Merge branch 'main' into batch_custom_text

add back pip install of xai sdk (#935 )
Add automl regression and classification with model evaluation (#911 )
2026-09-26 22:51:56 +00:00 · 2022-09-09 08:44:25 -04:00 · 2022-09-09 01:02:56 +01:00 · 2022-09-08 14:42:04 -07:00 · 2022-09-08 14:00:13 -07:00 · 2022-09-08 20:21:03 +00:00
30 changed files with 14754 additions and 280 deletions
@@ -1,5 +1,6 @@
 *  @vertex-ai-samples-contributors  @GoogleCloudPlatform/cloudml-samples-owners
 /tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk	@yinghsienwu
+/pytorch_pre_built_images_deployment @googleapis/vertex-prediction-team
 /pytorch_text_classification_using_vertex_sdk_and_gcloud  @RajeshThallam
 /pytorch_text_classification_using_vertex_sdk_and_gcloud		@RajeshThallam @ultrons
 /sklearn_text_classification_from_script_using_vertex_sdk       @maxhardt
@@ -2,4 +2,5 @@ cpr_model_server.py
 entrypoint.py
 state_dict.pth
 config.json
-**/__pycache__
+**/__pycache__
+!testdata/**
@@ -2,7 +2,7 @@

 ## About CPR

-CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/custom-prediction-routine/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application. 
+CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application. 

 ## Using this example

@@ -34,6 +34,23 @@ Finally, install the Python modules required to build and run the model server:
 pip install -r requirements.txt
 ```

+### Auth
+
+This example uses Google Cloud Storage for hosting model artifacts and Artifact Registry to store the container image. 
+You'll need to authorize yourself before you can interact with these.
+
+First, log in to GCP with application default credentials:
+```sh
+gcloud auth application-default login
+```
+
+Next, if you haven't done so already, set up the [gcloud credential helper](https://cloud.google.com/artifact-registry/docs/docker/authentication)
+for the Artifact Registry region where you intend to host the image.  
+```
+gcloud auth configure-docker <region>-docker.pkg.dev
+```
+
+
 ### Predictor

 The `TimmPredictor` class in `timm_serving/predictor.py` implements most of the important logic for the server.
@@ -60,9 +60,9 @@ class CPRConfig(object):
    image: str = "timm_predictor:latest"
    artifact_local_dir: str = ""
    region: str = "us-central1"
-    project_id: str = "samthrasher-experimental"
+    project_id: str = "<your project ID here>"
    repository: str = "cpr-images"
-    artifact_gcs_dir: str = "gs://samthrasher-cpr-example/timm-vit224/"
+    artifact_gcs_dir: str = "gs://<your bucket ID here>/timm-vit224/"
    model_name: str = ""
    endpoint_name: str = ""
    machine_type: str = "n1-standard-2"
@@ -5,4 +5,4 @@ timm==0.5.4
 smart_open==6.0.0

 google-cloud-storage>=1.26.0,<2.0.0dev
-google-cloud-aiplatform[prediction] @ git+https://github.com/googleapis/python-aiplatform.git@custom-prediction-routine
+google-cloud-aiplatform[prediction]>=1.16.0
@@ -70,7 +70,10 @@ class PredictorUnitTests(absltest.TestCase):
    def setUp(self):
        super().setUp()
        self.config = CPRConfig()
-        self.config.load()
+        try:
+            self.config.load()
+        except FileNotFoundError:
+            logging.info("No saved config file found, using default values.")
        self.predictor = predictor.TimmPredictor()

    def test_load_from_saved_state_dict_ok(self):
@@ -170,7 +173,10 @@ class ServerEndToEndTests(absltest.TestCase):
    def setUp(self):
        super().setUp()
        self.config = CPRConfig()
-        self.config.load()
+        try:
+            self.config.load()
+        except FileNotFoundError:
+            logging.info("No saved config file found, using default values.")
        self.local_model = cpr.LocalModel(
            serving_container_spec=aiplatform.gapic.ModelContainerSpec(
                image_uri=self.config.image
@@ -0,0 +1 @@
+blah
@@ -0,0 +1,30 @@
+# PyTorch Deployment on Google Cloud: Text Classification
+
+**This is an Experimental release**, covered by the Pre-GA Offerings Terms of your Google Cloud Platform [Terms of Service](https://cloud.google.com/terms).
+
+Experiments are focused on validating a prototype and are not guaranteed to be released. 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.
+
+**Kindly drop us a note before you run any scale tests.**
+
+**Do not hesitate to contact vertexai-prediction-preview-feedback@google.com if you have any questions or run into any issues.**
+
+The projects need to be added to the allowlist in order to deploy PyTorch models using Vertex AI Prediction pre-built PyTorch images. If you are interested in the feature, please send an email to vertexai-prediction-preview-feedback@google.com to provide your project numbers OR project ids.
+
+## Overview
+
+In the PyTorch on Google Cloud series of blog posts, we aim to share how to deploy PyTorch models at scale on [Vertex AI](https://cloud.google.com/vertex-ai).
+
+This tutorial on text classification shows how to deploy a PyTorch based text classification model on [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/client-libraries#python) using Vertex SDK and [`gcloud ai`](https://cloud.google.com/sdk/gcloud/reference/beta/ai).
+
+## Notebooks
+
+| <h4>Notebook</h4> | <h4>Description</h4>                       |
+| :-------- | :------- |
+| [pytorch-text-classification-vertex-ai-deploy.ipynb](./pytorch-text-classification-vertex-ai-deploy.ipynb) | Notebook to show deploying a PyTorch model on Vertex AI |
+
+## Folders
+
+
+| <h4>Folder Name</h4> | <h4>Description</h4>                       |
+| :-------- | :------- |
+| [`predictor`](./predictor) | Folder with custom prediction handler to deploy a PyTorch model to Vertex Prediction. In the [notebook](./pytorch-text-classification-vertex-ai-deploy.ipynb), this folder is used for deploying a PyTorch model on Vertex AI using Vertex Prediction pre-built PyTorch images |
@@ -0,0 +1,91 @@
+
+import os
+import json
+import logging
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from ts.torch_handler.base_handler import BaseHandler
+
+logger = logging.getLogger(__name__)
+
+
+class TransformersClassifierHandler(BaseHandler):
+    """
+    The handler takes an input string and returns the classification text 
+    based on the serialized transformers checkpoint.
+    """
+    def __init__(self):
+        super(TransformersClassifierHandler, self).__init__()
+        self.initialized = False
+
+    def initialize(self, ctx):
+        """ Loads the model.pt file and initialized the model object.
+        Instantiates Tokenizer for preprocessor to use
+        Loads labels to name mapping file for post-processing inference response
+        """
+        self.manifest = ctx.manifest
+
+        properties = ctx.system_properties
+        model_dir = properties.get("model_dir")
+        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+
+        # Read model serialize/pt file
+        serialized_file = self.manifest["model"]["serializedFile"]
+        model_pt_path = os.path.join(model_dir, serialized_file)
+        if not os.path.isfile(model_pt_path):
+            raise RuntimeError("Missing the model.pt or pytorch_model.bin file")
+        
+        # Load model
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+        self.model.to(self.device)
+        self.model.eval()
+        logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))
+        
+        # Ensure to use the same tokenizer used during training
+        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+        # Read the mapping file, index to object name
+        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
+
+        if os.path.isfile(mapping_file_path):
+            with open(mapping_file_path) as f:
+                self.mapping = json.load(f)
+        else:
+            logger.warning('Missing the index_to_name.json file. Inference output will default.')
+            self.mapping = {"0": "Negative",  "1": "Positive"}
+
+        self.initialized = True
+
+    def preprocess(self, data):
+        """ Preprocessing input request by tokenizing
+            Extend with your own preprocessing steps as needed
+        """
+        text = data[0].get("data")
+        if text is None:
+            text = data[0].get("body")
+        sentences = text.decode('utf-8')
+        logger.info("Received text: '%s'", sentences)
+
+        # Tokenize the texts
+        tokenizer_args = ((sentences,))
+        inputs = self.tokenizer(*tokenizer_args,
+                                padding='max_length',
+                                max_length=128,
+                                truncation=True,
+                                return_tensors = "pt")
+        return inputs
+
+    def inference(self, inputs):
+        """ Predict the class of a text using a trained transformer model.
+        """
+        prediction = self.model(inputs['input_ids'].to(self.device))[0].argmax().item()
+
+        if self.mapping:
+            prediction = self.mapping[str(prediction)]
+
+        logger.info("Model predicted: '%s'", prediction)
+        return [prediction]
+
+    def postprocess(self, inference_output):
+        return inference_output
@@ -0,0 +1,5 @@
+
+{
+    "0": "Negative", 
+    "1": "Positive"
+}
@@ -82,9 +82,11 @@
        "The steps performed include:\n",
        "\n",
        "- Download a pretrained image classification model from TensorFlow Hub.\n",
+        "- Upload the TensorFlow Hub model as a `Vertex AI Model` resource.\n",
+        "- Make batch prediction with raw (uncompressed) image data to the `Model` resource, in JSONL format.\n",
        "- Create a serving function to receive compressed image data, and output decomopressed preprocessed data for the model input.\n",
        "- Upload the TensorFlow Hub model and serving function as a `Vertex AI Model` resource.\n",
-        "- Make batch prediction to the `Model` resource, in JSONL format.\n",
+        "- Make batch prediction with compressed image data to the `Model` resource, in File-List format.\n",
        "\n",
        "There is one key difference between using batch prediction and using online prediction:\n",
        "\n",
@@ -697,7 +699,7 @@
      "source": [
        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
        "\n",
-        "Finally, you upload the model artifacts from the TFHub model and serving function 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 a `Vertex AI Model` resource using the method `upload()`, with the following parameters:\n",
        "\n",
        "- `display_name`: A human readable name for the `Model` resource.\n",
        "- `artifact_uri`: The Cloud Storage location of the model package.\n",
@@ -739,13 +741,16 @@
        "\n",
        "### Input format for batch prediction jobs\n",
        "\n",
-        "The batch server accepts the following input formats:\n",
+        "The batch server accepts the following input formats for custom image models:\n",
        "\n",
        "- JSONL\n",
-        "- CSV\n",
-        "- TFRecords\n",
        "- File-List\n",
-        "- BigQuery table\n",
+        "\n",
+        "### Output format for batch prediction jobs\n",
+        "\n",
+        "The batch server accepts the following output formats for custom image models:\n",
+        "\n",
+        "- JSONL\n",
        "\n",
        "### Pivot format\n",
        "\n",
@@ -1035,6 +1040,376 @@
        "            break"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "12ed21da6f1f"
+      },
+      "source": [
+        "#### Delete the batch prediction job\n",
+        "\n",
+        "You can delete your `Vertex AI Batch Prediction` job with the `delete()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "286a90d9b6e7"
+      },
+      "outputs": [],
+      "source": [
+        "batch_prediction_job.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6cdd298348a1"
+      },
+      "source": [
+        "#### Delete the model\n",
+        "\n",
+        "You can delete your `Vertex AI Model` resource with the `delete()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4c9484c41c39"
+      },
+      "outputs": [],
+      "source": [
+        "model.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68f6562b12cb"
+      },
+      "source": [
+        "## Image models with serving functions\n",
+        "\n",
+        "Previously, your model server took input as a 3-dimensional array. Image models typically take a compressed image and use a serving function fused to the model to decompress the compressed image into a 3-dimensional array, and other preprocesing -- such as normalizing the pixel values.\n",
+        "\n",
+        "Next, you upload your custom image model as a `Vertex AI Model` resource with a serving function. During upload, you define a serving function to convert data to the format your model expects. If you send encoded data to Vertex AI, your serving function ensures that the data is decoded on the model server before it is passed as input to your model.\n",
+        "\n",
+        "### How does the serving function work\n",
+        "\n",
+        "When you send a request to an online prediction server, the request is received by a HTTP server. The HTTP server extracts the prediction request from the HTTP request content body. The extracted prediction request is forwarded to the serving function. For Google pre-built prediction containers, the request content is passed to the serving function as a `tf.string`.\n",
+        "\n",
+        "The serving function consists of two parts:\n",
+        "\n",
+        "- `preprocessing function`:\n",
+        "  - Converts the input (`tf.string`) to the input shape and data type of the underlying model (dynamic graph).\n",
+        "  - Performs the same preprocessing of the data that was done during training the underlying model -- e.g., normalizing, scaling, etc.\n",
+        "- `post-processing function`:\n",
+        "  - Converts the model output to format expected by the receiving application -- e.q., compresses the output.\n",
+        "  - Packages the output for the the receiving application -- e.g., add headings, make JSON object, etc.\n",
+        "\n",
+        "Both the preprocessing and post-processing functions are converted to static graphs which are fused to the model. The output from the underlying model is passed to the post-processing function. The post-processing function passes the converted/packaged output back to the HTTP server. The HTTP server returns the output as the HTTP response content.\n",
+        "\n",
+        "One consideration you need to consider when building serving functions for TF.Keras models is that they run as static graphs. That means, you cannot use TF graph operations that require a dynamic graph. If you do, you will get an error during the compile of the serving function which will indicate that you are using an EagerTensor which is not supported."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "serving_function_image:post"
+      },
+      "source": [
+        "### Serving function for image data\n",
+        "\n",
+        "#### Preprocessing\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, and then preprocessed to match the model input requirements, before it is passed as input to the deployed model.\n",
+        "\n",
+        "To resolve this, you 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",
+        "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",
+        "\n",
+        "- `io.decode_jpeg`- Decompresses the JPG image which is returned as a Tensorflow tensor with three channels (RGB).\n",
+        "- `image.convert_image_dtype` - Changes integer pixel values to float 32, and rescales pixel data between 0 and 1.\n",
+        "- `image.resize` - Resizes the image to match the input shape for the model.\n",
+        "\n",
+        "At this point, the data can be passed to the model (`m_call`), via a concrete function. The serving function is a static graph, while the model is a dynamic graph. The concrete function performs the tasks of marshalling the input data from the serving function to the model, and marshalling the prediction result from the model back to the serving function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "serving_function_image"
+      },
+      "outputs": [],
+      "source": [
+        "CONCRETE_INPUT = \"numpy_inputs\"\n",
+        "\n",
+        "\n",
+        "def _preprocess(bytes_input):\n",
+        "    decoded = tf.io.decode_jpeg(bytes_input, channels=3)\n",
+        "    decoded = tf.image.convert_image_dtype(decoded, tf.float32)\n",
+        "    resized = tf.image.resize(decoded, size=(224, 224))\n",
+        "    return resized\n",
+        "\n",
+        "\n",
+        "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
+        "def preprocess_fn(bytes_inputs):\n",
+        "    decoded_images = tf.map_fn(\n",
+        "        _preprocess, bytes_inputs, dtype=tf.float32, back_prop=False\n",
+        "    )\n",
+        "    return {\n",
+        "        CONCRETE_INPUT: decoded_images\n",
+        "    }  # User needs to make sure the key matches model's input\n",
+        "\n",
+        "\n",
+        "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
+        "def serving_fn(bytes_inputs):\n",
+        "    images = preprocess_fn(bytes_inputs)\n",
+        "    prob = m_call(**images)\n",
+        "    return prob\n",
+        "\n",
+        "\n",
+        "m_call = tf.function(tfhub_model.call).get_concrete_function(\n",
+        "    [tf.TensorSpec(shape=[None, 224, 224, 3], dtype=tf.float32, name=CONCRETE_INPUT)]\n",
+        ")\n",
+        "\n",
+        "tf.saved_model.save(tfhub_model, MODEL_DIR, signatures={\"serving_default\": serving_fn})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "serving_function_signature:image"
+      },
+      "source": [
+        "## 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",
+        "\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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "serving_function_signature:image"
+      },
+      "outputs": [],
+      "source": [
+        "loaded = tf.saved_model.load(MODEL_DIR)\n",
+        "\n",
+        "serving_input = list(\n",
+        "    loaded.signatures[\"serving_default\"].structured_input_signature[1].keys()\n",
+        ")[0]\n",
+        "print(\"Serving function input:\", serving_input)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8ce91147c93"
+      },
+      "source": [
+        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
+        "\n",
+        "Finally, you upload the model artifacts from the TFHub model and serving function into a `Vertex AI Model` resource using the method `upload()`, with the following parameters:\n",
+        "\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",
+        "\n",
+        "*Note:* When you upload the model artifacts to a `Vertex AI Model` resource, you specify the corresponding deployment container image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ad61e1429512"
+      },
+      "outputs": [],
+      "source": [
+        "model = aip.Model.upload(\n",
+        "    display_name=\"resnet_\" + UUID,\n",
+        "    artifact_uri=MODEL_DIR,\n",
+        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
+        ")\n",
+        "\n",
+        "print(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "make_prediction"
+      },
+      "source": [
+        "### Make a batch prediction request\n",
+        "\n",
+        "Previously, you formatted the batch prediction instances using JSONL as 3-dimensional arrays. This time, you format the batch prediction instances as a file-list, where each line in the file is a Cloud Storage location of a compressed image."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1e29665076f"
+      },
+      "source": [
+        "#### Prepare data for batch prediction\n",
+        "\n",
+        "\n",
+        "Next, you format the same batch prediction request instances as a File-List format."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "becaaf02edde"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow.python.ops.numpy_ops.np_config as np_config\n",
+        "\n",
+        "np_config.enable_numpy_behavior()\n",
+        "\n",
+        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
+        "\n",
+        "n = 1\n",
+        "for image in x_test[:10]:\n",
+        "    c_image = tf.io.encode_jpeg(image)\n",
+        "    with tf.io.gfile.GFile(BUCKET_URI + f\"/images/image{n}.jpg\", \"wb\") as f:\n",
+        "        f.write(c_image.numpy())\n",
+        "    n += 1\n",
+        "\n",
+        "BATCH_PREDICTION_INSTANCES_FILE = \"batch_prediction_instances.txt\"\n",
+        "\n",
+        "BATCH_PREDICTION_GCS_SOURCE = (\n",
+        "    BUCKET_URI + \"/batch_prediction_instances/\" + BATCH_PREDICTION_INSTANCES_FILE\n",
+        ")\n",
+        "\n",
+        "with tf.io.gfile.GFile(BATCH_PREDICTION_GCS_SOURCE, \"w\") as f:\n",
+        "    for n in range(1, 11):\n",
+        "        f.write(BUCKET_URI + f\"/images/image{n}.jpg\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "send_prediction_request:image"
+      },
+      "source": [
+        "### Send the prediction request\n",
+        "\n",
+        "\n",
+        "To make a batch prediction request, call the model object's `batch_predict` method with the following parameters: \n",
+        "- `instances_format`: The format of the batch prediction request file: \"jsonl\", \"csv\", \"bigquery\", \"tf-record\", \"tf-record-gzip\" or \"file-list\"\n",
+        "- `prediction_format`: The format of the batch prediction response file: \"jsonl\", \"csv\", \"bigquery\", \"tf-record\", \"tf-record-gzip\" or \"file-list\"\n",
+        "- `job_display_name`: The human readable name for the prediction job.\n",
+        " - `gcs_source`: A list of one or more Cloud Storage paths to your batch prediction requests.\n",
+        "- `gcs_destination_prefix`: The Cloud Storage path that the service will write the predictions to.\n",
+        "- `model_parameters`: Additional filtering parameters for serving prediction results.\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",
+        "- `starting_replica_count`: The number of compute instances to initially provision.\n",
+        "- `max_replica_count`: The maximum number of compute instances to scale to. In this tutorial, only one instance is provisioned.\n",
+        "\n",
+        "### Compute instance scaling\n",
+        "\n",
+        "You can specify a single instance (or node) to process your batch prediction request. This tutorial uses a single node, so the variables `MIN_NODES` and `MAX_NODES` are both set to `1`.\n",
+        "\n",
+        "If you want to use multiple nodes to process your batch prediction request, set `MAX_NODES` to the maximum number of nodes you want to use. Vertex AI autoscales the number of nodes used to serve your predictions, up to the maximum number you set. Refer to the [pricing page](https://cloud.google.com/vertex-ai/pricing#prediction-prices) to understand the costs of autoscaling with multiple nodes.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1cf1076178fc"
+      },
+      "outputs": [],
+      "source": [
+        "MIN_NODES = 1\n",
+        "MAX_NODES = 1\n",
+        "\n",
+        "# The name of the job\n",
+        "BATCH_PREDICTION_JOB_NAME = \"cifar10_batch-\" + UUID\n",
+        "\n",
+        "# Folder in the bucket to write results to\n",
+        "DESTINATION_FOLDER = \"batch_prediction_results2\"\n",
+        "\n",
+        "# The Cloud Storage bucket to upload results to\n",
+        "BATCH_PREDICTION_GCS_DEST_PREFIX = BUCKET_URI + \"/\" + DESTINATION_FOLDER\n",
+        "\n",
+        "# Make SDK batch_predict method call\n",
+        "batch_prediction_job = model.batch_predict(\n",
+        "    instances_format=\"file-list\",\n",
+        "    predictions_format=\"jsonl\",\n",
+        "    job_display_name=BATCH_PREDICTION_JOB_NAME,\n",
+        "    gcs_source=BATCH_PREDICTION_GCS_SOURCE,\n",
+        "    gcs_destination_prefix=BATCH_PREDICTION_GCS_DEST_PREFIX,\n",
+        "    model_parameters=None,\n",
+        "    machine_type=DEPLOY_COMPUTE,\n",
+        "    accelerator_type=DEPLOY_GPU,\n",
+        "    accelerator_count=DEPLOY_NGPU,\n",
+        "    starting_replica_count=MIN_NODES,\n",
+        "    max_replica_count=MAX_NODES,\n",
+        "    sync=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "get_batch_prediction:mbsdk,custom,icn"
+      },
+      "source": [
+        "### 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",
+        "\n",
+        "- `instance`: The prediction request.\n",
+        "- `prediction`: The prediction response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "get_batch_prediction:mbsdk,custom,icn"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "bp_iter_outputs = batch_prediction_job.iter_outputs()\n",
+        "\n",
+        "prediction_results = list()\n",
+        "for blob in bp_iter_outputs:\n",
+        "    if blob.name.split(\"/\")[-1].startswith(\"prediction\"):\n",
+        "        prediction_results.append(blob.name)\n",
+        "\n",
+        "tags = list()\n",
+        "for prediction_result in prediction_results:\n",
+        "    gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{prediction_result}\"\n",
+        "    with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
+        "        for line in gfile.readlines():\n",
+        "            line = json.loads(line)\n",
+        "            print(line)\n",
+        "            break"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -702,7 +702,7 @@
        "        + f\"/{PRIVATE_REPO}\"\n",
        "        + \"/tf_serving:gpu\"\n",
        "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:latest-gpu\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4-gpu\"\n",
        "else:\n",
        "    DEPLOY_IMAGE = (\n",
        "        f\"{REGION}-docker.pkg.dev/\"\n",
@@ -710,15 +710,15 @@
        "        + f\"/{PRIVATE_REPO}\"\n",
        "        + \"/tf_serving:cpu\"\n",
        "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:latest\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4\"\n",
        "\n",
        "if not IS_COLAB:\n",
        "    if DEPLOY_GPU:\n",
-        "        ! sudo docker pull tensorflow/serving:latest-gpu\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4-gpu\n",
        "    else:\n",
-        "        ! sudo docker pull tensorflow/serving:latest\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4\n",
        "\n",
-        "    ! docker tag tensorflow/serving $DEPLOY_IMAGE\n",
+        "    ! docker tag $TF_IMAGE $DEPLOY_IMAGE\n",
        "    ! docker push $DEPLOY_IMAGE\n",
        "else:\n",
        "    # install docker daemon\n",
@@ -1434,7 +1434,7 @@
      },
      "outputs": [],
      "source": [
-        "delete_bucket = False\n",
+        "delete_bucket = True\n",
        "delete_model = True\n",
        "delete_endpoint = True\n",
        "delete_batch_job = True\n",
@@ -16,6 +16,7 @@
 /model_monitoring @andrewferlitsch
 /tensorboard @zbl94

+/bigquery_ml/bqml-online-prediction.ipynb @polong-lin
 /model_monitoring/model_monitoring.ipynb @mco-gh
 /ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb @jialuzh
 /ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb @jialuzh
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2021 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",
@@ -66,17 +66,6 @@
        "This tutorial demonstrates how to use the Vertex AI SDK to create image object detection models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:salads,iod"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) 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 bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -101,6 +90,17 @@
        "* 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."
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:salads,iod"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) 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 bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -201,7 +201,7 @@
      },
      "outputs": [],
      "source": [
-        "! pip3 install -U google-cloud-storage $USER_FLAG"
+        "! pip3 install -U --upgrade tensorflow google-cloud-storage $USER_FLAG"
      ]
    },
    {
@@ -213,17 +213,6 @@
        "Install the latest version of *tensorflow* library."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_tensorflow"
-      },
-      "outputs": [],
-      "source": [
-        "! pip3 install --upgrade tensorflow $USER_FLAG"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -383,9 +372,9 @@
        "id": "timestamp"
      },
      "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
      ]
    },
    {
@@ -396,9 +385,16 @@
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
      ]
    },
    {
@@ -409,7 +405,7 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
        "\n",
@@ -494,7 +490,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + UUID"
      ]
    },
    {
@@ -669,7 +665,7 @@
      "outputs": [],
      "source": [
        "dataset = aiplatform.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"Salads\" + \"_\" + UUID,\n",
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.bounding_box,\n",
        ")\n",
@@ -717,7 +713,7 @@
      "outputs": [],
      "source": [
        "job = aiplatform.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + TIMESTAMP,\n",
+        "    display_name=\"salads_\" + UUID,\n",
        "    prediction_type=\"object_detection\",\n",
        "    multi_label=False,\n",
        "    model_type=\"CLOUD\",\n",
@@ -760,7 +756,7 @@
      "source": [
        "model = job.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    model_display_name=\"salads_\" + UUID,\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
@@ -790,7 +786,7 @@
      "outputs": [],
      "source": [
        "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + UUID)\n",
        "\n",
        "# Get a reference to the Model Service client\n",
        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -961,7 +957,7 @@
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    job_display_name=\"salads_\" + UUID,\n",
        "    gcs_source=gcs_input_uri,\n",
        "    gcs_destination_prefix=BUCKET_URI,\n",
        "    machine_type=\"n1-standard-4\",\n",
@@ -32,18 +32,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-online-prediction.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/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-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",
        "    </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/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-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",
        "    </a>\n",
@@ -63,7 +63,7 @@
        "\n",
        "### Dataset\n",
        "\n",
-        "The dataset, [available publicly on BigQuery](https://console.cloud.google.com/bigquery?project=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&p=bigquery-public-data&page=dataset), comes from obfuscated [Google Analytics 4 data](https://support.google.com/analytics/answer/10937659) from the [Google Merchandise Store](https://shop.googlemerchandisestore.com/).\n",
+        "The dataset, <a href=\"https://console.cloud.google.com/bigquery?project=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&p=bigquery-public-data&page=dataset\" target=\"_blank\">available publicly on BigQuery</a>, comes from obfuscated <a href=\"https://support.google.com/analytics/answer/10937659\" target=\"_blank\">Google Analytics 4 data</a> from the <a href=\"https://shop.googlemerchandisestore.com/\" target=\"_blank\">Google Merchandise Store</a>).\n",
        "\n",
        "### Objective\n",
        "\n",
@@ -95,9 +95,9 @@
        "* Vertex AI\n",
        "\n",
        "\n",
-        "Learn about [BigQuery Pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML pricing](https://cloud.google.com/bigquery-ml/pricing), [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "Learn about <a href=\"https://cloud.google.com/bigquery/pricing\" target=\"_blank\">BigQuery Pricing</a>, <a href=\"https://cloud.google.com/bigquery-ml/pricing\" target=\"_blank\">BigQuery ML pricing</a>, <a href=\"https://cloud.google.com/vertex-ai/pricing\" target=\"_blank\">Vertex AI\n",
+        "pricing</a>, and use the <a href=\"https://cloud.google.com/products/calculator/\" target=\"_blank\">Pricing\n",
+        "Calculator</a>\n",
        "to generate a cost estimate based on your projected usage."
      ]
    },
@@ -128,18 +128,18 @@
        "* virtualenv\n",
        "* Jupyter notebook running in a virtual environment with Python 3\n",
        "\n",
-        "The Google Cloud guide to [Setting up a Python development\n",
-        "environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
-        "installation guide](https://jupyter.org/install) provide detailed instructions\n",
+        "The Google Cloud guide to <a href=\"https://cloud.google.com/python/setup\" target=\"_blank\">Setting up a Python development\n",
+        "environment</a> and the <a href=\"https://jupyter.org/install\" target=\"_blank\">Jupyter\n",
+        "installation guide</a> provide detailed instructions\n",
        "for meeting these requirements. The following steps provide a condensed set of\n",
        "instructions:\n",
        "\n",
-        "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
+        "1. <a href=\"https://cloud.google.com/sdk/docs/\" target=\"_blank\">Install and initialize the Cloud SDK.</a>\n",
        "\n",
-        "1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_python\" target=\"_blank\">Install Python 3.</a>\n",
        "\n",
-        "1. [Install\n",
-        "   virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_and_using_virtualenv\" target=\"_blank\">Install\n",
+        "   virtualenv</a>\n",
        "   and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
        "\n",
        "1. To install Jupyter, run `pip3 install jupyter` on the\n",
@@ -234,13 +234,13 @@
        "\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",
+        "1. <a href=\"https://console.cloud.google.com/cloud-resource-manager\" target=\"_blank\">Select or create a Google Cloud project</a>. 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",
+        "1. <a href=\"https://cloud.google.com/billing/docs/how-to/modify-project\" target=\"_blank\">Make sure that billing is enabled for your project</a>.\n",
        "\n",
-        "1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+        "1. <a href=\"https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com\" target=\"_blank\">Enable the Vertex AI API</a>.\n",
        "\n",
-        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
+        "1. If you are running this notebook locally, you will need to install the <a href=\"https://cloud.google.com/sdk\" target=\"_blank\">Cloud SDK</a>.\n",
        "\n",
        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook.\n",
@@ -267,7 +267,7 @@
      },
      "outputs": [],
      "source": [
-        "PROJECT_ID = \"YOUR-PROJECT-ID\"\n",
+        "PROJECT_ID = \"[YOUR-PROJECT-ID]\"\n",
        "\n",
        "# Get your Google Cloud project ID from gcloud\n",
        "import os\n",
@@ -314,9 +314,9 @@
        "- Europe: `europe-west4`\n",
        "- Asia Pacific: `asia-east1`\n",
        "\n",
-        "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
+        "You might not be able to use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
        "\n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+        "Learn more about <a href=\"https://cloud.google.com/vertex-ai/docs/general/locations\" target=\"_blank\">Vertex AI regions</a>."
      ]
    },
    {
@@ -339,9 +339,9 @@
        "id": "06571eb4063b"
      },
      "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
+        "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."
      ]
    },
    {
@@ -352,9 +352,16 @@
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
      ]
    },
    {
@@ -380,8 +387,7 @@
        "\n",
        "**Otherwise**, follow these steps:\n",
        "\n",
-        "1. In the Cloud Console, go to the [**Create service account key**\n",
-        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
+        "1. In the Cloud Console, go to the <a href=\"https://console.cloud.google.com/apis/credentials/serviceaccountkey\" target=\"_blank\">**Create service account key** page</a>.\n",
        "\n",
        "2. Click **Create service account**.\n",
        "\n",
@@ -486,7 +492,10 @@
      },
      "outputs": [],
      "source": [
+        "from typing import Union\n",
+        "\n",
        "import google.cloud.aiplatform as vertex_ai\n",
+        "import pandas as pd\n",
        "from google.cloud import bigquery"
      ]
    },
@@ -550,24 +559,17 @@
      "outputs": [],
      "source": [
        "# Wrapper to use BigQuery client to run query/job, return job ID or result as DF\n",
-        "def bq_query(sql):\n",
+        "def run_bq_query(sql: str) -> Union[str, pd.DataFrame]:\n",
        "    \"\"\"\n",
        "    Input: SQL query, as a string, to execute in BigQuery\n",
        "    Returns the query results as a pandas DataFrame, or error, if any\n",
        "    \"\"\"\n",
-        "    # Import Exceptions library to help with dataset error catching\n",
-        "    from google.cloud.exceptions import BadRequest\n",
        "\n",
        "    # Try dry run before executing query to catch any errors\n",
-        "    try:\n",
-        "        job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)\n",
-        "\n",
-        "        bq_client.query(sql, job_config=job_config)\n",
-        "\n",
-        "    except BadRequest as err:\n",
-        "        print(err)\n",
-        "        return\n",
+        "    job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)\n",
+        "    bq_client.query(sql, job_config=job_config)\n",
        "\n",
+        "    # If dry run succeeds without errors, proceed to run query\n",
        "    job_config = bigquery.QueryJobConfig()\n",
        "    client_result = bq_client.query(sql, job_config=job_config)\n",
        "\n",
@@ -589,7 +591,7 @@
        "\n",
        "BigQuery ML (BQML) provides the capability to train ML tabular models, such as classification, regression, forecasting, and matrix factorization, in BigQuery using SQL syntax directly. BigQuery ML uses the scalable infrastructure of BigQuery ML so you don't need to set up additional infrastructure for training or batch serving.\n",
        "\n",
-        "Learn more about [BigQuery ML documentation](https://cloud.google.com/bigquery-ml/docs)."
+        "Learn more about <a href=\"https://cloud.google.com/bigquery-ml/docs\" target=\"_blank\">BigQuery ML documentation</a>."
      ]
    },
    {
@@ -600,9 +602,13 @@
      },
      "outputs": [],
      "source": [
-        "BQ_DATASET_NAME = \"ga4_churnprediction\"\n",
+        "BQ_DATASET_NAME = f\"ga4_churnprediction_{UUID}\"\n",
        "\n",
-        "bq_query(f\"\"\"CREATE SCHEMA IF NOT EXISTS {BQ_DATASET_NAME}\"\"\")"
+        "sql_create_dataset = f\"\"\"CREATE SCHEMA IF NOT EXISTS {BQ_DATASET_NAME}\"\"\"\n",
+        "\n",
+        "print(sql_create_dataset)\n",
+        "\n",
+        "run_bq_query(sql_create_dataset)"
      ]
    },
    {
@@ -620,7 +626,7 @@
        "id": "49dd00d5fbe5"
      },
      "source": [
-        "Inpect data that has been pre-processed from [Google Analytics 4 data from the Google Merchandise Store](https://support.google.com/analytics/answer/10937659) so that it can be used for classification. For more information on how this data was prepared, read [this blog post](https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml).\n",
+        "Inpect data that has been pre-processed from <a href=\"https://support.google.com/analytics/answer/10937659\" target=\"_blank\">Google Analytics 4 data from the Google Merchandise Store</a> so that it can be used for classification. For more information on how this data was prepared, read <a href=\"https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml\" target=\"_blank\">this blog post</a>.\n",
        "\n",
        "As seen below, each row represents a single user, and the columns represent their demographic features, their aggregated behavioral features in the first 24 hours of visiting the Google Merchandise Store, and the label (whether the user churned or returned any time after the first 24 hours)."
      ]
@@ -641,7 +647,7 @@
        "LIMIT\n",
        "    100\n",
        "\"\"\"\n",
-        "bq_query(sql_inspect)"
+        "run_bq_query(sql_inspect)"
      ]
    },
    {
@@ -662,9 +668,9 @@
        "The query below trains a logistic regression model using BigQuery ML. BigQuery resources are used to train the model.\n",
        "\n",
        "In the `OPTIONS` parameter:\n",
-        "* with `model_registry=\"vertex_ai\"`, the BigQuery ML model will automatically be [registered to Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml), which enables you to view all of your registered models and its versions on Google Cloud in one place.\n",
+        "* with `model_registry=\"vertex_ai\"`, the BigQuery ML model will automatically be <a href=\"https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml\" target=\"_blank\">registered to Vertex AI Model Registry</a>, which enables you to view all of your registered models and its versions on Google Cloud in one place.\n",
        "\n",
-        "* `vertex_ai_model_version_aliases allows you to set aliases to help you keep track of your model version ([documentation](https://cloud.google.com/vertex-ai/docs/model-registry/model-alias))."
+        "* `vertex_ai_model_version_aliases allows you to set aliases to help you keep track of your model version (<a href=\"https://cloud.google.com/vertex-ai/docs/model-registry/model-alias\" target=\"_blank\">documentation</a>)."
      ]
    },
    {
@@ -677,7 +683,7 @@
      "source": [
        "# this cell may take ~1 min to run\n",
        "\n",
-        "BQML_MODEL_NAME = \"bqmlmodelchurn\"\n",
+        "BQML_MODEL_NAME = f\"bqml_model_churn_{UUID}\"\n",
        "\n",
        "sql_train_model_bqml = f\"\"\"\n",
        "CREATE OR REPLACE MODEL {BQ_DATASET_NAME}.{BQML_MODEL_NAME}    \n",
@@ -696,7 +702,7 @@
        "\n",
        "print(sql_train_model_bqml)\n",
        "\n",
-        "bq_query(sql_train_model_bqml)"
+        "run_bq_query(sql_train_model_bqml)"
      ]
    },
    {
@@ -714,7 +720,7 @@
        "id": "2aaaae772f67"
      },
      "source": [
-        "With the model created, you can now evaluate the logistic regression model. Behind the scenes, BigQuery ML automatically [split the data](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method), which makes it easier to quickly train and evaluate models."
+        "With the model created, you can now evaluate the logistic regression model. Behind the scenes, BigQuery ML automatically <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method\" target=\"_blank\">split the data</a>, which makes it easier to quickly train and evaluate models."
      ]
    },
    {
@@ -734,7 +740,7 @@
        "\n",
        "print(sql_evaluate_model)\n",
        "\n",
-        "bq_query(sql_evaluate_model)"
+        "run_bq_query(sql_evaluate_model)"
      ]
    },
    {
@@ -745,7 +751,7 @@
      "source": [
        "These metrics help you understand the performance of the model. \n",
        "\n",
-        "There are various metrics for logistic regression and other model types (full list of metrics can be found in the [documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output))."
+        "There are various metrics for logistic regression and other model types (full list of metrics can be found in the <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output\" target=\"_blank\">documentation</a>)."
      ]
    },
    {
@@ -765,7 +771,7 @@
      "source": [
        "Make a batch prediction in BigQuery ML on the original training data to check the probability of churn for each of the users, as seen in the `probability` column, with the predicted label under the `predicted_churn` column.\n",
        "\n",
-        "[ML.EXPLAIN_PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict) has built-in [Explainable AI](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-xai-overview). This allows you to see the top contributing features to each prediction and interpret how it was computed."
+        "<a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict\" target=\"_blank\">ML.EXPLAIN_PREDICT</a> has built-in <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-xai-overview\" target=\"_blank\">Explainable AI</a>. This allows you to see the top contributing features to each prediction and interpret how it was computed."
      ]
    },
    {
@@ -787,7 +793,7 @@
        "\n",
        "print(sql_explain_predict)\n",
        "\n",
-        "bq_query(sql_explain_predict)"
+        "run_bq_query(sql_explain_predict)"
      ]
    },
    {
@@ -796,7 +802,7 @@
        "id": "fa1f96c0f452"
      },
      "source": [
-        "Since the `top_feature_attributions` is a nested column, you can unnest the array ([documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays)) into separate rows for each of the features. In other words, since ML.EXPLAIN_PREDICT provides the top 5 most important features, using `UNNEST` results in 5 rows per prediction:"
+        "Since the `top_feature_attributions` is a nested column, you can unnest the array (<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays\" target=\"_blank\">documentation</a>) into separate rows for each of the features. In other words, since ML.EXPLAIN_PREDICT provides the top 5 most important features, using `UNNEST` results in 5 rows per prediction:"
      ]
    },
    {
@@ -827,7 +833,7 @@
        "\n",
        "print(sql_explain_predict)\n",
        "\n",
-        "bq_query(sql_explain_predict)"
+        "run_bq_query(sql_explain_predict)"
      ]
    },
    {
@@ -847,7 +853,7 @@
      "source": [
        "When the model was trained in BigQuery ML, the line `model_registry=\"vertex_ai\"` registered the model to Vertex AI Model Registry automatically upon completion.\n",
        "\n",
-        "You can view the model on the [Vertex AI Model Registry page](https://console.cloud.google.com/vertex-ai/models), or use the code below to check that it was successfully registered:"
+        "You can view the model on the <a href=\"https://console.cloud.google.com/vertex-ai/models\" target=\"_blank\">Vertex AI Model Registry page</a>, or use the code below to check that it was successfully registered:"
      ]
    },
    {
@@ -858,12 +864,7 @@
      },
      "outputs": [],
      "source": [
-        "print(f\"BQML_MODEL_NAME = {BQML_MODEL_NAME}\")\n",
-        "\n",
-        "models = vertex_ai.Model.list(\n",
-        "    filter=f\"display_name={BQML_MODEL_NAME}\", order_by=\"update_time\"\n",
-        ")\n",
-        "model = models[0]\n",
+        "model = vertex_ai.Model(model_name=BQML_MODEL_NAME)\n",
        "\n",
        "print(model.gca_resource)"
      ]
@@ -883,7 +884,7 @@
        "id": "b6120dcc1ff6"
      },
      "source": [
-        "While BigQuery ML supports batch prediction with [ML.PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict) and [ML.EXPLAIN_PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict), BigQuery ML is not suitable for real-time predictions where you need low latency predictions with potentially high frequency of requests.\n",
+        "While BigQuery ML supports batch prediction with <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict\" target=\"_blank\">ML.PREDICT</a> and <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict\" target=\"_blank\">ML.EXPLAIN_PREDICT</a>, BigQuery ML is not suitable for real-time predictions where you need low latency predictions with potentially high frequency of requests.\n",
        "\n",
        "In other words, deploying the BigQuery ML model to an endpoint enables you to do online predictions."
      ]
@@ -906,30 +907,6 @@
        "To deploy your model to an endpoint, you will first need to create an endpoint before you deploy the model to it."
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3ce73125dff6"
-      },
-      "outputs": [],
-      "source": [
-        "def create_endpoint(\n",
-        "    project: str,\n",
-        "    display_name: str,\n",
-        "    location: str,\n",
-        "):\n",
-        "    endpoint = vertex_ai.Endpoint.create(\n",
-        "        display_name=display_name,\n",
-        "        project=project,\n",
-        "        location=location,\n",
-        "    )\n",
-        "\n",
-        "    print(endpoint.display_name)\n",
-        "    print(endpoint.resource_name)\n",
-        "    return endpoint"
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -938,17 +915,16 @@
      },
      "outputs": [],
      "source": [
-        "endpoint_name = f\"{BQML_MODEL_NAME}-{TIMESTAMP}\"\n",
+        "ENDPOINT_NAME = f\"{BQML_MODEL_NAME}-endpoint\"\n",
        "\n",
-        "print(\n",
-        "    f\"\"\"\n",
-        "PROJECT_ID: {PROJECT_ID},\n",
-        "endpoint_name: {endpoint_name}\n",
-        "REGION: {REGION}\n",
-        "\"\"\"\n",
+        "endpoint = vertex_ai.Endpoint.create(\n",
+        "    display_name=ENDPOINT_NAME,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
        ")\n",
        "\n",
-        "create_endpoint(PROJECT_ID, endpoint_name, REGION)"
+        "print(endpoint.display_name)\n",
+        "print(endpoint.resource_name)"
      ]
    },
    {
@@ -966,31 +942,7 @@
        "id": "951ed1693f6b"
      },
      "source": [
-        "List the endpoints to make sure it has successfully been created. You can also view your endpoints on the [Vertex AI Endpoints page](https://console.cloud.google.com/vertex-ai/endpoints?project=polong-contentdev)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0a9bad8d9ad4"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint = vertex_ai.Endpoint.list(\n",
-        "    # filter=f'display_name={endpoint_name}', # optional: filter by specific endpoint name\n",
-        "    order_by=\"update_time\"\n",
-        ")\n",
-        "endpoint[-1]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2431a4d28d97"
-      },
-      "source": [
-        "Retrieve the endpoint id so you can use it in the next step."
+        "List the endpoints to make sure it has successfully been created. (You can also view your endpoints on the <a href=\"https://console.cloud.google.com/vertex-ai/endpoints\" target=\"_blank\">Vertex AI Endpoints page</a>)."
      ]
    },
    {
@@ -1001,7 +953,7 @@
      },
      "outputs": [],
      "source": [
-        "endpoint[-1].to_dict()"
+        "endpoint.list()"
      ]
    },
    {
@@ -1019,74 +971,19 @@
        "id": "6a90be5b77a2"
      },
      "source": [
-        "With the model, you can now deploy it to an endpoint. "
+        "With the new endpoint, you can now deploy your model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "af323ea42c5b"
-      },
-      "outputs": [],
-      "source": [
-        "from typing import Dict, Optional, Sequence, Tuple\n",
-        "\n",
-        "\n",
-        "def deploy_model_with_automatic_resources_sample(\n",
-        "    project,\n",
-        "    location,\n",
-        "    model_name: str,\n",
-        "    endpoint: Optional[vertex_ai.Endpoint] = None,\n",
-        "    deployed_model_display_name: Optional[str] = None,\n",
-        "    traffic_percentage: Optional[int] = 0,\n",
-        "    traffic_split: Optional[Dict[str, int]] = None,\n",
-        "    min_replica_count: int = 1,\n",
-        "    max_replica_count: int = 1,\n",
-        "    metadata: Optional[Sequence[Tuple[str, str]]] = (),\n",
-        "    sync: bool = True,\n",
-        "):\n",
-        "    \"\"\"\n",
-        "    model_name: A fully-qualified model resource name or model ID.\n",
-        "          Example: \"projects/123/locations/us-central1/models/456\" or\n",
-        "          \"456\" when project and location are initialized or passed.\n",
-        "    \"\"\"\n",
-        "\n",
-        "    model = vertex_ai.Model(model_name=model_name)\n",
-        "\n",
-        "    model.deploy(\n",
-        "        endpoint=endpoint,\n",
-        "        deployed_model_display_name=deployed_model_display_name,\n",
-        "        traffic_percentage=traffic_percentage,\n",
-        "        traffic_split=traffic_split,\n",
-        "        min_replica_count=min_replica_count,\n",
-        "        max_replica_count=max_replica_count,\n",
-        "        metadata=metadata,\n",
-        "        sync=sync,\n",
-        "    )\n",
-        "\n",
-        "    model.wait()\n",
-        "\n",
-        "    print(model.display_name)\n",
-        "    print(model.resource_name)\n",
-        "    return"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9e6763369af4"
+        "id": "c70ecc568ee5"
      },
      "outputs": [],
      "source": [
        "# deploying the model to the endpoint may take 10-15 minutes\n",
-        "deploy_model_with_automatic_resources_sample(\n",
-        "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
-        "    model_name=BQML_MODEL_NAME,\n",
-        "    endpoint=endpoint[-1],\n",
-        ")"
+        "model.deploy(endpoint=endpoint)"
      ]
    },
    {
@@ -1095,7 +992,7 @@
        "id": "c303d779477b"
      },
      "source": [
-        "You can also check on the status of your model by visiting the [Vertex AI Endpoints page](https://console.cloud.google.com/vertex-ai/endpoints)."
+        "You can also check on the status of your model by visiting the <a href=\"https://console.cloud.google.com/vertex-ai/endpoints\" target=\"_blank\">Vertex AI Endpoints page</a>."
      ]
    },
    {
@@ -1168,35 +1065,12 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "2c6093ce9f8a"
+        "id": "b4839f31d2f8"
      },
      "outputs": [],
      "source": [
-        "def endpoint_predict_sample(\n",
-        "    project: str, location: str, instances: list, endpoint: str\n",
-        "):\n",
-        "    endpoint = vertex_ai.Endpoint(endpoint)\n",
-        "\n",
-        "    prediction = endpoint.predict(instances=instances)\n",
-        "    return prediction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0c41fd6eeb6f"
-      },
-      "outputs": [],
-      "source": [
-        "prediction_response = endpoint_predict_sample(\n",
-        "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
-        "    instances=df_sample_requests_list,\n",
-        "    endpoint=endpoint[-1].name,\n",
-        ")\n",
-        "\n",
-        "prediction_response"
+        "prediction = endpoint.predict(df_sample_requests_list)\n",
+        "print(prediction)"
      ]
    },
    {
@@ -1216,7 +1090,7 @@
      },
      "outputs": [],
      "source": [
-        "prediction_response.predictions"
+        "prediction.predictions"
      ]
    },
    {
@@ -1227,8 +1101,8 @@
      "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",
+        "To clean up all Google Cloud resources used in this project, you can <a href=\"https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects\" target=\"_blank\">delete the Google Cloud\n",
+        "project</a> you used for the tutorial.\n",
        "\n",
        "Otherwise, you can delete the individual resources you created in this tutorial:"
      ]
@@ -1241,18 +1115,12 @@
      },
      "outputs": [],
      "source": [
-        "# MODEL_ID = model.name\n",
+        "# Undeploy model from endpoint and delete endpoint\n",
+        "endpoint.undeploy_all()\n",
+        "endpoint.delete()\n",
        "\n",
-        "ENDPOINT_ID = int(endpoint[-1].name)\n",
-        "\n",
-        "# Undeploy model from endpoint\n",
-        "endpoint[-1].undeploy_all()\n",
-        "\n",
-        "# Delete endpoint resource\n",
-        "! gcloud ai endpoints delete $ENDPOINT_ID --quiet --region $REGION\n",
-        "\n",
-        "# Delete BigQuery ML model\n",
-        "! bq rm -f --model $PROJECT_ID\\:$BQ_DATASET_NAME\\.$BQML_MODEL_NAME"
+        "# Delete BigQuery dataset, including the BigQuery ML model\n",
+        "! bq rm -r -f $PROJECT_ID:$BQ_DATASET_NAME"
      ]
    }
  ],
@@ -44,7 +44,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb\">\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/custom/custom-tabular-bq-managed-dataset.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",
@@ -223,12 +223,22 @@
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
+        "# Don't bother installing tensorflow or explainable_ai_sdk on Colab\n",
+        "extra_pkgs = \"tensorflow explainable_ai_sdk\"\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "  extra_pkgs = \"\"\n",
+        "\n",
        "# Install required packages.\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-aiplatform\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade tensorflow\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade explainable_ai_sdk\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-api-python-client google-auth-oauthlib google-auth-httplib2 oauth2client requests\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-storage==1.32.0"
+        "! pip3 install {USER_FLAG} \\\n",
+        "    google-cloud-aiplatform \\\n",
+        "    explainable_ai_sdk \\\n",
+        "    $extra_pkgs \\\n",
+        "    google-api-python-client \\\n",
+        "    google-auth-oauthlib \\\n",
+        "    google-auth-httplib2 \\\n",
+        "    oauth2client \\\n",
+        "    requests \\\n",
+        "    google-cloud-storage==1.32.0"
      ]
    },
    {
@@ -562,7 +572,7 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "init_aip:mbsdk,region"
+        "id": "wGa5T9eRR8Mz"
      },
      "outputs": [],
      "source": [
Author	SHA1	Message	Date
gericdongandGitHub	b7afec4eb8	Merge branch 'main' into batch_custom_text	2022-09-09 08:44:25 -04:00
MarcandGitHub	ce9e6ecf62	add back pip install of xai sdk (#935 )	2022-09-09 01:02:56 +01:00
Soheila Zangeneh GitHub krishr2d2 sudarshan-SpringML Andrew Ferlitsch	9ab5f4274a	Add automl regression and classification with model evaluation (#911 ) * Add automl regression model eval first draft * Remove extra file * Pring evaluation results * adds the automl-tabular-classification notebook in model_evaluation folder * removes unnecessary imports * adjusts the imports inside the pipeline * adjusts the imports * elaborates imports inside pipeline * modified regression notebook * renamed pipeline displayname to resolve error * Add automl regression model eval first draft * Remove extra file * Pring evaluation results * modified some text * added suggested updates from review: remove dataflow params, add/change textual descriptions, add UUID * removes the output from the notebooks * removes the extra matplotlib import * ran linter test * addressed soheila's comments * ran linter * addresses the review comments * ran linter test * removes the artifacts comment * ran linter test * reviewed comments * ran linter * addresses review comments: textual updates, removes unnecessary parameters * ran linter test * addressed comments * ran linter * removed unwanted variables * ran linter * addresses the tech-writer's comments + updates the pipeline image with data-sampler task * ran linter test Co-authored-by: krishr2d2 <krishna.movva@springml.com> Co-authored-by: sudarshan-SpringML <sudarshan.c@springml.com> Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-08 14:42:04 -07:00
Andrew FerlitschandGitHub	371628b8ff	Merge branch 'main' into batch_custom_text	2022-09-08 14:00:13 -07:00
Andrew Ferlitsch	52958a8da6	feat: notebook for custom text model batch prediction	2022-09-08 20:21:03 +00:00
Andrew Ferlitsch	c4dcac147e	feat: notebook for custom text model batch prediction	2022-09-08 20:18:33 +00:00
Andrew FerlitschandGitHub	29e584a422	feat: add batch automl video notebook (#933 ) * feat: batch for automl video * feat: batch for automl video * feat: batch for automl video	2022-09-08 11:34:29 -07:00
MarcandGitHub	beabb87cff	fix import problem described in b/245553683 (#932 ) * fix aiplatform import problem * lint fix * build fix, missing tensforflow * lint fixes	2022-09-08 16:47:20 +01:00
Polong Lin GitHub Ivan Cheung	e3f6717ff6	community -> official for bqml-online-prediction.ipynb (#788 ) * move bqml-vertex notebook from community to official * add to CODEOWNERS official * fix errors for execution-test * fix project_id line * fix linting * fixes re: comments from sarahcdugan * fix links at top of notebook from community/ to official/ * added UUID to model name * fix linting * fix error in TIMESTAMP --> UUID * fixing linting double space * fixes re: ivanmkc comments * fixed notebook after linting issues * linting via cloud shell * simplified run_bq_query function * linting Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com>	2022-09-07 18:20:02 -04:00
samthrasher GitHub Andrew Ferlitsch	fd30c4014a	Minor fixes for CPR Pytorch sample (#744 ) * Minor fixes for CPR Pytorch sample: Add missing test data, add auth info to readme, scrub private project and bucket names from config, tolerate missing config.json in unit tests. * Minor fixes for CPR Pytorch sample: Add missing test data, add auth info to readme, scrub private project and bucket names from config, tolerate missing config.json in unit tests. * Fix merge conflicts * fix typo * Point CPR links to main branch of SDK repo. Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-07 13:02:55 -07:00
Andrew FerlitschandGitHub	4be8b0a59a	fix: finetuning of batch notebooks (#930 ) * fix: fine-tuning * fix: fine-tuning	2022-09-07 10:44:44 -07:00
Andrew FerlitschandGitHub	aa09d46265	feat: batch prediction for AutoML text models (#929 ) * feat: Automl text model batch predict * feat: Automl text model batch predict * feat: Automl text model batch predict	2022-09-07 13:14:16 -04:00
Andrew FerlitschandGitHub	5667967131	feat: add BQ input example (#926 ) * feat: add notebook for custom tabular batch predict * feat: add notebook for custom tabular batch predict * feat: add example for BQ input * feat: add example for BQ input * feat: add example for BQ input * feat: add example for BQ input	2022-09-07 08:38:36 -07:00
Andrew Ferlitsch GitHub gericdong	4e4f532658	feat: batch prediction for automl tabular models (#928 ) * feat: notebook for AutoML tabular batch prediction * feat: notebook for AutoML tabular batch prediction Co-authored-by: gericdong <itseric@google.com>	2022-09-06 15:53:54 -04:00
Andrew FerlitschandGitHub	14b2ce4f2e	feat: notebook for batch predict for automl image models (#927 ) * feat: batch predict for automl image model * feat: batch predict for automl image model	2022-09-06 11:45:04 -04:00
Chun-Hsiang WangandGitHub	c14b98c92d	samples: Minor fix for the wording. (#924 )	2022-09-05 10:42:25 -07:00
gericdong GitHub Andrew Ferlitsch	8275ea6c49	fix: correct the download_url (#923 ) * fix: correct the download_url * fix: fixed formatting issue Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-02 17:04:42 -04:00
uday kumar GitHub Andrew Ferlitsch	40fbffcc95	Sdk automl image object detection batch (#869 ) * changed to andrew comments * changes according to andrew comments * changes according to andrew comments * review changes * review changes * review changes Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-02 13:34:25 -07:00
Andrew FerlitschandGitHub	08bb513488	feat: Batch prediction for custom tabular model (#920 ) * feat: add notebook for custom tabular batch predict * feat: add notebook for custom tabular batch predict	2022-09-01 20:33:34 -04:00
Chun-Hsiang WangandGitHub	b298f83cd3	Vertex Prediction PyTorch Experimental: Add a sample for pre-built PyTorch deployments. (#898 ) * samples: Add a new sample for pre-built Pytorch deployments. It's borrowed from the examples in community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud. * samples: Removed all training related stuff in the notebooks. * samples: Fixed comments. * samples: Updated readme. * samples: Updated emails for Pytorch launch.	2022-09-01 11:48:59 -07:00
Andrew FerlitschandGitHub	659cbb54c4	feat: add model monitoring with custom container (#919 ) * feat: add notebook using custom deployment container * feat: add notebook using custom deployment container	2022-09-01 10:22:05 -07:00
Andrew Ferlitsch GitHub gericdong	6ddcaa540a	fix: tf serving workaround (#917 ) * fix: pin TF serving image * fix: pin TF serving image Co-authored-by: gericdong <itseric@google.com>	2022-09-01 12:58:27 -04:00
Andrew Ferlitsch GitHub gericdong	1656c57b18	feat: extend image batch notebook (#916 ) * feat: add notebook for custom image model batch prediction * feat: add notebook for custom image model batch prediction * fix: review comments * fix: review comments * feat: extend image batch notebook * feat: extend image batch notebook Co-authored-by: gericdong <itseric@google.com>	2022-09-01 09:56:36 -07:00