter tuning service using `hpt.report_hyperparameter_tuning_metric()`.\n",
+        "- Train the model with the `fit()` method and specify a callback which reports the validation loss back to the hyperparameter tuning service."
       ]
     },
     {
@@ -776,7 +727,7 @@
       "source": [
         "#### Store hyperparameter tuning script on your Cloud Storage bucket\n",
         "\n",
-        "Next, you package the hyperparameter tuning folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
+        "Next, package the hyperparameter tuning folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
     },
     {
@@ -801,9 +752,9 @@
       "source": [
         "### Prepare your machine specification\n",
         "\n",
-        "Now define the machine specification for your custom hyperparameter tuning job. This tells Vertex what type of machine instance to provision for the hyperparameter tuning.\n",
+        "Now define the machine specification for your custom hyperparameter tuning job. This tells Vertex AI about the type of machine instance to provision for the hyperparameter tuning.\n",
         "  - `machine_type`: The type of GCP instance to provision -- e.g., n1-standard-8.\n",
-        "  - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you will use a CPU.\n",
+        "  - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you're using a GPU; otherwise you're using a CPU.\n",
         "  - `accelerator_count`: The number of accelerators."
       ]
     },
@@ -833,7 +784,7 @@
       "source": [
         "### Prepare your disk specification\n",
         "\n",
-        "(optional) Now define the disk specification for your custom hyperparameter tuning job. This tells Vertex what type and size of disk to provision in each machine instance for the hyperparameter tuning.\n",
+        "(**optional**) Now define the disk specification for your custom hyperparameter tuning job. This tells Vertex AI about the type and size of disk to provision in each machine instance for the hyperparameter tuning.\n",
         "\n",
         "  - `boot_disk_type`: Either SSD or Standard. SSD is faster, and Standard is less expensive. Defaults to SSD.\n",
         "  - `boot_disk_size_gb`: Size of disk in GB."
@@ -861,32 +812,32 @@
       "source": [
         "### Define the worker pool specification\n",
         "\n",
-        "Next, you define the worker pool specification for your custom hyperparameter tuning job. The worker pool specification will consist of the following:\n",
+        "Next, define the worker pool specification for your custom hyperparameter tuning job. The worker pool specification consists of the following parameters:\n",
         "\n",
-        "- `replica_count`: The number of instances to provision of this machine type.\n",
+        "- `replica_count`: The number of instances of this machine type to provision.\n",
         "- `machine_spec`: The hardware specification.\n",
         "- `disk_spec` : (optional) The disk storage specification.\n",
-        "\n",
         "- `python_package`: The Python training package to install on the VM instance(s) and which Python module to invoke, along with command line arguments for the Python module.\n",
         "\n",
-        "Let's dive deeper now into the Python package specification:\n",
+        "    - `executor_image_spec`: This is the docker image which is configured for your custom hyperparameter tuning job.\n",
         "\n",
-        "-`executor_image_spec`: This is the docker image which is configured for your custom hyperparameter tuning job.\n",
+        "    - `package_uris`: This is a list of the locations (URIs) of your Python training packages to install on the provisioned instance. The locations need to be in a Cloud Storage bucket. These can be either individual Python files or a zip (archive) of an entire package. In the later case, the job service unzips (unarchives) the contents into the docker image.\n",
         "\n",
-        "-`package_uris`: This is a list of the locations (URIs) of your Python training packages to install on the provisioned instance. The locations need to be in a Cloud Storage bucket. These can be either individual Python files or a zip (archive) of an entire package. In the later case, the job service will unzip (unarchive) the contents into the docker image.\n",
+        "    - `python_module`: The Python module (script) to invoke for running the custom hyperparameter tuning job. In this example, invoke the `trainer.task.py` -- note that it isn't neccessary to append the `.py` suffix.\n",
         "\n",
-        "-`python_module`: The Python module (script) to invoke for running the custom hyperparameter tuning job. In this example, you will be invoking `trainer.task.py` -- note that it was not neccessary to append the `.py` suffix.\n",
-        "\n",
-        "-`args`: The command line arguments to pass to the corresponding Python module. In this example, you will be setting:\n",
-        "  - `\"--model-dir=\" + MODEL_DIR` : The Cloud Storage location where to store the model artifacts. There are two ways to tell the hyperparameter tuning script where to save the model artifacts:\n",
-        "      - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
-        "      - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
-        "  - `\"--epochs=\" + EPOCHS`: The number of epochs for training.\n",
-        "  - `\"--steps=\" + STEPS`: The number of steps (batches) per epoch.\n",
-        "  - `\"--distribute=\" + TRAIN_STRATEGY\"` : The hyperparameter tuning distribution strategy to use for single or distributed hyperparameter tuning.\n",
-        "     - `\"single\"`: single device.\n",
-        "     - `\"mirror\"`: all GPU devices on a single compute instance.\n",
-        "     - `\"multi\"`: all GPU devices on all compute instances."
+        "    - `args`: The command line arguments to pass to the corresponding Python module. In this example, you're going to configure\n",
+        "    \n",
+        "        - `--model-dir` : The Cloud Storage location for where to store the model artifacts. There are two ways to tell the hyperparameter tuning script where to save the model artifacts:\n",
+        "          \n",
+        "          - **method-1**(set `DIRECT` to `True`): Pass the Cloud Storage location as a command line argument to your training script.\n",
+        "          - **method-2**(set `DIRECT` to `False`): The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script. In this case, tell the service the model artifact location in the job specification.\n",
+        "          \n",
+        "    - `--epochs`: The number of epochs for training.\n",
+        "    - `--steps`: The number of steps (batches) per epoch.\n",
+        "    - `--distribute` : The hyperparameter tuning distribution strategy to use for single or distributed hyperparameter tuning.\n",
+        "         - `\"single\"`: single device.\n",
+        "         - `\"mirror\"`: all GPU devices on a single compute instance.\n",
+        "         - `\"multi\"`: all GPU devices on all compute instances."
       ]
     },
     {
@@ -1037,7 +988,7 @@
       "source": [
         "### Display the hyperparameter tuning job trial results\n",
         "\n",
-        "After the hyperparameter tuning job has completed, the property `trials` will return the results for each trial."
+        "Once the hyperparameter tuning job completes, the `trials` property provides the results for each individual trial."
       ]
     },
     {
@@ -1059,7 +1010,7 @@
       "source": [
         "### Best trial\n",
         "\n",
-        "Now look at which trial was the best:"
+        "Now identify which trial is the best."
       ]
     },
     {
@@ -1100,7 +1051,7 @@
       "source": [
         "## Get the Best Model\n",
         "\n",
-        "If you used the method of having the service tell the tuning script where to save the model artifacts (`DIRECT = False`), then the model artifacts for the best model are saved at:\n",
+        "If you're using the method where the service informs the tuning script where to save the model artifacts (`DIRECT = False`), then the model artifacts for the best model are saved at:\n",
         "\n",
         "    MODEL_DIR/<best_trial_id>/model"
       ]
@@ -1124,7 +1075,7 @@
       "source": [
         "### Delete the hyperparameter tuning job\n",
         "\n",
-        "The method 'delete()' will delete the hyperparameter tuning job."
+        "The method `delete()` deletes the hyperparameter tuning job."
       ]
     },
     {
@@ -1146,7 +1097,7 @@
       "source": [
         "## Vertex AI Hyperparameter Tuning and Vertex AI Vizer service combined\n",
         "\n",
-        "The following example demonstrates how to setup, execute and evaluate trials using the Vertex AI Hyperparameter Tuning service with `Vizier` search service."
+        "The following example demonstrates how to setup, execute and evaluate trials using the Vertex AI hyperparameter tuning service with Vizier search service."
       ]
     },
     {
@@ -1194,7 +1145,8 @@
         "- `metrics_spec`: The metrics to optimize. The dictionary key is the metric_id, which is reported by your training job, and the dictionary value is the optimization goal of the metric('minimize' or 'maximize').\n",
         "- `parameter_spec`: The parameters to optimize. The dictionary key is the metric_id, which is passed into your training job as a command line key word argument, and the dictionary value is the parameter specification of the metric.\n",
         "- `search_algorithm`: The search algorithm to use: `grid`, `random` and `None`. If `None` is specified, the `Vizier` service (Bayesian) is used.\n",
-        "- `max_trial_count`: The maximum number of trials to perform."
+        "- `max_trial_count`: The maximum number of trials to perform.\n",
+        "- `parallel_trial_count`: (Optional) It specifies the number of trials to run in parallel."
       ]
     },
     {
@@ -1253,7 +1205,7 @@
       "source": [
         "### Display the hyperparameter tuning job trial results\n",
         "\n",
-        "After the hyperparameter tuning job has completed, the property `trials` will return the results for each trial."
+        "Once the hyperparameter tuning job completes, the `trials` property provides the results for each individual trial."
       ]
     },
     {
@@ -1275,7 +1227,7 @@
       "source": [
         "### Best trial\n",
         "\n",
-        "Now look at which trial was the best:"
+        "Now look at which trial performed the best:"
       ]
     },
     {
@@ -1316,7 +1268,7 @@
       "source": [
         "### Get the Best Model\n",
         "\n",
-        "If you used the method of having the service tell the tuning script where to save the model artifacts (`DIRECT = False`), then the model artifacts for the best model are saved at:\n",
+        "If you're using the method where the service informs the tuning script where to save the model artifacts (`DIRECT = False`), then the model artifacts for the best model are saved at:\n",
         "\n",
         "    MODEL_DIR/<best_trial_id>/model"
       ]
@@ -1342,7 +1294,7 @@
       "source": [
         "### Delete the hyperparameter tuning job\n",
         "\n",
-        "The method 'delete()' will delete the hyperparameter tuning job."
+        "The method `delete()` deletes the hyperparameter tuning job."
       ]
     },
     {
@@ -1364,11 +1316,11 @@
       "source": [
         "## Standalone Vertex AI Vizer service\n",
         "\n",
-        "The `Vizier` service can be used as a standalone service for selecting the next set of parameters for a trial.\n",
+        "The Vizier service can be used as a standalone service for selecting the next set of parameters for a trial.\n",
         "\n",
-        "*Note:* The service does not execute trials. You create your own trial and execution.\n",
+        "**Note:** The service doesn't execute trials. You create your own trial and execution.\n",
         "\n",
-        "Learn more about [Using Vizier](https://cloud.google.com/vertex-ai/docs/vizier/using-vizier)"
+        "Learn more about how to [Create Vertex AI Vizier studies](https://cloud.google.com/vertex-ai/docs/vizier/using-vizier)"
       ]
     },
     {
@@ -1379,7 +1331,7 @@
       "source": [
         "### Specify the algorithm used to suggest trial parameters\n",
         "\n",
-        "First, you create a `StudyConfig`, and specify the algorithm to suggest the next trial.\n",
+        "First, create a `StudyConfig`, and specify the algorithm to suggest the next trial.\n",
         "\n",
         "    GRID_SEARCH: grid search\n",
         "    RANDOM_SEARCH: random search\n",
@@ -1410,7 +1362,7 @@
         "\n",
         "In the following example, the goal is to maximize y = x^2 with x in the range of \\[-10. 10\\]. This example has only one parameter and uses an easily calculated function to help demonstrate how to use Vizier.\n",
         "\n",
-        "First, you specify the metrics to minimize or maximize in the study as a list to the property `metric_information`. Then you specify the parameters to the study using the `add_XXX_params()` method for the corresponding data type:\n",
+        "First, specify the metrics to minimize or maximize in the study as a list to the property `metric_information`. Then specify the parameters to the study using the `add_XXX_params()` method for the corresponding data type:\n",
         "\n",
         "    - add_bool_param\n",
         "    - add_categorical_param\n",
@@ -1418,7 +1370,7 @@
         "    - add_float_param\n",
         "    - add_int_param\n",
         "\n",
-        "You create the study using the `create_or_load()` method."
+        "You can create the study using the `create_or_load()` method."
       ]
     },
     {
@@ -1452,7 +1404,7 @@
       "source": [
         "### Get Vizier study\n",
         "\n",
-        "You can get a study using the method `list()`."
+        "You can get a study using the `list()` method."
       ]
     },
     {
@@ -1475,11 +1427,11 @@
       "source": [
         "### Get suggested trial\n",
         "\n",
-        "Next, query the Vizier service for a suggested trial(s) using the method `suggest()`, with the following key/value pairs:\n",
+        "Next, query the Vizier service for a suggested trial(s) using the `suggest()` method, with the following key/value pairs:\n",
         "\n",
         "- `count`: The number of trials to suggest.\n",
         "\n",
-        "This call is a long running operation. The method `result()` from the response object will wait until the call has completed."
+        "This call is a long running operation. The `result()` method from the response object waits until the call completes."
       ]
     },
     {
@@ -1509,7 +1461,7 @@
         "### Evaluate the results\n",
         "After receiving your trial suggestions, evaluate each trial and record each result as a measurement.\n",
         "\n",
-        "For example, if the function you are trying to optimize is y = x^2, then you evaluate the function using the trial's suggested value of x. Using a suggested value of 0.1, the function evaluates to y = 0.1 * 0.1, which results in 0.01."
+        "For example, if the function you're trying to optimize is y = x^2, then evaluate the function using the trial's suggested value of x. Using a suggested value of 0.1, the function evaluates to y = 0.1 * 0.1, which results in 0.01."
       ]
     },
     {
@@ -1522,7 +1474,7 @@
         "\n",
         "After evaluating your trial suggestion to get a measurement, add this measurement to your trial.\n",
         "\n",
-        "Use the following commands to store your measurement and send the request. In this example, replace RESULT with the measurement. If the function you are optimizing is y = x^2, and the suggested value of x is 0.1, the result is 0.01."
+        "Use the following commands to store your measurement and send the request. In this example, replace `RESULT` with the measurement. If the function you're optimizing is y = x^2, and the suggested value of x is 0.1, the result is 0.01."
       ]
     },
     {
@@ -1541,28 +1493,6 @@
         "trials[0].add_measurement(measurement)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vizier_delete_study"
-      },
-      "source": [
-        "### Delete the Vizier study\n",
-        "\n",
-        "The method 'delete()' will delete the study."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "vizier_delete_study"
-      },
-      "outputs": [],
-      "source": [
-        "study.delete()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1574,9 +1504,7 @@
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
-        "\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial."
       ]
     },
     {
@@ -1587,11 +1515,16 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "# delete the Vizier study\n",
+        "study.delete()\n",
         "\n",
-        "delete_bucket = False\n",
+        "# remove locally generated files\n",
+        "! rm -rf custom.tar.gz custom\n",
         "\n",
-        "if os.getenv(\"IS_TESTING\"):\n",
+        "# deelte cloud storage bucket\n",
+        "delete_bucket = False  # set True for deletion\n",
+        "\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]
     }

notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb

@@ -31,25 +31,28 @@
       "source": [
         "# Forecasting retail demand with Vertex AI and BigQuery ML \n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fnotebook_template.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
+        "     </a>\n",
+        "   </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>"
       ]
     },
     {
@@ -66,7 +69,7 @@
         "\n",
         "#### ARIMA Modeling with BigQuery ML \n",
         "\n",
-        "The <a href='https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average'>ARIMA model</a> is designed to analyze historical data, spot patterns over time, and project them into the future--in other words, forecasting. The model is available inside BigQuery ML and enables users to create and execute machine learning models directly in BigQuery using SQL queries. Working with BigQuery ML is advantageous, as it already has access to the data, it can handle most of the modeling details automatically if desired, and will store both the model and any predictions also inside BigQuery. \n",
+        "The <a href='https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average'>ARIMA model</a> is designed to analyze historical data, spot patterns over time, and project them into the future in other words, forecasting. The model is available inside BigQuery ML and enables users to create and execute machine learning models directly in BigQuery using SQL queries. Working with BigQuery ML is advantageous, as it already has access to the data, it can handle most of the modeling details automatically if desired, and stores both the model and any predictions also inside BigQuery. \n",
         "\n",
         "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
       ]
@@ -101,7 +104,7 @@
       "source": [
         "### Dataset \n",
         "\n",
-        "This notebook uses the BigQuery public retail data set.\n",
+        "This notebook uses the BigQuery public retail dataset.\n",
         "The data covers 10 US stores and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price and gross margin. "
       ]
     },
@@ -117,11 +120,9 @@
         "* Vertex AI\n",
         "* BigQuery\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), [BigQuery\n",
-        "pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), \n",
+        "[BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the \n",
+        "[Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
       ]
     },
     {
@@ -141,7 +142,9 @@
       },
       "outputs": [],
       "source": [
-        "! pip3 install --quiet --upgrade pandas-gbq 'google-cloud-bigquery[bqstorage,pandas]' scikit-learn"
+        "! pip3 install --quiet --upgrade pandas-gbq 'google-cloud-bigquery[bqstorage,pandas]' \\\n",
+        "                                 scikit-learn \\\n",
+        "                                 matplotlib"
       ]
     },
     {
@@ -150,7 +153,8 @@
         "id": "e9255e3b156f"
       },
       "source": [
-        "### Colab Only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
@@ -161,122 +165,74 @@
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "435b8e413535"
+        "id": "4de1bd77992b"
       },
       "source": [
-        "### Before you begin\n",
-        "\n",
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "-  Run `gcloud config list`\n",
-        "-  Run `gcloud projects list`\n",
-        "-  See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "<div class=\"alert alert-block alert-warning\">,\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>,\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "befa6ca14bc0"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
+        "Authenticate your environment on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "be175254a715"
+        "id": "7de6ef0fac42"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bd0e79ceaea2"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "Learn more about [setting up a project and a development environment.](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8940d70dfdef"
       },
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# set the project id\n",
-        "! gcloud config set project $PROJECT_ID"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2e6b8b324ce1"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. \n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ae43d96c4b1b"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c43a8673066"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench** \n",
-        "- Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab Instance,** uncomment and run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fbc9cd30cc4b"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd0da2c26879"
-      },
-      "source": [
-        "**3. Colab,** uncomment and run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a336a05c6149"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0461097edfa5"
-      },
-      "source": [
-        "**4. Service Account or other**\n",
-        "- See all the authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)"
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -298,8 +254,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"your-bucket-name-unique\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -308,7 +263,7 @@
         "id": "b72bfdf29dae"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -319,38 +274,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "07fc8daffbf9"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "abc79bf099b2"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "! gsutil mb -l $LOCATION $BUCKET_URI"
       ]
     },
     {
@@ -435,7 +359,7 @@
       },
       "outputs": [],
       "source": [
-        "dataset_id = \"demandforecasting\" + \"_\" + UUID"
+        "dataset_id = \"demandforecasting\" + \"_\" + \"unique\""
       ]
     },
     {
@@ -444,7 +368,7 @@
         "id": "0wguBZeKp93_"
       },
       "source": [
-        "If you are using ***Vertex AI Workbench managed notebooks instance***, every cell which starts with \"#@bigquery\" will be a SQL Query. If you are using Vertex AI Workbench user managed notebooks instance or Colab it will be a markdown cell."
+        "If you're using ***Vertex AI Workbench managed notebooks instance***, Identify cells starting with \"#@bigquery\" as SQL queries. If you're using [Vertex AI Workbench user managed notebooks instance](https://cloud.google.com/vertex-ai/docs/workbench/user-managed/migrate-to-instances) or Colab it will be a markdown cell."
       ]
     },
     {
@@ -466,7 +390,7 @@
         "id": "RxnaBh4sp93_"
       },
       "source": [
-        "(**Optional**)If you are using Vertex AI Workbench managed notebooks instance, once the results from BigQuery are displayed in the below cell, click the **Query and load as DataFrame** button and execute the generated code stub to fetch the data into the current notebook as a dataframe.\n",
+        "(**Optional**) If you're using Vertex AI Workbench managed notebooks instance, once the results from BigQuery are displayed in the below cell, click the **Query and load as DataFrame** button and execute the generated code stub to fetch the data into the current notebook as a dataframe.\n",
         "\n",
         "*Note: By default the data is loaded into a `df` variable, though this can be changed before executing the cell if required.*"
       ]
@@ -720,7 +644,7 @@
         "id": "f25cf5322fbc"
       },
       "source": [
-        "**Check the data types of your dataframe's fields.**"
+        "**Check the data types of your dataframe fields.**"
       ]
     },
     {
@@ -1093,7 +1017,7 @@
         "id": "5c78641e2881"
       },
       "source": [
-        "**Check the data types of your dataframe's fields.**"
+        "**Check the data types of your dataframe fields.**"
       ]
     },
     {
@@ -1133,9 +1057,9 @@
         "id": "8ec5fa4b1ea0"
       },
       "source": [
-        "For this forecasting model, date values need to be present for all dates, for each product.\n",
+        "For this forecasting model, date values need to present for all dates, for each product.\n",
         "\n",
-        "To construct a dataframe with `0` values for the `sales_count` field, on dates in which products were not sold, determine the minimum and maximum dates so that you know which dates need `0` values.\n",
+        "To construct a dataframe with `0` values for the `sales_count` field, on dates in which products weren't sold, determine the minimum and maximum dates so that you know which dates need `0` values.\n",
         "\n",
         "**First, get the earliest (minimum) date.**"
       ]
@@ -1251,7 +1175,7 @@
         "id": "49eb81d44b65"
       },
       "source": [
-        "**View the data for one of the products, sorted by date, to show that many dates are not present in the dataset.**"
+        "**View the data for one of the products, sorted by date, to show that many dates are'nt present in the dataset.**"
       ]
     },
     {
@@ -1295,7 +1219,7 @@
         "    .sort_values(by=[\"date\"])\n",
         "    .drop(columns=0)\n",
         ")  # merging dates dataframe with product_id matching rows\n",
-        "df1[\"product_id\"] = 20552  # product_id will be null so making it the specified values\n",
+        "df1[\"product_id\"] = 20552  # Set the product ID to the specified values.\n",
         "df1.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
         "df1 = df1.fillna(0)  # for sales_count making null values as 0\n",
         "df1[\"sales_count\"] = df1[\"sales_count\"].astype(\n",
@@ -1315,7 +1239,7 @@
         "    .sort_values(by=[\"date\"])\n",
         "    .drop(columns=0)\n",
         ")  # merging dates dataframe with product_id matching rows\n",
-        "df2[\"product_id\"] = 13596  # product_id will be null so making it the specified values\n",
+        "df2[\"product_id\"] = 13596  # Set the product ID to the specified values.\n",
         "df2.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
         "df2 = df2.fillna(0)  # for sales_count making null values as 0\n",
         "df2[\"sales_count\"] = df2[\"sales_count\"].astype(\n",
@@ -1334,7 +1258,7 @@
         "    .sort_values(by=[\"date\"])\n",
         "    .drop(columns=0)\n",
         ")  # merging dates dataframe with product_id matching rows\n",
-        "df3[\"product_id\"] = 23641  # product_id will be null so making it the specified values\n",
+        "df3[\"product_id\"] = 23641  # Set the product ID to the specified values.\n",
         "df3.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
         "df3 = df3.fillna(0)  # for sales_count making null values as 0\n",
         "df3[\"sales_count\"] = df3[\"sales_count\"].astype(\n",
@@ -1353,7 +1277,7 @@
         "    .sort_values(by=[\"date\"])\n",
         "    .drop(columns=0)\n",
         ")  # merging dates dataframe with product_id matching rows\n",
-        "df4[\"product_id\"] = 28305  # product_id will be null so making it the specified values\n",
+        "df4[\"product_id\"] = 28305  # Set the product ID to the specified values.\n",
         "df4.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
         "df4 = df4.fillna(0)  # for sales_count making null values as 0\n",
         "df4[\"sales_count\"] = df4[\"sales_count\"].astype(\n",
@@ -1372,7 +1296,7 @@
         "    .sort_values(by=[\"date\"])\n",
         "    .drop(columns=0)\n",
         ")  # merging dates dataframe with product_id matching rows\n",
-        "df5[\"product_id\"] = 20547  # product_id will be null so making it the specified values\n",
+        "df5[\"product_id\"] = 20547  # Set the product ID to the specified values.\n",
         "df5.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
         "df5 = df5.fillna(0)  # for sales_count making null values as 0\n",
         "df5[\"sales_count\"] = df5[\"sales_count\"].astype(\n",
@@ -1650,7 +1574,7 @@
         "    SALES_TABLE=SALES_TABLE,\n",
         "    PROJECT_ID=PROJECT_ID,\n",
         ")\n",
-        "# execute the query (as it is a create query, there won't be any tabular output)\n",
+        "# execute the query (as it's a create query, there won't be any tabular output)\n",
         "query_job = client.query(query)\n",
         "print(query_job.result())"
       ]
@@ -2164,7 +2088,7 @@
       "source": [
         "## Executor feature in managed instances\n",
         "\n",
-        "If you are using managed instances, along the top toolbar, above your notebook, click the **Executor** button."
+        "If you're using managed instances, along the top toolbar, above your notebook, click the **Executor** button."
       ]
     },
     {
@@ -2307,7 +2231,12 @@
         "    dataset_id, delete_contents=True, not_found_ok=True\n",
         ")  # Make an API request.\n",
         "\n",
-        "print(\"Deleted dataset '{}'.\".format(dataset_id))"
+        "print(\"Deleted dataset '{}'.\".format(dataset_id))\n",
+        "\n",
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = True  # Set True for deletion\n",
+        "if delete_bucket:\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb

@@ -33,21 +33,24 @@
         "\n",
         "<table align=\"left\">\n",
         "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fblob%2Fmain%2Fnotebooks%2Fofficial%2Fworkbench%2Ffraud_detection%2Ffraud-detection-model.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
         "  </td>\n",
-        "   <td>\n",
+        "   <td style=\"text-align: center\">\n",
         "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "</table>"
@@ -74,7 +77,7 @@
         "\n",
         "This tutorial shows you how to build, deploy, and analyze predictions from a simple [Random Forest](https://en.wikipedia.org/wiki/Random_forest) model using tools like scikit-learn, Vertex AI, and the [What-IF Tool (WIT)](https://cloud.google.com/ai-platform/prediction/docs/using-what-if-tool) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.\n",
         "\n",
-        "**Note:** The What-If tool widget used in this notebook only runs in a Colab environment. It is not explicitly supported for Vertex AI user-managed notebook instances. \n",
+        "**Note:** The What-If tool widget used in this notebook only runs in a Colab environment. It isn't explicitly supported for Vertex AI user-managed notebooks instances. \n",
         "\n",
         "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
       ]
@@ -143,12 +146,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "611991f03b38"
+        "id": "d1ea81ac77f0"
       },
       "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook. "
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e5d353aa47ac"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -170,60 +180,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "e9255e3b156f"
+        "id": "16220914acc5"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "0c0b2427998a"
+        "id": "157953ab28f0"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "c587f3b9e1e9"
+        "id": "c87a2a5d7e35"
       },
       "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "435b8e413535"
+        "id": "5dccb1c8feb6"
       },
       "source": [
-        "#### Set your project ID\n",
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "-  Run `gcloud config list`\n",
-        "-  Run `gcloud projects list`\n",
-        "-  See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cc7251520a07"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c2fc3d7b6bfa"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -237,88 +267,9 @@
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# set the project id\n",
-        "! gcloud config set project $PROJECT_ID"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2e6b8b324ce1"
-      },
-      "source": [
-        "#### Region\n",
+        "! gcloud config set project $PROJECT_ID\n",
         "\n",
-        "You can also change the `REGION` variable used by Vertex AI. \n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ae43d96c4b1b"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c43a8673066"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench** \n",
-        "- Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab Instance,** uncomment and run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fbc9cd30cc4b"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd0da2c26879"
-      },
-      "source": [
-        "**3. Colab,** uncomment and run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a336a05c6149"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0461097edfa5"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "LOCATION = \"us-central1\"  # @param {type: \"string\"}"
       ]
     },
     {
@@ -360,7 +311,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -418,7 +369,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -1124,7 +1075,7 @@
         "\n",
         "# delete the bucket\n",
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil rm -r $BUCKET_URI\n",
         "\n",
         "# delete the local files\n",

notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb

@@ -32,55 +32,29 @@
         "# Churn prediction for game developers using Google Analytics 4 and BigQuery ML\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fworkbench%2Fgaming_churn_predictionchurn_prediction_for_game_developers.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\" target='_blank'>\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
+        "  </td>\n",
         "</table>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "81facaeb11b6"
-      },
-      "source": [
-        "## Table of contents\n",
-        "\n",
-        "* [Overview](#section-1)\n",
-        "* [Objective](#section-2)\n",
-        "* [Dataset](#section-3)\n",
-        "* [Costs](#section-4)\n",
-        "* [Create a BigQuery dataset](#section-5)\n",
-        "* [Explore data](#section-6)\n",
-        "* [Preparing the training data](#section-7)\n",
-        "\t* [Identifying the label for each user](#section-7-subsection-1)\n",
-        "    * [Extracting demographic data for each user](#section-7-subsection-2)\n",
-        "\t* [Extracting behavioral data for each user](#section-7-subsection-3)\n",
-        "\t* [Combining the label, demographic and behavioral data together as training data](#section-7-subsection-4)\n",
-        "* [Training the propensity model with BigQuery ML](#section-8)\n",
-        "* [Model evaluation](#section-9)\n",
-        "\t* [Confusion matrix: predicted vs actual values](#section-9-subsection-1)\n",
-        "\t* [ROC Curve](#section-9-subsection-2)\n",
-        "* [Model prediction](#section-10)  \n",
-        "* [Export predictions table to Cloud Storage](#section-11)\n",
-        "* [Clean up](#section-12)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -107,15 +81,15 @@
         "In this tutorial, you learn how to train, evaluate a propensity model in BigQuery ML.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
-        "* BigQuery.\n",
+        "* BigQuery\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "* Explore an export of Google Analytics 4 data on BigQuery.\n",
+        "* Explore the data exported from Google Analytics 4 in BigQuery.\n",
         "* Prepare the training data using demographic, behavioral data, and labels (churn/not-churn).\n",
         "* Train an XGBoost model using BigQuery ML.\n",
-        "* Evaluate a model using BigQuery ML.\n",
-        "* Make predictions on which users will churn using BigQuery ML."
+        "* Evaluate the model using BigQuery ML.\n",
+        "* Use BigQuery ML to predict which users are likely to churn."
       ]
     },
     {
@@ -127,11 +101,11 @@
         "### Dataset\n",
         "<a name=\"section-3\"></a>\n",
         "\n",
-        "This notebook uses [this public BigQuery dataset](https://console.cloud.google.com/bigquery?p=firebase-public-project&d=analytics_153293282&t=events_20181003&page=table), which contains raw event data from a real mobile gaming app called Flood It! ([Android app](https://play.google.com/store/apps/details?id=com.labpixies.flood), [iOS app](https://itunes.apple.com/us/app/flood-it!/id476943146?mt=8)). The [data schema](https://support.google.com/analytics/answer/7029846) originates from Google Analytics for Firebase, but is the same schema as [Google Analytics 4](https://support.google.com/analytics/answer/9358801); the techniques in this notebook can be applied to either Google Analytics for Firebase or Google Analytics 4 data.\n",
+        "This notebook uses [this public BigQuery dataset](https://console.cloud.google.com/bigquery?p=firebase-public-project&d=analytics_153293282&t=events_20181003&page=table), which contains raw event data from a real mobile gaming app called Flood It. ([Android app](https://play.google.com/store/apps/details?id=com.labpixies.flood), [iOS app](https://itunes.apple.com/us/app/flood-it!/id476943146?mt=8)). The [data schema](https://support.google.com/analytics/answer/7029846) originates from Google Analytics for Firebase, but is the same schema as [Google Analytics 4](https://support.google.com/analytics/answer/9358801); the techniques in this notebook can be applied to either Google Analytics for Firebase or Google Analytics 4 data.\n",
         "\n",
-        "Google Analytics 4 (GA4) uses an [event-based](https://support.google.com/analytics/answer/9322688) measurement model. Events provide insight on what is happening in an app or on a website, such as user actions, system events, or errors. Every row in the dataset is an event, with various characteristics relevant to that event stored in a nested format within the row. While Google Analytics logs many types of events already by default, developers can also customize the types of events they also wish to log.\n",
+        "Google Analytics 4 (GA4) uses an [event-based](https://support.google.com/analytics/answer/9322688) measurement model. Events offer insights into app or website activities, including user actions, system events, or errors. Each row in the dataset represents an event, with its various characteristics stored in a nested format within the row. Google Analytics logs many types of events by default, but developers can also customize and log additional event types as needed.\n",
         "\n",
-        "Note that as you cannot simply use the raw event data to train a machine learning model, this notebook shows you some important steps of how to pre-process the raw data into an appropriate format to use as training data for classification models."
+        "**Note:** You can't use raw event data directly to train a machine learning model. This notebook demonstrates some important steps to pre-process raw data into an appropriate format for training classification models."
       ]
     },
     {
@@ -160,10 +134,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "OMUaAMn091Pu"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "### Install additional packages\n"
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install required packages\n"
       ]
     },
     {
@@ -180,41 +163,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "e9255e3b156f"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab Only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "0c0b2427998a"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "435b8e413535"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "### Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "#### Set your project ID\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "-  Run `gcloud config list`\n",
-        "-  Run `gcloud projects list`\n",
-        "-  See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -226,92 +248,12 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# set the project id\n",
         "! gcloud config set project $PROJECT_ID"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2e6b8b324ce1"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. \n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ae43d96c4b1b"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c43a8673066"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench** \n",
-        "- Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab Instance,** uncomment and run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fbc9cd30cc4b"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd0da2c26879"
-      },
-      "source": [
-        "**3. Colab,** uncomment and run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a336a05c6149"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0461097edfa5"
-      },
-      "source": [
-        "**4. Service Account or other**\n",
-        "- See all the authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -331,8 +273,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"your-bucket-name-unique\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -341,7 +282,7 @@
         "id": "b72bfdf29dae"
       },
       "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+        "**If your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
       ]
     },
     {
@@ -352,38 +293,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "06571eb4063b"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "697568e92bd6"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -403,8 +313,6 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
         "import matplotlib.pyplot as plt\n",
         "from google.cloud import bigquery\n",
         "from google.cloud.bigquery import Client"
@@ -446,7 +354,7 @@
         "id": "S4qnPgig91P1"
       },
       "source": [
-        "If you are using ***Vertex AI Workbench managed notebooks instance***, every cell which starts with \"#@bigquery\" will be a SQL Query. If you are using Vertex AI Workbench user managed notebooks instance or Colab it will be a markdown cell."
+        "If you're using a ***Vertex AI Workbench managed notebooks instance***, every cell which starts with \"#@bigquery\" is a SQL Query. If you're using a ***Vertex AI Workbench user-managed notebooks instance*** or Colab, it's a markdown cell."
       ]
     },
     {
@@ -479,9 +387,9 @@
         "id": "L0JQL6mO91P2"
       },
       "source": [
-        "(**Optional**)If you are using Vertex AI Workbench managed notebooks instance, once the results from BigQuery are displayed in the above cell, click the **Query and load as DataFrame** button and execute the generated code stub to fetch the data into the current notebook as a dataframe.\n",
+        "(**Optional**)If you're using Vertex AI Workbench managed notebooks instance, once the results from BigQuery are displayed in the above cell, click the **Query and load as DataFrame** button and execute the generated code stub to fetch the data into the current notebook as a dataframe.\n",
         "\n",
-        "*Note: By default the data is loaded into a `df` variable, though this can be changed before executing the cell if required.*"
+        "**Note**: By default the data is loaded into a `df` variable, though this can be changed before executing the cell if required."
       ]
     },
     {
@@ -492,7 +400,7 @@
       },
       "outputs": [],
       "source": [
-        "dataset_id = \"bqmlga4\" + \"_\" + UUID"
+        "dataset_id = \"bqmlga4_unique\""
       ]
     },
     {
@@ -571,7 +479,7 @@
         "id": "PYBMN963Qydl"
       },
       "source": [
-        "It may be helpful to take a look at the overall schema used in Google Analytics 4. As mentioned earlier, Google Analytics 4 uses an event-based measurement model and each row in this dataset is an event. [View the complete schema and details about each column](https://support.google.com/analytics/answer/7029846). As you can see above, specific columns are nested records and contain detailed information:"
+        "It can be helpful to take a look at the overall schema used in Google Analytics 4. As mentioned earlier, Google Analytics 4 uses an event-based measurement model and each row in this dataset is an event. [View the complete schema and details about each column](https://support.google.com/analytics/answer/7029846). As you can see above, certain columns are nested records that contain the following information:"
       ]
     },
     {
@@ -659,7 +567,7 @@
         "id": "P358Jo_s8WC0"
       },
       "source": [
-        "You cannot simply use raw event data to train a machine learning model as it would not be in the right shape and format to use as training data. So in this section, you learn how to pre-process the raw data into an appropriate format to use as training data for classification models.\n"
+        "You can't simply use raw event data to train a machine learning model since it won't be in the right shape and format to use as training data. So in this section, you learn how to pre-process the raw data into an appropriate format to use as training data for classification models."
       ]
     },
     {
@@ -696,7 +604,7 @@
         "- Feature(s) for **behavioral data**\n",
         "- The actual **label** that you want to train the model to predict (for example, 1 = churned, 0 = returned)\n",
         "\n",
-        "You can train a model with only demographic data or behavioral data, but having a combination of both likely help you create a more predictive model. For this reason, in this section, you learn how to pre-process the raw data to follow this training data format."
+        "You can train a model using only demographic data or behavioral data, but combining both is likely to create a more predictive model. Therefore, in this section, you learn how to pre-process the raw data to achieve this training data format."
       ]
     },
     {
@@ -738,13 +646,13 @@
         "id": "lgqxD30Hl6FM"
       },
       "source": [
-        "There are many ways to define user churn, but for the purposes of this notebook, you predict 1-day churn as users who do not come back and use the app again after 24 hours of the user's first engagement. \n",
+        "There are many ways to define user churn, but for the purpose of this notebook, predict 1-day churn as users who don't come back and use the app again after 24 hours of the user's first engagement. \n",
         "\n",
         "In other words, after 24 hours of a user's first engagement with the app:\n",
         "- if the user _shows no event data thereafter_, the user is considered **churned**. \n",
         "- if the user _does have at least one event datapoint thereafter_, then the user is considered **returned**\n",
         "\n",
-        "You may also want to remove users who were unlikely to have ever returned anyway after spending just a few minutes with the app, which is sometimes referred to as \"bouncing\". For example, you may want to build the model only on users who spent at least 10 minutes with the app (users who did not bounce).\n",
+        "You can remove users who are unlikely to have ever returned anyway after spending just a few minutes with the app, which is sometimes referred to as \"bouncing\". For example, you can build the model only on users who spent at least 10 minutes with the app (users who didn't bounce).\n",
         "\n",
         "So your updated definition of a **churned user** for this notebook is:\n",
         "> \"any user who spent at least 10 minutes on the app, but after 24 hours from when they first engaged with the app, never used the app again\"\n"
@@ -952,7 +860,7 @@
         "id": "ulbfb8SY2fSM"
       },
       "source": [
-        "You might wonder how many of these 15k users bounced and returned? You can run the following query to check:"
+        "You can run the following query to check how many of these 15k users bounced and returned."
       ]
     },
     {
@@ -1066,7 +974,7 @@
         "id": "08a5324dac82"
       },
       "source": [
-        "There are 23% churners in the data which is not bad for training a churn prediction model. If the class imbalance seems to be high, oversampling or undersampling techniques can be considered to balance the class distribution."
+        "There are 23% churners in the data which isn't bad for training a churn prediction model. If the class imbalance seems to be high, oversampling or undersampling techniques can be considered to balance the class distribution."
       ]
     },
     {
@@ -1087,9 +995,9 @@
       "source": [
         "This section is focused on extracting the demographic information for each user. Different demographic information about the user is available in the dataset already, including `app_info`, `device`, `ecommerce`, `event_params`, and `geo`. Demographic features can help the model predict whether users on certain devices or countries are more likely to churn.\n",
         "\n",
-        "For this notebook, you can start just with `geo.country`, `device.operating_system`, and `device.language`. If you are using your own dataset and have joinable first-party data, this section is a good opportunity to add any additional attributes for each user that may not be readily available in Google Analytics 4.\n",
+        "For this notebook, you can start just with `geo.country`, `device.operating_system`, and `device.language`. If you're using your own dataset and have joinable first-party data, this section is a good opportunity to add any additional attributes for each user that aren't readily available in Google Analytics 4.\n",
         "\n",
-        "Note that a user's demographics may occasionally change (e.g. moving from one country to another). For simplicity, you just use the demographic information that Google Analytics 4 provides when the user LAST engaged with the app as indicated by `MAX(event_timestamp)`. This enables every unique user to be represented by a single row."
+        "Note that a user's demographics can occasionally change (e.g., moving from one country to another). For simplicity, you just use the demographic information that Google Analytics 4 provides when the user LAST engaged with the app as indicated by `MAX(event_timestamp)`. This enables every unique user to be represented by a single row."
       ]
     },
     {
@@ -1232,7 +1140,7 @@
       "source": [
         "Behavioral data in the raw event data spans across multiple events -- and thus rows -- per user. The goal of this section is to aggregate and extract behavioral data for each user, resulting in one row of behavioral data per unique user.\n",
         "\n",
-        "But what kind of behavioral data you need to prepare? Since the end goal of this notebook is to predict, based on a user's activity within the first 24 hours since app installation, whether that user will churn or return thereafter, then you want to use behavioral data from the first 24 hours in your training data. Later on, you can also extract some extra time-related features from `user_first_engagement`, such as the month or day of the first engagement.\n",
+        "The ultimate objective of this notebook is to predict whether a user will churn or continue using the app based on their activity within the first 24 hours since installation. Therefore, it's crucial to include behavioral data from the first 24 hours in your training data. Additionally, you can derive additional time-related features from `user_first_engagement`, such as the month or day of the first engagement, to enhance your model later on.\n",
         "\n",
         "Google Analytics automatically collects [specific events](https://support.google.com/analytics/answer/6317485) that you can use to analyze behavior. In addition, there are recommended [events for games](https://support.google.com/analytics/answer/6317494). \n",
         "\n",
@@ -1318,7 +1226,7 @@
       "source": [
         "In SQL, you can aggregate the behavioral data by calculating the total number of times when each of the above `event_names` occurred in the dataset per user.\n",
         "\n",
-        "If you are using your own dataset, you may have different event types that you can aggregate and extract. Your app may be sending very different `event_names` to Google Analytics so be sure to use events suitable to your scenario."
+        "If you're using your own dataset, you can have different event types that you can aggregate and extract. Your app can be sending very different `event_names` to Google Analytics, so be sure to use events suitable to your scenario."
       ]
     },
     {
@@ -1449,7 +1357,7 @@
         "id": "ed161899b055"
       },
       "source": [
-        "In this section, you can now combine these three intermediary views (label, demographic data, and behavioral data) into the final training dataset. Here you can also specify `bounced = 0`, in order to limit the training data only to users who did not \"bounce\" within the first 10 minutes of using the app."
+        "In this section, you can now combine *label*, *demographic data*, and *behavioral data* views into the final training dataset. Here you can also specify `bounced = 0`, in order to limit the training data only to users who didn't \"bounce\" within the first 10 minutes of using the app."
       ]
     },
     {
@@ -1647,7 +1555,7 @@
         "id": "ec1a7945f3cb"
       },
       "source": [
-        "In this section, using the training data you prepared, you now train machine learning models in SQL using BigQuery ML."
+        "In this section, train machine learning models in SQL using BigQuery ML with the prepared training data."
       ]
     },
     {
@@ -1674,7 +1582,7 @@
         "id": "1bfabaccff00"
       },
       "source": [
-        "The following code trains an XGBoost model. This may take several minutes.\n",
+        "The following code trains an XGBoost model. This can take several minutes.\n",
         "\n",
         "For more information on the default hyperparameters used, see [The CREATE MODEL statement for boosted tree models using XGBoost](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree)."
       ]
@@ -1760,7 +1668,7 @@
       "source": [
         "To evaluate the model, you can run [`ML.EVALUATE`](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate) on a model that has finished training to inspect some of the metrics.\n",
         "\n",
-        "The metrics are based on the test sample data that was automatically split during model creation ([see the CREATE MODEL documentation for more information](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method))."
+        "The metrics are based on the test sample data that's automatically split during model creation ([see the CREATE MODEL documentation for more information](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method))."
       ]
     },
     {
@@ -1839,7 +1747,7 @@
         "id": "dc448a895918"
       },
       "source": [
-        "In addition to model evaluation metrics, you may also want to use a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels.\n",
+        "In addition to model evaluation metrics, use a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels.\n",
         "\n",
         "With the rows indicating the actual labels, and the columns as the predicted labels, the resulting format for ML.CONFUSION_MATRIX for binary classification looks like:\n",
         "\n",
@@ -2254,7 +2162,7 @@
         "print(\"Deleted dataset '{}'.\".format(dataset_id))\n",
         "\n",
         "delete_bucket = False\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "if delete_bucket:\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }

notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb

@@ -32,46 +32,27 @@
         "# Analysis of pricing optimization on CDM Pricing Data\n",
         "\n",
         "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fworkbench%2Fpricing_optimization%2Fpricing-optimization.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
         "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
         "    </a>\n",
-        "  </td>                                                                                               \n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd1268747961"
-      },
-      "source": [
-        "## Table of contents\n",
-        "* [Overview](#section-1)\n",
-        "* [Objective](#section-2)\n",
-        "* [Dataset](#section-3)\n",
-        "* [Costs](#section-4)\n",
-        "* [Create a BigQuery dataset](#section-5)\n",
-        "* [Load the dataset from Cloud Storage](#section-6)\n",
-        "* [Data analysis](#section-7)\n",
-        "* [Preprocess the data for training](#section-8)\n",
-        "* [Train the model using BigQuery ML](#section-9)\n",
-        "* [Generate forecasts from the model](#section-10)\n",
-        "* [Interpret the results to choose the best price](#section-11)\n",
-        "* [Clean up](#section-12)\n"
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>\n"
       ]
     },
     {
@@ -85,7 +66,7 @@
         "\n",
         "This notebook demonstrates analysis of pricing optimization on [CDM Pricing Data](https://github.com/trifacta/trifacta-google-cloud/tree/main/design-pattern-pricing-optimization) and automating the workflow using Vertex AI Workbench managed notebooks.\n",
         "\n",
-        "*Note: This notebook file was developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.*\n",
+        "**Note**: This notebook file is developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.\n",
         "\n",
         "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml#machine_learning_directly_in)."
       ]
@@ -97,9 +78,8 @@
       },
       "source": [
         "### Objective\n",
-        "<a name=\"section-2\"></a>\n",
         "\n",
-        "The objective of this notebook is to build a pricing optimization model using BigQuery ML. The following steps have been followed:  \n",
+        "The objective of this notebook is to build a pricing optimization model using BigQuery ML.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
@@ -156,10 +136,19 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "2bed1491312f"
+        "id": "61RBz8LLbxCR"
       },
       "source": [
-        "### Install additional packages\n"
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No17Cw5hgx12"
+      },
+      "source": [
+        "### Install Vertex AI SDK for Python and other required packages\n"
       ]
     },
     {
@@ -176,41 +165,80 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "e9255e3b156f"
+        "id": "R5Xep4W9lq-Z"
       },
       "source": [
-        "### Colab Only: Uncomment the following cell to restart the kernel"
+        "### Restart runtime (Colab only)\n",
+        "\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "0c0b2427998a"
+        "id": "XRvKdaPDTznN"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import sys\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "435b8e413535"
+        "id": "SbmM4z7FOBpM"
       },
       "source": [
-        "### Before you begin\n",
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dmWOrTJ3gx13"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)\n",
         "\n",
-        "#### Set your project ID\n",
+        "Authenticate your environment on Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NyKGtVQjgx13"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
         "\n",
-        "**If you don't know your project ID**, try the following:\n",
-        "-  Run `gcloud config list`\n",
-        "-  Run `gcloud projects list`\n",
-        "-  See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
       ]
     },
     {
@@ -222,121 +250,10 @@
       "outputs": [],
       "source": [
         "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# set the project id\n",
-        "! gcloud config set project $PROJECT_ID"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2e6b8b324ce1"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. \n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ae43d96c4b1b"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c43a8673066"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
-        "\n",
-        "**1. Vertex AI Workbench** \n",
-        "- Do nothing as you are already authenticated.\n",
-        "\n",
-        "**2. Local JupyterLab Instance,** uncomment and run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fbc9cd30cc4b"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd0da2c26879"
-      },
-      "source": [
-        "**3. Colab,** uncomment and run:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a336a05c6149"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0461097edfa5"
-      },
-      "source": [
-        "**4. Service Account or other**\n",
-        "- See all the authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "06571eb4063b"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "697568e92bd6"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "! gcloud config set project {PROJECT_ID}"
       ]
     },
     {
@@ -380,7 +297,7 @@
       },
       "outputs": [],
       "source": [
-        "DATASET = \"pricing_optimization\" + \"_\" + UUID  # set the BigQuery dataset-id\n",
+        "DATASET = \"pricing_optimization_unique\"  # set the BigQuery dataset-id\n",
         "TRAINING_DATA_TABLE = (\n",
         "    \"training_data_table\"  # set the BigQuery table-id to store the training data\n",
         ")"
@@ -402,7 +319,7 @@
         "id": "3a063f530682"
       },
       "source": [
-        "If you are using ***Vertex AI Workbench managed notebooks instance***, every cell which starts with \"#@bigquery\" will be a SQL Query. If you are using Vertex AI Workbench user managed notebooks instance or Colab it will be a markdown cell."
+        "If you're using ***Vertex AI Workbench managed notebooks instance***, every cell which starts with \"#@bigquery\" is a SQL Query. If you're using a Vertex AI Workbench user-managed notebooks instance or Colab, it's a markdown cell."
       ]
     },
     {
@@ -516,7 +433,7 @@
         "id": "7b98d5f09842"
       },
       "source": [
-        "You build a forecast model on this data and thus determine the best price for a product. For this type of model, you will not be using many fields: only the sales and price related ones. For the current execrcise, focus on the following fields:\n",
+        "Build a forecast model on this data to determine the best price for a product. For this type of model, you need only a few fields: the sales and price related ones. For the current exercise, focus is on the following fields:\n",
         "\n",
         "- `Product_ID`\n",
         "- `Customer_Hierarchy`\n",
@@ -526,9 +443,9 @@
         "- `Net_Sales`\n",
         "\n",
         "## Data Analysis\n",
-        "<a name=\"section-7\"></a>\n",
         "\n",
-        "First, explore the data and distributions.\n",
+        "\n",
+        "First, explore the data and it's distributions.\n",
         "\n",
         "#### Select the required columns from the dataframe."
       ]
@@ -555,7 +472,7 @@
         "id": "c2d25505947c"
       },
       "source": [
-        "Create a view to extract only required columns"
+        "Create a view to extract only the required columns."
       ]
     },
     {
@@ -584,7 +501,7 @@
         "id": "21ec9e50ba23"
       },
       "source": [
-        "See the data stored in the view"
+        "Inspect data in the `required_columns` view"
       ]
     },
     {
@@ -654,7 +571,7 @@
         "id": "ae528c679547"
       },
       "source": [
-        "Change Fiscal_Date data type from datetime to date and store resulting entire data in a view"
+        "Change the `Fiscal_Date` data type from datetime to date and store the resulting data in a view."
       ]
     },
     {
@@ -684,7 +601,7 @@
         "id": "0c4f4ac078fb"
       },
       "source": [
-        "See the data in required_columns_final view"
+        "Inspect data in the `required_columns_final` view"
       ]
     },
     {
@@ -850,9 +767,9 @@
         "id": "8d15f712edf2"
       },
       "source": [
-        "You follow three steps to check percentage changes in the orders based on the percentage changes in the price\n",
+        "Execute the following steps to check percentage changes in the orders based on the percentage changes in their price.\n",
         "\n",
-        "**Step 1**.First, you create a table that has one line each time the price of a product has changed, with information about that particular product pricing like how many items were ordered with each price and the total net sales associated with that price.\n"
+        "**Step 1**: First, generate a table that records each instance of a price change for a product. Include details such as the number of items ordered at each price point and the total net sales associated with that price."
       ]
     },
     {
@@ -940,10 +857,10 @@
       "source": [
         "**Step 2**. Next, with the temporary table in place, you can calculate the price change across SKUs\n",
         "\n",
-        "Ex: (previous_list-list_price_converged)/nullif(previous_list,0)*100\n",
+        "Ex: (`previous_list`-`list_price_converged`)/nullif(`previous_list`,0)*100\n",
         "\n",
-        "**Step 3**. Next, you can calculate the total_ordered_pieces change across SKUs\n",
-        "(total_ordered_pieces-previous_total_ordered_pieces)/nullif(previous_total_ordered_pieces,0)*100 "
+        "**Step 3**. Next, you can calculate the `total_ordered_pieces` change across SKUs\n",
+        "(`total_ordered_pieces`-`previous_total_ordered_pieces`)/nullif(`previous_total_ordered_pieces`,0)*100 "
       ]
     },
     {
@@ -969,7 +886,7 @@
         "id": "e7377794e288"
       },
       "source": [
-        "Now you have dataframe(df_for_plot) which has price_change_perc, order_change_perc fields"
+        "Now you have a dataframe(`df_for_plot`) which has `price_change_perc`, `order_change_perc`fields"
       ]
     },
     {
@@ -1013,7 +930,7 @@
         "id": "7ae3e75de755"
       },
       "source": [
-        "Finally, you can analyze what happens after a price has been changed by looking at the relationship between each price change and the total amount of items that were ordered:"
+        "Finally, analyze the relationship between each price change and the total number of items ordered."
       ]
     },
     {
@@ -1044,10 +961,9 @@
       "source": [
         "For most of the products, the percentage change in orders are high where the percentage changes in the prices are low. This suggests that too much change in the prices can affect the number of orders. \n",
         "\n",
-        "**Note**: There seem to be some outliers in the data as percentage changes greater than 800 are found. In the current exercise, do not take any manual measures to deal with outliers as you will create a BigQuery ML timeseries model that already deals with outliers.\n",
+        "**Note**: The data contains outliers, with percentage changes exceeding 800. In the current exercise, you can refrain from manually handling outliers, as the BigQuery ML timeseries model is designed to manage outliers automatically.\n",
         "\n",
         "## Preprocess the data for training\n",
-        "<a name=\"section-8\"></a>\n",
         "\n",
         "#### Check which `Product_ID`'s  have the maximum orders."
       ]
@@ -1058,7 +974,7 @@
         "id": "fad58b6d45a4"
       },
       "source": [
-        "Create a view which stores amount of orders for for each product based on Customer_Hierarchy"
+        "Create a view that stores the number of orders for each product categorized by `Customer_Hierarchy`."
       ]
     },
     {
@@ -1131,10 +1047,10 @@
       "source": [
         "#### Select top products in each Customer_Hierarchy\n",
         "\n",
-        "Below is a example to show how you find out top products in each Customer_Hierarchy\n",
+        "Below is an example demonstrating how to identify the top products within each `Customer_Hierarchy`.\n",
         "\n",
         "Example:\n",
-        "Assume at first total_orders view is\n",
+        "Assume that the `total_orders` view is\n",
         "\n",
         "<table>\n",
         "    <tr>\n",
@@ -1170,8 +1086,8 @@
         "    </tr>\n",
         "   \n",
         "</table>    \n",
-        "For this first we partition total_orders view by Customer_Hierarchy and ORDER BY Invoiced_quantity_in_Pieces in descending order.\n",
-        "After applying partion it becomes  \n",
+        "\n",
+        "After partitioning the `total_orders` view by `Customer_Hierarchy` and ordering by `Invoiced_quantity_in_Pieces` in descending order, it becomes\n",
         "\n",
         "<table>\n",
         "    <tr>\n",
@@ -1207,9 +1123,7 @@
         "    </tr>\n",
         "</table>   \n",
         "\n",
-        "Now for every Customer_Hierarchy, Invoiced_quantity_in_Pieces will be in descending order.    \n",
-        "Now we apply ROW_NUMBER function to above table \n",
-        "Now it becomes\n",
+        "Now, for every `Customer_Hierarchy`, `Invoiced_quantity_in_Pieces` is arranged in descending order. Then, apply the `ROW_NUMBER` function to the table.\n",
         "\n",
         "<table>\n",
         "    <tr>\n",
@@ -1252,7 +1166,7 @@
         "    </tr>\n",
         "</table>   \n",
         "\n",
-        "(For unique Customer_Hierarchy number starts from 1)\n"
+        "(Each unique `Customer_Hierarchy` is assigned a number starting from 1.)\n"
       ]
     },
     {
@@ -1291,7 +1205,7 @@
         "id": "a0092c1d6277"
       },
       "source": [
-        "As you can see if you take Customer_Hierarchy paper, Invoiced_quantity_in_Pieces is in descending order and rowNumber starts from 1 "
+        "As observed, when arranging by `Customer_Hierarchy`, `Invoiced_quantity_in_Pieces` is in descending order, with rowNumber starting from 1"
       ]
     },
     {
@@ -1311,7 +1225,7 @@
         "id": "d3cbe5e74137"
       },
       "source": [
-        "We want row for which Invoiced_quantity_in_Pieces is highest in each Customer_Hierarchy, so selecting rowNumber 1"
+        "Select the row where `Invoiced_quantity_in_Pieces` is the highest within each Customer_Hierarchy, so choose the row with rowNumber 1."
       ]
     },
     {
@@ -1383,7 +1297,7 @@
         "id": "b82f4e3dca26"
       },
       "source": [
-        "First from required_columns_final view we select only rows that have our desired product id and customer hierarchy"
+        "First, select rows from the `required_columns_final` view that have your desired product ID and customer hierarchy."
       ]
     },
     {
@@ -1410,7 +1324,7 @@
         "id": "fb87696b852e"
       },
       "source": [
-        "Then we plot various prices available for these `Product_ID`s."
+        "Then plot various prices available for these `Product_ID`s."
       ]
     },
     {
@@ -1545,7 +1459,7 @@
         "id": "f023af578c0f"
       },
       "source": [
-        "In the publishing category, `Product_ID` `SKU 8` and `SKU 17` are less than or equal to two different prices in the entire data and so you exclude them and consider the rest for building the forecast model. The idea here is to train a forecast model on the timeseries data for products with different prices.\n",
+        "In the publishing category, exclude `Product_ID`, `SKU 8`, and `SKU 17`, as they have fewer than or equal to two different prices in the entire dataset. Instead, consider the remaining products for building the forecast model on the timeseries data with varying prices.\n",
         "\n",
         "#### Join the data for all the `Product_ID`s into one dataframe and remove duplicate records."
       ]
@@ -1799,7 +1713,7 @@
         "id": "67ff3acc74a5"
       },
       "source": [
-        "Based on the plots for price vs. the average forecasted orders, it can be said that to use the maximum orders, each of the considered `Product_ID`s can follow the below prices:\n",
+        "Based on the plots for price vs. the average forecasted orders, the following prices can be recommended to maximize orders for each of the considered `Product_IDs`:\n",
         "\n",
         "- SKU 107's price range can be from 4.44 - 4.73 units\n",
         "- SKU 140's price can be 1.95 units\n",