fix: remove pandas inmport

update: use util to read from bucket
fix: revert to JSON
2026-09-27 15:42:05 +00:00 · 2023-05-05 15:24:49 +00:00 · 2023-05-04 21:22:44 +00:00 · 2023-05-04 21:02:44 +00:00 · 2023-05-04 18:08:36 +00:00 · 2023-05-04 17:49:25 +00:00
52 changed files with 2850 additions and 872 deletions
@@ -17,6 +17,7 @@

 import argparse
 import pathlib
+import os

 import execute_changed_notebooks_helper

@@ -39,6 +40,19 @@ parser.add_argument(
    help="The path to the file that has newline-limited folders of notebooks that should be tested.",
    required=True,
 )
+parser.add_argument(
+    "--test_percent",
+    type=int,
+    help="The percent of notebooks to be tested (between 1 and 100).",
+    required=False,
+    default=100,
+)
+parser.add_argument(
+    "--build_id",
+    type=str,
+    help="The build id (which may be a Cloud Build job specific or user explicit.",
+    required=True
+)
 parser.add_argument(
    "--base_branch",
    help="The base git branch to diff against to find changed files.",
@@ -107,24 +121,49 @@ parser.add_argument(
    default=True,
    help="Should run notebooks in parallel.",
 )
+parser.add_argument(
+    "--dry_run",
+    type=str2bool,
+    default=False,
+    help="Dry run for testing - no execution",
+)

 args = parser.parse_args()

-notebooks = execute_changed_notebooks_helper.get_changed_notebooks(
+changed_notebooks = execute_changed_notebooks_helper.get_changed_notebooks(
    test_paths_file=args.test_paths_file,
    base_branch=args.base_branch,
 )

-execute_changed_notebooks_helper.process_and_execute_notebooks(
-    notebooks=notebooks,
-    container_uri=args.container_uri,
-    staging_bucket=args.staging_bucket,
-    artifacts_bucket=args.artifacts_bucket,
-    should_parallelize=args.should_parallelize,
-    timeout=args.timeout,
-    variable_project_id=args.variable_project_id,
-    variable_region=args.variable_region,
-    variable_service_account=args.variable_service_account,
-    variable_vpc_network=args.variable_vpc_network,
-    private_pool_id=args.private_pool_id,
+
+results_bucket = f"{args.artifacts_bucket}"
+results_file = f"{args.build_id}.json"
+
+if args.test_percent == 100:
+    notebooks = changed_notebooks
+    accumulative_results = {}
+else:
+    accumulative_results = execute_changed_notebooks_helper.load_results(results_bucket, results_file)
+
+    notebooks = [changed_notebook for changed_notebook in changed_notebooks if execute_changed_notebooks_helper.select_notebook(changed_notebook, accumulative_results, args.test_percent)]
+
+if args.dry_run:
+    print("Dry run ...\n")
+    for notebook in notebooks:
+        print(f"Would execute: {notebook}")
+else:
+    execute_changed_notebooks_helper.process_and_execute_notebooks(
+        notebooks=notebooks,
+        container_uri=args.container_uri,
+        staging_bucket=args.staging_bucket,
+        artifacts_bucket=args.artifacts_bucket,
+        results_file=results_file,
+        accumulative_results=accumulative_results,
+        should_parallelize=args.should_parallelize,
+        timeout=args.timeout,
+        variable_project_id=args.variable_project_id,
+        variable_region=args.variable_region,
+        variable_service_account=args.variable_service_account,
+        variable_vpc_network=args.variable_vpc_network,
+        private_pool_id=args.private_pool_id
 )
@@ -21,11 +21,15 @@ import json
 import git
 import operator
 import os
+import io
+import json
 import pathlib
 import re
 import subprocess
+import random
+from google.cloud import storage
 import utils
-from typing import List, Optional
+from typing import List, Optional, Dict, Any
 from utils import util

 import execute_notebook_helper
@@ -65,7 +69,9 @@ def format_timedelta(delta: datetime.timedelta) -> str:
@dataclasses.dataclass
 class NotebookExecutionResult:
    name: str
+    path: str
    duration: datetime.timedelta
+    start_time: datetime.datetime
    is_pass: bool
    log_url: str
    output_uri: str
@@ -80,6 +86,40 @@ class NotebookExecutionResult:
        else:
            return None

+def load_results(results_bucket: str,
+        results_file: str) -> Dict[str,Any]:
+    '''
+    Load accumulated notebook test results
+    '''
+
+    print("Loading existing accumulative results ...")
+    accumulative_results = {}
+    try:
+        content = util.download_blob_into_memory(results_bucket, results_file, download_as_text=True)
+        accumulative_results = json.loads(content)
+        print(accumulative_results)
+    except Exception as e:
+        print(e)
+
+    # If there are no accumulative results, an empty dict is returned
+    return accumulative_results
+
+def select_notebook(changed_notebook: str,
+                    accumulative_results: Dict[str, Any],
+                    test_percent: int) -> bool:
+    '''
+    Algorithm to randomly select a notebook, but weight the propbability of selected based on past failures
+    '''
+
+    if changed_notebook in accumulative_results:
+        pass_count = accumulative_results[changed_notebook]['passed']
+        fail_count = accumulative_results[changed_notebook]['failed']
+    else:
+        pass_count = 1
+        fail_count = 0
+
+    return (random.randint(1, 100) * (1 + (fail_count / (pass_count + fail_count))) < test_percent)
+

 def _process_notebook(
    notebook_path: str,
@@ -191,7 +231,9 @@ def process_and_execute_notebook(

    result = NotebookExecutionResult(
        name=tag,
+        path=notebook,
        duration=datetime.timedelta(seconds=0),
+        start_time=datetime.datetime.now(),
        is_pass=False,
        output_uri=notebook_output_uri,
        log_url="",
@@ -201,7 +243,6 @@ def process_and_execute_notebook(
    )

    # TODO: Handle cases where multiple notebooks have the same name
-    time_start = datetime.datetime.now()
    operation = None
    try:
        # Get the python version for running the notebook if specified
@@ -247,9 +288,10 @@ def process_and_execute_notebook(
        # Block and wait for the result
        operation_result = operation.result(timeout=timeout_in_seconds)

-        result.duration = datetime.datetime.now() - time_start
+        result.duration = datetime.datetime.now() - result.start_time
        result.is_pass = True
        print(f"{notebook} PASSED in {format_timedelta(result.duration)}.")
+
    except Exception as error:
        result.error_message = str(error)

@@ -268,7 +310,7 @@ def process_and_execute_notebook(
            except Exception as error:
                result.error_message = str(error)

-        result.duration = datetime.datetime.now() - time_start
+        result.duration = datetime.datetime.now() - result.start_time
        result.is_pass = False

        print(
@@ -336,12 +378,54 @@ def get_changed_notebooks(

    return notebooks

+def _save_results(results: List[NotebookExecutionResult],
+                  accumulative_results: Dict[str,Any],
+                  artifacts_bucket: str,
+                  results_file: str):
+
+    artifacts_bucket = artifacts_bucket.replace("gs://", "").split('/')[0]
+
+    print("Updating accumulative results ...")
+    for result in results:
+        if result.path in accumulative_results:
+            accumulative_results[result.path]['duration'] = result.duration.total_seconds()
+            accumulative_results[result.path]['start_time'] = str(result.start_time)
+            if result.is_pass:
+                accumulative_results[result.path]['passed'] += 1
+            else:
+                accumulative_results[result.path]['failed'] += 1
+            print(f"updating {result.path}")
+        else:
+            if result.is_pass:
+                pass_count = 1
+                fail_count = 0
+            else:
+                pass_count = 0
+                fail_count = 1
+            accumulative_results[result.path] = {
+                    'duration': result.duration.total_seconds(),
+                    'start_time': str(result.start_time),
+                    'passed': pass_count,
+                    'failed': fail_count
+                    }
+            print(f"adding {result.path}")
+
+    print("Saving accumulative results ...")
+    content = json.dumps(accumulative_results)
+
+    client = storage.Client()
+    bucket = client.get_bucket(artifacts_bucket)
+    bucket.blob(str(results_file)).upload_from_string(content, 'text/json')
+
+

 def process_and_execute_notebooks(
    notebooks: List[str],
    container_uri: str,
    staging_bucket: str,
    artifacts_bucket: str,
+    results_file: str,
+    accumulative_results: List[NotebookExecutionResult],
    should_parallelize: bool,
    timeout: int,
    variable_project_id: str,
@@ -369,6 +453,10 @@ def process_and_execute_notebooks(
            Required. The GCS staging bucket to write source code to.
        artifacts_bucket (str):
            Required. The GCS staging bucket to write executed notebooks to.
+        results_file (str):
+            Required: The path to the artifacts bucket to save results 
+        accumulative_results (List):
+            Required: The in-memory previous accumulative notebook CI/CD test results.
        variable_project_id (str):
            Required. The value for PROJECT_ID to inject into notebooks.
        variable_region (str):
@@ -471,7 +559,7 @@ def process_and_execute_notebooks(
            print("=" * 100)

            build_id = results_sorted[0].build_id
-            logs_bucket_name = (results_sorted[0].logs_bucket).removeprefix("gs://")
+            logs_bucket_name = (results_sorted[0].logs_bucket).replace("gs://", "")
            log_file_name = f"log-{build_id}.txt"

            log_contents = util.download_blob_into_memory(
@@ -489,6 +577,11 @@ def process_and_execute_notebooks(
            else:
                print(log_contents)

+        _save_results(results_sorted, 
+                      accumulative_results,
+                      artifacts_bucket, 
+                      results_file)
+
        print("\n=== END RESULTS===\n")

        total_notebook_duration = functools.reduce(
@@ -36,7 +36,7 @@ steps:
    - -c
    - |
      . workspace/env/bin/activate &&
-      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GPC_VPC_NETWORK_NAME}" `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi`
+      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GPC_VPC_NETWORK_NAME}" `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi` --build_id ${BUILD_ID}
    env:
    - 'IS_TESTING=1'
 timeout: 86400s
@@ -3,12 +3,15 @@ numpy
 jupyter
 nbconvert
 papermill
-pandas
 matplotlib
 tabulate
 google-cloud-aiplatform
 google-cloud-storage
 google-cloud-build
+google-cloud-storage
 ratemate
 GitPython
-tqdm
+tqdm
+fsspec
+pandas
+
@@ -0,0 +1,40 @@
+notebooks/official/training/pytorch_gcs_data_training.ipynb
+notebooks/official/custom/custom_training_tensorboard_profiler.ipynb
+notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
+notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
+notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb
+notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb
+notebooks/official/tabnet/get_started_with_tabnet.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb
+notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb
+notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb
+notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb
+notebooks/official/matching_engine/sdk_matching_engine_create_stack_overflow_embeddings.ipynb
+notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb
+notebooks/official/matching_engine/sdk_matching_engine_create_text_to_image_embeddings.ipynb
+notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb
+notebooks/official/explainable_ai/xai_image_classification_feature_attributions.ipynb
+notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb
+notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb
+notebooks/official/model_registry/get_started_with_model_registry.ipynb
+notebooks/official/model_registry/bqml_vertexai_model_registry.ipynb
+notebooks/official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb
+notebooks/official/model_monitoring/batch_prediction_model_monitoring.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_custom.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_custom_tf_serving.ipynb
+notebooks/official/model_monitoring/model_monitoring.ipynb
+notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb
+notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb
+notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb
+notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb
+notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb
+notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb
+notebooks/official/experiments/comparing_local_trained_models.ipynb
+notebooks/official/automl/automl_image_classification_online_online_prediction.ipynb
+notebooks/official/automl/automl-text-classification.ipynb
+notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb
+notebooks/official/feature_store/sdk-feature-store-pandas.ipynb
+notebooks/official/prediction/custom_batch_prediction_feature_filter.ipynb
+notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb
@@ -0,0 +1,80 @@
+notebooks/official/training/hyperparameter_tuning_tensorflow.ipynb
+notebooks/official/training/get_started_with_vertex_distributed_training.ipynb
+notebooks/official/training/hyperparameter_tuning_xgboost.ipynb
+notebooks/official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb
+notebooks/official/training/distributed_hyperparameter_tuning.ipynb
+notebooks/official/training/pytorch-text-sentiment-classification-custom-train-deploy.ipynb
+notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb
+notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb
+notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb
+notebooks/official/bigquery_ml/bqml-online-prediction.ipynb
+notebooks/official/custom/custom_training_container_and_model_registry.ipynb
+notebooks/official/custom/sdk-custom-image-classification-online.ipynb
+notebooks/official/custom/sdk-custom-image-classification-batch.ipynb
+notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb
+notebooks/official/custom/get_started_vertex_training_xgboost.ipynb
+notebooks/official/custom/get_started_with_vertex_endpoint_and_shared_vm.ipynb
+notebooks/official/custom/SDK_Custom_Container_Prediction.ipynb
+notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb
+notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb
+notebooks/official/vizier/get_started_vertex_vizier.ipynb
+notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
+notebooks/official/pipelines/get_started_with_hpt_pipeline_components.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb
+notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb
+notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb
+notebooks/official/pipelines/get_started_with_machine_management.ipynb
+notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb
+notebooks/official/pipelines/control_flow_kfp.ipynb
+notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb
+notebooks/official/pipelines/pipelines_intro_kfp.ipynb
+notebooks/official/pipelines/automl_tabular_classification_beans.ipynb
+notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb
+notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb
+notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb
+notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb
+notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain_get_metadata.ipynb
+notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb
+notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb
+notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb
+notebooks/official/sdk/SDK_AutoML_Video_Classification.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_automl.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_batch.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb
+notebooks/official/model_monitoring/get_started_with_model_monitoring_xgboost.ipynb
+notebooks/official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb
+notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb
+notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb
+notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb
+notebooks/official/model_evaluation/get_started_with_custom_model_evaluation_import.ipynb
+notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb
+notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb
+notebooks/official/experiments/get_started_with_vertex_experiments.ipynb
+notebooks/official/experiments/comparing_pipeline_runs.ipynb
+notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb
+notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb
+notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb
+notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb
+notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb
+notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb
+notebooks/official/automl/sdk_automl_forecasting_hierarchical_batch.ipynb
+notebooks/official/automl/automl_text_entity_extraction_batch_prediction.ipynb
+notebooks/official/automl/automl_image_classification_batch_prediction.ipynb
+notebooks/official/automl/automl_text_sentiment_analysis_batch_prediction.ipynb
+notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb
+notebooks/official/automl/get_started_automl_training.ipynb
+notebooks/official/automl/automl-tabular-classification.ipynb
+notebooks/official/automl/automl_image_object_detection_export_edge.ipynb
+notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb
+notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb
+notebooks/official/automl/sdk_automl_video_classification_batch.ipynb
+notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb
+notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb
+notebooks/official/automl/automl_image_object_detection_online_prediction.ipynb
+notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb
+notebooks/official/datasets/get_started_bq_datasets.ipynb
+notebooks/official/datasets/get_started_with_data_labeling.ipynb
+notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb
@@ -0,0 +1,46 @@
+# grep PASSED tests.txt | cut -c 10-100 >passed.txt
+
+import os
+
+repo_dir = '/home/jupyter/vertex-ai-samples/'
+repo_dir_len = len(repo_dir)
+official_dir = repo_dir + 'notebooks/official'
+
+entries = os.scandir(official_dir)
+folders = []
+for entry in entries:
+    if entry.is_dir():
+        folders.append(entry.path)
+
+# Passing
+with open('passed.txt', 'r') as pass_file:
+    notebook_names = pass_file.readlines()
+        
+notebooks = []
+for folder in folders:
+    entries = os.scandir(folder)
+    for entry in entries:
+        for notebook in notebook_names:
+            if entry.name == notebook.rstrip():
+                notebooks.append(entry.path[repo_dir_len:])
+                
+with open('passing_tests.txt', 'w') as f:
+    for notebook in notebooks:
+        f.write(notebook + '\n')
+        
+        
+# Failing
+with open('failed.txt', 'r') as fail_file:
+    notebook_names = fail_file.readlines()
+        
+notebooks = []
+for folder in folders:
+    entries = os.scandir(folder)
+    for entry in entries:
+        for notebook in notebook_names:
+            if entry.name == notebook.rstrip():
+                notebooks.append(entry.path[repo_dir_len:])
+                
+with open('failing_tests.txt', 'w') as f:
+    for notebook in notebooks:
+        f.write(notebook + '\n')
@@ -56,3 +56,4 @@
 /notebooks/community/model_garden/model_garden_pytorch_owlvit.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb @xiangxu-google
+/notebooks/community/model_garden/model_garden_pytorch_detectron2.ipynb @lavraicse
@@ -271,7 +271,7 @@
        "\n",
        "\n",
        "def deploy_model(model_id, task):\n",
-        "    model_name = \"blip-image-captioning\"\n",
+        "    model_name = \"blip2\"\n",
        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
        "    serving_env = {\n",
        "        \"MODEL_ID\": model_id,\n",
@@ -345,10 +345,10 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "12893aa2c5af"
+        "id": "80b3fd2ace09"
      },
      "source": [
-        "NOTE: The model weights will be downloaded after the deployment succeeds. When the model is very large it could add 5~15mins additional time before the endpoint is ready for prediction."
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 10 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
      ]
    },
    {
@@ -409,10 +409,10 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "0ac7f8d945e3"
+        "id": "80b3fd2ace09"
      },
      "source": [
-        "NOTE: The model weights will be downloaded after the deployment succeeds. When the model is very large it could add 5~15mins additional time before the endpoint is ready for prediction."
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 10 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
      ]
    },
    {
@@ -332,6 +332,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -332,6 +332,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -314,9 +314,16 @@
      "source": [
        "This section uploads the pre-trained model to Model Registry and deploys it on the Endpoint with 1 T4 GPU.\n",
        "\n",
-        "The model deployment step will take ~15 minutes to complete.\n",
-        "\n",
-        "Once deployed, you can send images and object texts to get classification results."
+        "The model deployment step will take ~15 minutes to complete."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "99f3c6b404b5"
+      },
+      "source": [
+        "### Zero-shot image classification"
      ]
    },
    {
@@ -332,6 +339,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -353,20 +369,87 @@
        "print(preds)"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ccf3714dbe9"
+      },
+      "outputs": [],
+      "source": [
+        "# Undeploy model and delete endpoint.\n",
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "db7ffebdb4be"
+        "id": "ddf9e9ec7b58"
      },
      "source": [
-        "### Clean up resources"
+        "### Image/text feature embedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "2ccf3714dbe9"
+        "id": "8897bbac6887"
+      },
+      "outputs": [],
+      "source": [
+        "model, endpoint = deploy_model(\n",
+        "    model_id=\"openai/clip-vit-base-patch32\", task=\"feature-embedding\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9f854c45f8f3"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed after the above model deployment step succeeds and before you run the next step below. Otherwise you might see a ServiceUnavailable: 503 502:Bad Gateway error when you send requests to the endpoint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2abd54335f36"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Extract feature embedding of images.\n",
+        "image = download_image(\"http://images.cocodataset.org/val2017/000000039769.jpg\")\n",
+        "display(image)\n",
+        "instances = [\n",
+        "    {\"image\": image_to_base64(image)},\n",
+        "]\n",
+        "preds = endpoint.predict(instances=instances).predictions\n",
+        "image_features = np.array(preds[0][\"image_features\"])\n",
+        "print(image_features.shape)\n",
+        "\n",
+        "# Extract feature embedding of texts.\n",
+        "instances = [\n",
+        "    {\"text\": \"two cats\"},\n",
+        "    {\"text\": \"hello world\"},\n",
+        "]\n",
+        "preds = endpoint.predict(instances=instances).predictions\n",
+        "text_features = np.array(preds[0][\"text_features\"])\n",
+        "print(text_features.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "712eb9d0b336"
      },
      "outputs": [],
      "source": [
@@ -278,6 +278,8 @@
      "outputs": [],
      "source": [
        "import base64\n",
+        "import os\n",
+        "from datetime import datetime\n",
        "from io import BytesIO\n",
        "\n",
        "import cv2\n",
@@ -410,6 +412,13 @@
        "# for a full list of training arguments.\n",
        "model = job.run(\n",
        "    args=[\n",
+        "        f\"--num_machines={num_nodes}\",\n",
+        "        f\"--num_processes={num_gpus}\",\n",
+        "        \"--machine_rank=0\",\n",
+        "        \"--mixed_precision=no\",\n",
+        "        \"--gpu_ids=all\",\n",
+        "        \"--same_network\",\n",
+        "        \"--dynamo_backend=no\",\n",
        "        \"controlnet/train_controlnet.py\",\n",
        "        \"--tracker_project_name=train_controlnet\",\n",
        "        f\"--pretrained_model_name_or_path={stable_diffusion_model_id}\",\n",
@@ -477,6 +486,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -520,11 +538,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    },
    {
@@ -558,6 +576,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -352,6 +352,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -332,6 +332,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -351,6 +351,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -510,6 +510,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -585,6 +594,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -510,6 +510,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -0,0 +1,556 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ApNHZJmT2AMH"
+      },
+      "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": "YfxrFG052AMI"
+      },
+      "source": [
+        "# Vertex AI Model Garden - TIMM\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_timm.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_timm.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_timm.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",
+        "    (a Python-3 CPU notebook is recommended)\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "76BCoQcm2AMJ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates finetuning the PyTorch [timm](https://github.com/rwightman/pytorch-image-models) models and deploying the models on [Vertex AI](https://cloud.google.com/vertex-ai).\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Setup environment.\n",
+        "- Create a custom training job on Vertex AI to train or finetune a model.\n",
+        "- Deploy the model on Vertex AI for online prediction.\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) 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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9iU1NKfh2AMJ"
+      },
+      "source": [
+        "## Setup environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7l3GN-QL2AMJ"
+      },
+      "source": [
+        "### Setup 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). 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.\n",
+        "\n",
+        "1. [Enable Artifact Registry](https://cloud.google.com/artifact-registry/docs/enable-service) and [create a repository](https://cloud.google.com/artifact-registry/docs/repositories/create-repos) for storing docker images.\n",
+        "\n",
+        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs.\n",
+        "\n",
+        "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hyAQXmgf2AMK"
+      },
+      "source": [
+        "### Setup required libraries\n",
+        "\n",
+        "It's highly recommended to run this notebook on [Vertex AI workbench](https://cloud.google.com/vertex-ai-workbench), where you don't need to manually install any additional libraries.\n",
+        "\n",
+        "If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk) and [gsutil](https://cloud.google.com/storage/docs/gsutil_install)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-uF7Kb112AMK"
+      },
+      "source": [
+        "### Colab Only\n",
+        "Run the following commands for colab and skip this section if you use workbench."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2lP6dnfy2AMK"
+      },
+      "outputs": [],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
+        "\n",
+        "    # Automatically restart kernel after installs\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)\n",
+        "\n",
+        "    from google.colab import auth as google_auth\n",
+        "\n",
+        "    google_auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fo65Wg9Y2AMK"
+      },
+      "source": [
+        "### Setup environment variables\n",
+        "\n",
+        "This notebook supports models in https://huggingface.co/docs/timm/models.\n",
+        "\n",
+        "You can also run\n",
+        "`python -c \"from timm import list_models; print(list_models(pretrained=True))\"`\n",
+        "locally to see all pretrained models.\n",
+        "\n",
+        "The following models have been manually verified to work with this notebook:\n",
+        "\n",
+        "* vit_tiny_patch16_224\n",
+        "* beit_base_patch16_224\n",
+        "* deit3_small_patch16_224\n",
+        "* efficientnet_b2\n",
+        "* mobilenetv2_100\n",
+        "* resnet50\n",
+        "* resnest50d\n",
+        "* convnext_base\n",
+        "* cspdarknet53\n",
+        "* inception_v4"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3msH2i5V2AMK"
+      },
+      "outputs": [],
+      "source": [
+        "# The cloud project id.\n",
+        "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
+        "# The region for running jobs.\n",
+        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# The model you want to train and serve.\n",
+        "# We use a ViT model as the example.\n",
+        "MODEL_NAME = \"vit_tiny_patch16_224\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# The Cloud Storage bucket name without gs:// prefix for training outputs.\n",
+        "# For example: test_bucket\n",
+        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JlutYfxH2AMK"
+      },
+      "source": [
+        "## Run training jobs\n",
+        "\n",
+        "This section runs a regular training job or a hyperparameter tuning job on Vertex AI.\n",
+        "\n",
+        "Before creating a training job, you need to prepare the dataset for training and evaluation.\n",
+        "\n",
+        "For example, you can use [ImageNet-1K](https://huggingface.co/datasets/imagenet-1k) held on a Cloud Storage bucket as the input dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qy4gKksX2AMK"
+      },
+      "outputs": [],
+      "source": [
+        "# The training docker uri.\n",
+        "TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-timm-train\"\n",
+        "\n",
+        "# The path to data directory on Cloud Storage without gs:// prefix.\n",
+        "# In the form of: <bucket-name>/path-to-data\n",
+        "GCS_DATA_DIR = \"\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0DnlJhO72AMK"
+      },
+      "source": [
+        "### Create a training job on Vertex AI\n",
+        "\n",
+        "This section creates a training job on Vertex AI. If you want to create a hyperparameter tuning job instead, you can skip to the next section."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1R5JWJGS2AML"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "# Init common setup.\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=GCS_BUCKET)\n",
+        "\n",
+        "# Input and output path.\n",
+        "data_dir = f\"/gcs/{GCS_DATA_DIR}\"\n",
+        "output_dir = f\"/gcs/{GCS_BUCKET}/timm\"\n",
+        "\n",
+        "# Worker pool spec.\n",
+        "# Single node with multiple GPUs.\n",
+        "machine_type = \"n1-highmem-32\"\n",
+        "num_nodes = 1\n",
+        "gpu_type = \"NVIDIA_TESLA_P100\"\n",
+        "num_gpus = 4\n",
+        "\n",
+        "# Model specific config.\n",
+        "job_name = f\"pytorch-{MODEL_NAME}\"\n",
+        "batch_size = 32\n",
+        "epochs = 2\n",
+        "\n",
+        "job = aiplatform.CustomContainerTrainingJob(\n",
+        "    display_name=job_name,\n",
+        "    container_uri=TRAIN_DOCKER_URI,\n",
+        ")\n",
+        "model = job.run(\n",
+        "    args=[\n",
+        "        \"--standalone\",\n",
+        "        f\"--nnodes={num_nodes}\",\n",
+        "        f\"--nproc_per_node={num_gpus}\",\n",
+        "        \"train.py\",\n",
+        "        data_dir,\n",
+        "        f\"--model={MODEL_NAME}\",\n",
+        "        \"--pretrained\",\n",
+        "        f\"--output={output_dir}\",\n",
+        "        f\"--batch-size={batch_size}\",\n",
+        "        f\"--epochs={epochs}\",\n",
+        "    ],\n",
+        "    replica_count=num_nodes,\n",
+        "    machine_type=machine_type,\n",
+        "    accelerator_type=gpu_type,\n",
+        "    accelerator_count=num_gpus,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Tf6P7ZI82AML"
+      },
+      "source": [
+        "### Create a hyperparameter tuning job on Vertex AI\n",
+        "\n",
+        "You can use a [hyperparameter tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) job to find the best configuration of your hyperparameters.\n",
+        "\n",
+        "You can skip this section if you already trained a model in the previous section and do not want to tune the hyperparameters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hy_aCff_2AML"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
+        "from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
+        "\n",
+        "# Init common setup.\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=GCS_BUCKET)\n",
+        "\n",
+        "# Input and output path.\n",
+        "data_dir = f\"/gcs/{GCS_DATA_DIR}\"\n",
+        "output_dir = f\"/gcs/{GCS_BUCKET}/timm\"\n",
+        "\n",
+        "# Model specific config.\n",
+        "job_name = f\"pytorch-hp-{MODEL_NAME}\"\n",
+        "batch_size = 32\n",
+        "epochs = 2\n",
+        "\n",
+        "# Worker pool spec.\n",
+        "machine_type = \"n1-highmem-16\"\n",
+        "num_nodes = 1\n",
+        "gpu_type = \"NVIDIA_TESLA_V100\"\n",
+        "num_gpus = 2\n",
+        "worker_pool_specs = [\n",
+        "    {\n",
+        "        \"machine_spec\": {\n",
+        "            \"machine_type\": machine_type,\n",
+        "            \"accelerator_type\": gpu_type,\n",
+        "            \"accelerator_count\": num_gpus,\n",
+        "        },\n",
+        "        \"replica_count\": num_nodes,\n",
+        "        \"container_spec\": {\n",
+        "            \"image_uri\": TRAIN_DOCKER_URI,\n",
+        "            \"args\": [\n",
+        "                \"--standalone\",\n",
+        "                f\"--nnodes={num_nodes}\",\n",
+        "                f\"--nproc_per_node={num_gpus}\",\n",
+        "                \"train.py\",\n",
+        "                data_dir,\n",
+        "                f\"--model={MODEL_NAME}\",\n",
+        "                \"--pretrained\",\n",
+        "                f\"--output={output_dir}\",\n",
+        "                f\"--batch-size={batch_size}\",\n",
+        "                f\"--epochs={epochs}\",\n",
+        "            ],\n",
+        "        },\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "# Hyperparameter job specs.\n",
+        "metric_spec = {\"top1_accuracy\": \"maximize\"}\n",
+        "parameter_spec = {\n",
+        "    \"lr\": hpt.DoubleParameterSpec(min=0.001, max=0.05, scale=\"log\"),\n",
+        "}\n",
+        "max_trial_count = 2\n",
+        "parallel_trial_count = 2\n",
+        "\n",
+        "# Launch jobs.\n",
+        "training_job = aiplatform.CustomJob(\n",
+        "    display_name=job_name, worker_pool_specs=worker_pool_specs\n",
+        ")\n",
+        "hp_job = aiplatform.HyperparameterTuningJob(\n",
+        "    display_name=job_name,\n",
+        "    custom_job=training_job,\n",
+        "    metric_spec=metric_spec,\n",
+        "    parameter_spec=parameter_spec,\n",
+        "    max_trial_count=max_trial_count,\n",
+        "    parallel_trial_count=parallel_trial_count,\n",
+        ")\n",
+        "hp_job.run()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DAyRWwqW2AML"
+      },
+      "source": [
+        "## Deploy model for online prediction\n",
+        "\n",
+        "This section uploads the model to Model Registry and deploys it on an Endpoint resource.\n",
+        "\n",
+        "The model deployment step will take ~15 minutes to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jbNbg0yR2AML"
+      },
+      "outputs": [],
+      "source": [
+        "# The serve docker uri.\n",
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-timm-serve\"\n",
+        "# The port number used by torchserve traffic.\n",
+        "SERVE_PORT = 7080\n",
+        "# The path to model checkpoint file, including gs:// prefix.\n",
+        "MODEL_PT_PATH = \"gs://path_to_model_best.pth.tar\"  # @param {type:\"string\"}\n",
+        "# [Optional] the path to index_to_name.json, including gs:// prefix.\n",
+        "INDEX_TO_NAME_FILE = \"gs://path_to_index_to_name.json\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Converts gs:// uris to /gcs/ to use GCS Fuse.\n",
+        "# See https://github.com/GoogleCloudPlatform/gcsfuse.\n",
+        "MODEL_PT_PATH = MODEL_PT_PATH.replace(\"gs://\", \"/gcs/\")\n",
+        "INDEX_TO_NAME_FILE = INDEX_TO_NAME_FILE.replace(\"gs://\", \"/gcs/\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "INPri3HQ2AML"
+      },
+      "source": [
+        "### Upload and deploy model on Vertex AI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WOvqoAaN2AML"
+      },
+      "outputs": [],
+      "source": [
+        "# Upload model.\n",
+        "serving_env = {\n",
+        "    \"MODEL_NAME\": MODEL_NAME,\n",
+        "    \"MODEL_PT_PATH\": MODEL_PT_PATH,\n",
+        "    \"INDEX_TO_NAME_FILE\": INDEX_TO_NAME_FILE,\n",
+        "}\n",
+        "model = aiplatform.Model.upload(\n",
+        "    display_name=MODEL_NAME,\n",
+        "    serving_container_image_uri=SERVE_DOCKER_URI,\n",
+        "    serving_container_ports=[SERVE_PORT],\n",
+        "    serving_container_predict_route=\"/predictions/timm_serving\",\n",
+        "    serving_container_health_route=\"/ping\",\n",
+        "    serving_container_environment_variables=serving_env,\n",
+        ")\n",
+        "# Or reuse a pre-uploaded model.\n",
+        "# model = aiplatform.Model('projects/123456789/locations/us-central1/models/123456789@1')\n",
+        "\n",
+        "# Create an endpoint.\n",
+        "endpoint = aiplatform.Endpoint.create(display_name=\"pytorch-timm-endpoint\")\n",
+        "# Or reuse a pre-created endpoint.\n",
+        "# endpoint = aiplatform.Endpoint('projects/123456789/locations/us-central1/endpoints/123456789')\n",
+        "\n",
+        "# Deploy model to endpoint.\n",
+        "model.deploy(\n",
+        "    endpoint=endpoint,\n",
+        "    machine_type=\"n1-standard-8\",\n",
+        "    accelerator_type=\"NVIDIA_TESLA_T4\",\n",
+        "    accelerator_count=1,\n",
+        "    traffic_percentage=100,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s2gnYFEJ2AML"
+      },
+      "source": [
+        "You can mange your uploaded models in the [Model Registry](https://pantheon.corp.google.com/vertex-ai/models) and your endpoints in the [Endpoints](https://pantheon.corp.google.com/vertex-ai/endpoints)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5UP84Q5R2AMM"
+      },
+      "source": [
+        "### Test online prediction\n",
+        "\n",
+        "You will now test the deployed endpoint. Please prepare an image to predict."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zL2Qbm2x2AMM"
+      },
+      "outputs": [],
+      "source": [
+        "import base64\n",
+        "\n",
+        "# You can get the deployed endpoint object by its resource name returned by Endpoint.create(). For example:\n",
+        "# endpoint = aiplatform.Endpoint('projects/816369962409/locations/us-central1/endpoints/8809168414485512192')\n",
+        "\n",
+        "# Please upload an image and enter its filename below.\n",
+        "IMAGE_FILENAME = \"cat.jpg\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Alternatively, uncomment the follow line to download a cat image for demonstration.\n",
+        "# ! wget http://images.cocodataset.org/val2017/000000039769.jpg -O cat.jpg\n",
+        "\n",
+        "with open(IMAGE_FILENAME, \"rb\") as f:\n",
+        "    image_b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n",
+        "instances = [{\"data\": {\"b64\": image_b64}}]\n",
+        "\n",
+        "prediction = endpoint.predict(instances=instances)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KqSxSyT42AMM"
+      },
+      "source": [
+        "### Clean Up Resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tMwdxiH-2AMM"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint.undeploy_all()\n",
+        "model.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_pytorch_timm.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -332,6 +332,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -332,6 +332,15 @@
        ")"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80b3fd2ace09"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -57,6 +57,77 @@ The steps performed are:
 &nbsp;&nbsp;&nbsp;Learn more about [BQML ARIMA+ forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview).


+[AutoML training image classification model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_classification_batch_prediction.ipynb)
+
+```
+In this tutorial, you create an AutoML image classification model from a Python script, and then do a batch prediction using the Vertex SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Get predictions from an image classification model](https://cloud.google.com/vertex-ai/docs/image-data/classification/get-predictions).
+
+
+[AutoML training image classification model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_classification_online_prediction.ipynb)
+
+```
+In this tutorial, you create an AutoML image classification model and deploy for online prediction from a Python script using the Vertex SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Get predictions from an image classification model](https://cloud.google.com/vertex-ai/docs/image-data/classification/get-predictions).
+
+
+[AutoML training image object detection model for export to edge](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_object_detection_export_edge.ipynb)
+
+```
+In this tutorial, you create an AutoML image object detection model from a Python script using the Vertex SDK, and then export the model as an Edge model in TFLite format.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- Export the `Edge` model from the `Model` resource to Cloud Storage.
+- Download the model locally.
+- Make a local prediction.
+
+```
+
+
+[AutoML training image object detection model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_image_object_detection_online_prediction.ipynb)
+
+```
+In this tutorial, you create an AutoML image object detection model and deploy for online prediction from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
+
+
 [AutoML Tabular Workflow pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)

 ```
@@ -72,6 +143,38 @@ The steps performed are:
 &nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for E2E AutoML](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).


+[AutoML training text entity extraction model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_text_entity_extraction_batch_prediction.ipynb)
+
+```
+In this tutorial, you create an AutoML text entity extraction model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).
+
+
+[AutoML training text sentiment analysis model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_text_sentiment_analysis_batch_prediction.ipynb)
+
+```
+In this tutorial, you create an AutoML text sentiment analysis model from a Python script, and then do a batch prediction using the Vertex SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+
 [Get started with AutoML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/get_started_automl_training.ipynb)

 ```
@@ -101,9 +204,7 @@ The steps performed include:
 - Create a Vertex AI `TimeSeriesDataset` resource.
 - Train the model.
 - View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
+- Make a batch prediction.

 ```

@@ -60,6 +60,22 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


+[Custom training with custom training container and automatic registering of the model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom_training_container_and_model_registry.ipynb)
+
+```
+In this tutorial, you create a custom model from a Python script in a custom Docker container using the Vertex AI SDK, and automatically register the model in the Vertex AI Model Registry.
+
+The steps performed include:
+
+- Create a Vertex AI custom job for training a model.
+- Train and register a TensorFlow model using a custom container,
+- List the registered model from the Vertex AI Model Registry.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
 [Profile model training performance using Profiler](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom_training_tensorboard_profiler.ipynb)

 ```
@@ -77,6 +93,45 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler).


+[Get started with Vertex AI Training for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/get_started_vertex_training.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for training a XGBoost custom model.
+
+The steps performed include:
+
+- Training using a Python package.
+- Report accuracy when hyperparameter tuning.
+- Save the model artifacts to Cloud Storage using GCSFuse.
+- Create a `Vertex AI Model` resource.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[Get started with Endpoint and shared VM](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/get_started_with_vertex_endpoint_and_shared_vm.ipynb)
+
+```
+Learn how to use deployment resource pools for deploying models.
+
+The steps performed include:
+
+- Upload a pre-trained image classification model as a `Model` resource (model A).
+- Upload a pre-trained text sentence encoder model as a `Model` resource (model B).
+- Create a shared VM deployment resource pool.
+- List shared VM deployment resource pools.
+- Create two `Endpoint` resources.
+- Deploy first model (model A) to first `Endpoint` resource using deployment resource pool.
+- Deploy second model (model B) to second `Endpoint` resource using deployment resource pool.
+- Make a prediction request with first deployed model (model A).
+- Make a prediction request with second deployed model (model B).
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Shared resources across deployments](https://cloud.google.com/vertex-ai/docs/predictions/model-co-hosting).
+
+
 [Custom training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)

 ```
@@ -18,6 +18,8 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro).

+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI for BigQuery users](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+

 [Get started with Vertex AI Data Labeling](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/datasets/get_started_with_data_labeling.ipynb)

@@ -56,6 +56,24 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).


+[Delete Outdated Experiments in Vertex AI TensorBoard](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb)
+
+```
+Learn how to delete outdated TensorBoard Experiments to avoid unnecessary storage costs.
+
+The steps performed include:
+
+- How to delete the TB Experiment with a predefined key-value label pair `<label_key, label_value>`
+
+- How to delete the TB Experiments created before the  `create_time`
+
+- How to delete the TB Experiments created before the  `update_time`
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
+
+
 [Get started with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb)

 ```
@@ -88,3 +106,18 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).

+
+[Autologging](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/autologging.ipynb)
+
+```
+Learn how to use `Vertex AI Autologging`.
+
+The steps performed include:
+
+- Enable autologging in the Vertex AI SDK.
+- Train scikit-learn model and see the resulting experiment run with metrics and parameters autologged to Vertex AI Experiments without setting an experiment run.
+- Train Tensorflow model, check autologged metrics and parameters to Vertex AI Experiments by manually setting an experiment run with `aiplatform.start_run()` and `aiplatform.end_run()`.
+- Disable autologging in the Vertex AI SDK, train a PyTorch model and check that none of the parameters or metrics are logged.
+
+```
+
@@ -628,6 +628,7 @@
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.cloud import aiplatform as vertex_ai\n",
        "from tensorflow.python.keras import Sequential, layers\n",
@@ -967,6 +968,12 @@
        "\n",
        "    # Log final metrics\n",
        "    loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
+        "    if np.isnan(loss):\n",
+        "        loss = 0\n",
+        "    if np.isnan(mae):\n",
+        "        mae = 0\n",
+        "    if np.isnan(mse):\n",
+        "        mse = 0\n",
        "    vertex_ai.log_metrics({\"eval_loss\": loss, \"eval_mae\": mae, \"eval_mse\": mse})\n",
        "\n",
        "    vertex_ai.end_run()"
@@ -1,669 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ur8xi4C7S06n"
-      },
-      "outputs": [],
-      "source": [
-        "# Copyright 2022 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": [
-        "# Delete Outdated Experiments in Vertex AI TensorBoard\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/experiments/delete_outdated_tensorboard_experiments.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/official/experiments/delete_outdated_tensorboard_experiments.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/vertex-ai-samples/main/notebooks/official/experiments/delete_outdated_tensorboard_experiments.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>"
-      ]
-    },
-    {
-      "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": {
-        "id": "tvgnzT1CKxrO"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "Vertex AI will have a new Vertex AI TensorBoard billing model. From August 2023, it will change from monthly `$300/user` to monthly `$10/GB`. In preparation for this change, users need to delete old Vertex AI TensorBoard Experiments to avoid unnecessary storage costs when the pricing change takes place.\n",
-        "\n",
-        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d975e698c9a4"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to delete outdated TensorBoard Experiments to avoid unnecessary storage costs.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
-        "\n",
-        "- Vertex AI Tensorboard\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- How to delete the TB Experiment with a predefined key-value label pair `<label_key, label_value>`\n",
-        "\n",
-        "- How to delete the TB Experiments created before the  `create_time`\n",
-        "\n",
-        "- How to delete the TB Experiments created before the  `update_time`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "08d289fa873f"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "No dataset is used."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aed92deeb4a0"
-      },
-      "source": [
-        "### 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), and [Cloud Storage pricing](https://cloud.google.com/storage/pricing),\n",
-        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i7EUnXsZhAGF"
-      },
-      "source": [
-        "## Installation\n",
-        "\n",
-        "Install the following packages required to execute this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2b4ef9b72d43"
-      },
-      "outputs": [],
-      "source": [
-        "# Install the packages\n",
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    USER = \"--user\"\n",
-        "else:\n",
-        "    USER = \"\"\n",
-        "! pip3 install {USER} --upgrade google-cloud-aiplatform"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "58707a750154"
-      },
-      "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "f200f10a1da3"
-      },
-      "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)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BF1j6f9HApxa"
-      },
-      "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)."
-      ]
-    },
-    {
-      "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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "oM1iC_MfAts1"
-      },
-      "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": "QAup21nz6LHk"
-      },
-      "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 permissions to your service account at https://cloud.google.com/marketplace/docs/grant-service-account-access."
-      ]
-    },
-    {
-      "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": "NUJOpq7_-nt6"
-      },
-      "source": [
-        "### Define constants\n",
-        "\n",
-        "Define variables you use in this tutorial. In particular, you set\n",
-        "\n",
-        "- `CREATE_TIME_CUT` : delete tensorboard experiments that were created before CREATE_TIME_CUT. For example, `2022-12-31`.\n",
-        "\n",
-        "- `UPDATE_TIME_CUT` : delete tensorboard experiments that were created before UPDATE_TIME_CUT. For example, `2022-12-31`.\n",
-        "\n",
-        "- `DETAILED_LOG` : a booled variable to see Tensorboard deletion progress. If True, it shows progress by experiments. Otherwise, it reports progress per 100 experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0AkyoMre-qe5"
-      },
-      "outputs": [],
-      "source": [
-        "CREATE_TIME_CUT = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "UPDATE_TIME_CUT = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "DETAILED_LOG = True  # @param {type: \"boolean\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "n68J5KJ4ERbF"
-      },
-      "source": [
-        "### Define Helpers"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "JIXC-tVrETET"
-      },
-      "outputs": [],
-      "source": [
-        "def clean_up_by_label(tensorboard_instance, label_key, label_value):\n",
-        "    \"\"\"Delete the TB Experiment with the key-value label pair <label_key, label_value>\"\"\"\n",
-        "    # List tensorboard experiments\n",
-        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
-        "        tensorboard_name=tensorboard_instance.resource_name\n",
-        "    )\n",
-        "    # Get the number of tensorboard experiments\n",
-        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
-        "    # For each experiment\n",
-        "    for i in range(num_tensorboard_experiments):\n",
-        "        tensorboard_experiment = tensorboard_experiments[i]\n",
-        "        if detailed_log or (i % 100 == 0):\n",
-        "            print(\n",
-        "                f\">>>checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
-        "            )\n",
-        "        # Get experiment labels\n",
-        "        labels = tensorboard_experiment.labels\n",
-        "        # Filter by label\n",
-        "        if label_key in labels and labels[label_key] == label_value:\n",
-        "            # Delete experiment\n",
-        "            tensorboard_experiment.delete()\n",
-        "\n",
-        "\n",
-        "def clean_up_by_create_time(tensorboard_instance, create_time_cut):\n",
-        "    \"\"\"Delete the TB Experiment with the `create_time`\"\"\"\n",
-        "    # List tensorboard experiments\n",
-        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
-        "        tensorboard_name=tensorboard_instance.resource_name, order_by=\"create_time\"\n",
-        "    )\n",
-        "    # Get the number of tensorboard experiments\n",
-        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
-        "    # For each experiment\n",
-        "    for i in range(num_tensorboard_experiments):\n",
-        "        tensorboard_experiment = tensorboard_experiments[i]\n",
-        "        if detailed_log or (i % 100 == 0):\n",
-        "            print(\n",
-        "                f\">>> checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
-        "            )\n",
-        "        # Filter by create_time\n",
-        "        if str(tensorboard_experiment.create_time) < create_time_cut:\n",
-        "            # Delete experiment\n",
-        "            tensorboard_experiment.delete()\n",
-        "        else:\n",
-        "            break\n",
-        "\n",
-        "\n",
-        "def clean_up_by_update_time(tensorboard_instance, update_time_cut):\n",
-        "    \"\"\"Delete the TB Experiment with with the `update_time`\"\"\"\n",
-        "    # List tensorboard experiments\n",
-        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
-        "        tensorboard_name=tensorboard_instance.resource_name, order_by=\"update_time\"\n",
-        "    )\n",
-        "    # Get the number of tensorboard experiments\n",
-        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
-        "    # For each experiment\n",
-        "    for i in range(num_tensorboard_experiments):\n",
-        "        tensorboard_experiment = tensorboard_experiments[i]\n",
-        "        if detailed_log or (i % 100 == 0):\n",
-        "            print(\n",
-        "                f\">>> checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
-        "            )\n",
-        "        # Filter by update_time\n",
-        "        if str(tensorboard_experiment.update_time) < update_time_cut:\n",
-        "            tensorboard_experiment.delete()\n",
-        "        else:\n",
-        "            break"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UCUOlTLYCT0B"
-      },
-      "source": [
-        "## Delete outdated Vertex AI TensorBoard Experiments"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EGfFVHhWDjkl"
-      },
-      "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": "G1l-7Wft3jb6"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e4672ef33691"
-      },
-      "source": [
-        "### Set delete outdated TensorBoard experiments \n",
-        "\n",
-        "Initialize a flag variable to start deleting outdated TensorBoard experiments and a flag variable to choose the deleting method. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8cdc326c9823"
-      },
-      "outputs": [],
-      "source": [
-        "delete_tb_experiments = False  # @param {type: \"boolean\"}\n",
-        "\n",
-        "delete_method = \"\"  # @param [\"by_label\", \"by_create_time\", \"by_update_time\"]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JjJVARe5V-MG"
-      },
-      "source": [
-        "### Delete an TensorBoard instance\n",
-        "\n",
-        "To delete a Tensorboard instance, you need `TENSORBOARD_INSTANCE` ID which uniquely identifies the Tensorboard instance where you run experiments.\n",
-        "\n",
-        "To get the Tensorboard instance ID, you can either\n",
-        "\n",
-        "- go to the cloud console UI, Vertex AI > Experiments > Tensorboard Instances, or\n",
-        "- use the list command below to list all TensorBoard instances for your project and region.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "eqM2pIO8_4Y7"
-      },
-      "outputs": [],
-      "source": [
-        "tensorboard_instances = aiplatform.Tensorboard.list(project=PROJECT_ID, location=REGION)\n",
-        "print(tensorboard_instances)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wT_uPArACMPX"
-      },
-      "source": [
-        "Set the tensorboard instance id for which you want to delete experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "hUync8E9CLcm"
-      },
-      "outputs": [],
-      "source": [
-        "if delete_tb_experiments:\n",
-        "\n",
-        "    TENSORBOARD_INSTANCE_ID = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "    TENSORBOARD_INSTANCE = aiplatform.Tensorboard(\n",
-        "        project=PROJECT_ID, location=REGION, tensorboard_name=TENSORBOARD_INSTANCE_ID\n",
-        "    )\n",
-        "    print(TENSORBOARD_INSTANCE)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OOp22fxdHqmT"
-      },
-      "source": [
-        "### Delete a TensorBoard Experiment with the key-value label pair\n",
-        "\n",
-        "You delete a TensorBoard experiment using a predefined `label_key` and `label_value`. For example, you may have assigned `delete` label key and `true` label value to indicate all Tensorboard experiments you want to delete.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "N7KajCsjij1g"
-      },
-      "outputs": [],
-      "source": [
-        "LABEL_KEY = \"delete\"  # @param {type:\"string\"}\n",
-        "LABEL_VALUE = \"true\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "N45samDzWBO2"
-      },
-      "outputs": [],
-      "source": [
-        "if delete_tb_experiments and delete_method == \"by_label\":\n",
-        "    clean_up_by_label(TENSORBOARD_INSTANCE, LABEL_KEY, LABEL_VALUE)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XrEVqlgSIL6K"
-      },
-      "source": [
-        "### Delete a TensorBoard Experiment with `create_time`\n",
-        "\n",
-        "You delete a TensorBoard experiment using `create_time` field"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4_bjNeQ7p87A"
-      },
-      "outputs": [],
-      "source": [
-        "if delete_tb_experiments and delete_method == \"by_create_time\":\n",
-        "    clean_up_by_create_time(TENSORBOARD_INSTANCE, CREATE_TIME_CUT)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZGNlSbIrIP_o"
-      },
-      "source": [
-        "### Delete a TensorBoard Experiment with `update_time`\n",
-        "\n",
-        "You delete a TensorBoard Experiment using a predefined `update_time` field"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SDyc_a8XwEve"
-      },
-      "outputs": [],
-      "source": [
-        "if delete_tb_experiments and delete_method == \"by_update_time\":\n",
-        "    clean_up_by_update_time(TENSORBOARD_INSTANCE, UPDATE_TIME_CUT)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "delete_outdated_tensorboard_experiments.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
@@ -192,7 +192,7 @@
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q"
+        "! pip3 install --upgrade google-cloud-aiplatform pandas {USER_FLAG} -q"
      ]
    },
    {
@@ -60,7 +60,7 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).


-[Custom training image classification model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)
+[Custom training image classification model for online prediction with explainability](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)

 ```
 Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.
@@ -1,5 +1,5 @@

-[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb)
+[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb)

 ```
 Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.
@@ -18,15 +18,14 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Classification for image data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_images).


-[Custom Scikit-Learn model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb)
+[AutoML image object detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-object-detection-batch-online.ipynb)

 ```
-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.
+Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.

 The steps performed include:

- Create a `Vertex AI` custom job for training a scikit-learn model.
- Upload the trained model artifacts as a `Model` resource.
+- Train an AutoML object detection model.
 - Make a batch prediction.
 - Deploy model to a endpoint
 - Make a online prediction
@@ -35,40 +34,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).

-&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb)
-
-```
-Learn to use `Vertex AI Hyperparameter` to create and tune a custom trained model.
-
-The steps performed include:
-
- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[AutoML Video Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
-
-```
-Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
-
-The steps performed include:
-
- Train an AutoML video classification model.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
+&nbsp;&nbsp;&nbsp;Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).


 [AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb)
@@ -88,55 +54,7 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos).


-[Custom Image Classification w/pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb)
-
-```
-Learn how to train a tensorflow image classification model using a prebuilt container and Vertex AI training.
-
-The steps performed include:
-
- *Package the training code into a python application.*
- *Containerize the training application using Cloud Build and Artifact Registry.*
- *Create a custom container training job in Vertex AI and run it.*
- *Evaluate the model generated from the training job.*
- *Create a model resource for the trained model in Vertex AI Model Registry.*
- *Run a Vertex AI batch prediction job.*
- *Deploy the model resource to a Vertex AI Endpoint.*
- *Run a online prediction job on the model resource.*
- *Clean up the resources created.*
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[Custom Image Classification w/custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb)
-
-```
-Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
-
-The steps performed include:
-
- *Package the training code into a python application.*
- *Containerize the training application using Cloud Build and Artifact Registry.*
- *Create a custom container training job in Vertex AI and run it.*
- *Evaluate the model generated from the training job.*
- *Create a model resource for the trained model in Vertex AI Model Registry.*
- *Run a Vertex AI batch prediction job.*
- *Deploy the model resource to a Vertex AI Endpoint.*
- *Run a online prediction job on the model resource.*
- *Clean up the resources created.*
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[AutoML Tabular Binary Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb)
+[AutoML tabular binary classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb)

 ```
 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.
@@ -157,26 +75,7 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).


-[AutoML Image Object Detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb)
-
-```
-Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.
-
-The steps performed include:
-
- Train an AutoML object detection model.
- Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
-
-
-[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb)
+[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb)

 ```
 The objective of this notebook is to build a AutoML Text Classification Model.
@@ -198,7 +97,7 @@ The steps performed include the following:
 &nbsp;&nbsp;&nbsp;Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text).


-[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
+[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb)

 ```
 The objective of this notebook is to build a AutoML Text Entity Extraction model.
@@ -241,7 +140,72 @@ The steps performed include the following:
 &nbsp;&nbsp;&nbsp;Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).


-[Custom XGBoost model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb)
+[AutoML Video Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
+
+```
+Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
+
+The steps performed include:
+
+- Train an AutoML video classification model.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
+
+
+[Custom image classification with a custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-custom-container.ipynb)
+
+```
+Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
+
+The steps performed include:
+
+- *Package the training code into a python application.*
+- *Containerize the training application using Cloud Build and Artifact Registry.*
+- *Create a custom container training job in Vertex AI and run it.*
+- *Evaluate the model generated from the training job.*
+- *Create a model resource for the trained model in Vertex AI Model Registry.*
+- *Run a Vertex AI batch prediction job.*
+- *Deploy the model resource to a Vertex AI Endpoint.*
+- *Run a online prediction job on the model resource.*
+- *Clean up the resources created.*
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[Custom image classification with a pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb)
+
+```
+Learn how to train a tensorflow image classification model using a prebuilt container and Vertex AI training.
+
+The steps performed include:
+
+- *Package the training code into a python application.*
+- *Containerize the training application using Cloud Build and Artifact Registry.*
+- *Create a custom container training job in Vertex AI and run it.*
+- *Evaluate the model generated from the training job.*
+- *Create a model resource for the trained model in Vertex AI Model Registry.*
+- *Run a Vertex AI batch prediction job.*
+- *Deploy the model resource to a Vertex AI Endpoint.*
+- *Run a online prediction job on the model resource.*
+- *Clean up the resources created.*
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[Custom Scikit-Learn model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-scikit-learn-prebuilt-container.ipynb)

 ```
 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.
@@ -260,3 +224,39 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).

+
+[Custom XGBoost model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-xgboost-prebuilt-container.ipynb)
+
+```
+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.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a scikit-learn model.
+- Upload the trained model artifacts as a `Model` resource.
+- Make a batch prediction.
+- Deploy model to a endpoint
+- Make a online prediction
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-hyperparameter-tuning.ipynb)
+
+```
+Learn to use `Vertex AI Hyperparameter` to create and tune a custom trained model.
+
+The steps performed include:
+
+- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb\">\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb\">\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",
        "    </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/UJ1 Vertex SDK AutoML Image Classification.ipynb\" target='_blank'>\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",
@@ -1458,7 +1458,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ1 Vertex SDK AutoML Image Classification.ipynb",
+      "name": "sdk-automl-image-classification-batch-online.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-object-detection-batch-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",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-object-detection-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",
        "    </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/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\" target='_blank'>\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-object-detection-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",
@@ -1454,7 +1454,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ5 Vertex SDK AutoML Image Object Detection.ipynb",
+      "name": "sdk-automl-image-object-detection-batch-online.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -633,8 +633,9 @@
        "    gcs_source=[IMPORT_FILE],\n",
        "    import_schema_uri=aip.schema.dataset.ioformat.video.object_tracking,\n",
        ")\n",
-        "if os.getenv('IS_TESTING'):\n",
+        "if os.getenv(\"IS_TESTING\"):\n",
        "    import time\n",
+        "\n",
        "    time.sleep(30)\n",
        "\n",
        "print(dataset.resource_name)"
@@ -1251,7 +1252,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ15 Vertex SDK AutoML Object Tracking.ipynb",
+      "name": "sdk-automl-object-tracking-batch-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb\">\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb\">\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",
        "    </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/UJ4 Vertex SDK AutoML Tabular Binary Classification.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/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",
        "    </a>\n",
@@ -1410,7 +1410,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb",
+      "name": "sdk-automl-tabular-binary-classification-online-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.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",
        "    </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/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\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-text-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",
        "    </a>\n",
@@ -1337,7 +1337,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ6 Vertex SDK AutoML Text Classification.ipynb",
+      "name": "sdk-automl-text-classification-batch-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.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",
        "    </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/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\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-text-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",
        "    </a>\n",
@@ -1428,7 +1428,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb",
+      "name": "sdk-automl-text-entity-extraction-batch-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -1413,7 +1413,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb",
+      "name": "sdk-automl-text-sentiment-analysis-batch-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -1297,7 +1297,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ14 Vertex SDK AutoML Video Classification.ipynb",
+      "name": "sdk-automl-video-classification-batch-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\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",
        "    </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/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
@@ -1899,7 +1899,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb",
+      "name": "sdk-custom-image-classification-custom-container.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb\">\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb\">\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",
        "    </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/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb\" target='_blank'>\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",
@@ -2005,7 +2005,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb",
+      "name": "sdk-custom-image-classification-prebuilt-container.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\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",
        "    </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/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
@@ -1577,7 +1577,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb",
+      "name": "sdk-custom-scikit-learn-prebuilt-container.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\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",
        "    </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/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\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",
        "    </a>\n",
@@ -1459,7 +1459,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb",
+      "name": "sdk-custom-xgboost-prebuilt-container.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb\">\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",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb\">\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",
        "    </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/UJ11 Vertex SDK Hyperparameter Tuning.ipynb\" target='_blank'>\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",
        "    </a>\n",
@@ -1414,7 +1414,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ11 Vertex SDK Hyperparameter Tuning.ipynb",
+      "name": "sdk-hyperparameter-tuning.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -50,7 +50,7 @@ Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to
 The steps performed include:

 - Create a Vertex AI `Dataset`.
- Train a Automl Tabular Classification model on the `Dataset` resource.
+- Train a Automl Text Classification model on the `Dataset` resource.
 - Import the trained `AutoML model resource` into the pipeline.
 - Run a `Batch Prediction` job.
 - Evaulate the AutoML model using the `Classification Evaluation Component`.
@@ -152,3 +152,5 @@ The steps performed include:

 ```

+&nbsp;&nbsp;&nbsp;Learn more about [Model evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
+
@@ -23,7 +23,7 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).


-[Challenger vs Blessed methodology for model deployment into production](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/pipelines/challenger_vs_blessed_deployment_method.ipynb)
+[Challenger vs Blessed methodology for model deployment into production](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb)

 ```
 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.
@@ -45,6 +45,10 @@ The steps performed include:

 ```

+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Model evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
+

 [Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)

@@ -84,7 +88,7 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Custom training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline).


-[Training and batch prediction with BigQuery source and destinantion for a custom tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb)
+[Training and batch prediction with BigQuery source and destination for a custom tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb)

 ```
 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`.
@@ -110,6 +114,43 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction components](https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component).


+[Get started with Vertex AI Hyperparameter Tuning pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/get_started_with_hpt_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Hyperparameter Tuning`.
+
+The steps performed include:
+
+- Construct a pipeline for:
+    - Hyperparameter tune/train a custom model.
+    - Retrieve the tuned hyperparameter values and metrics to optimize.
+    - If the metrics exceed a specified threshold.
+      - Get the location of the model artifacts for the best tuned model.
+      - Upload the model artifacts to a `Vertex AI Model` resource.
+- Execute a Vertex AI pipeline.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+
+[Get started with machine management for Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/get_started_with_machine_management.ipynb)
+
+```
+Learn how to convert a self-contained custom training component into a `Vertex AI CustomJob`, whereby:
+
+The steps performed in this tutorial include:
+
+- Create a custom component with a self-contained training job.
+- Execute pipeline using component-level settings for machine resources
+- Convert the self-contained training component into a `Vertex AI CustomJob`.
+- Execute pipeline using customjob-level settings for machine resources
+
+```
+
+
 [AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)

 ```
@@ -298,6 +339,32 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).


+[Multicontender vs Champion methodology for model deployment into production](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb)
+
+```
+Learn how to construct a Vertex AI pipeline, which evaluates new production data from a deployed  model against other versions  of the model, to determine if a contender model becomes the champion model for replacement in production.
+
+The steps performed include:
+
+- Import a pretrained (champion) model to the `Vertex AI Model Registry`.
+- Import synthetic model training evaluation metrics to the corresponding (champion) model.
+- Create a `Vertex AI Endpoint` resource
+- Deploy the champion model to the `Endpoint` resource.
+- Import additional (contender) versions of the deployed model.
+- Import synthetic model training evaluation metrics to the corresponding (contender) models.
+- Create a Vertex AI Pipeline
+    - Get the champion model.
+    - (Fake) Fine-tune champion model with production data
+    - Import synthetic train+production evaluation metrics for the champion model.
+    - Get the contender models.
+    - (Fake) Fine-tune contender model with production data
+    - Import synthetic train+production evaluation metrics for the contenders modesl.
+    - Compare the evaluations of the contenders to the champion and set the new champion as the default.
+    - Deploy the new champion model.
+
+```
+
+
 [Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)

 ```
@@ -265,7 +265,7 @@
      "source": [
        "# Install Python package dependencies.\n",
        "print(\"Installing libraries\")\n",
-        "! pip3 install {USER_FLAG} --quiet google-cloud-pipeline-components kfp\n",
+        "! pip3 install {USER_FLAG} --quiet 'google-cloud-pipeline-components==1.0.20' 'kfp<2'\n",
        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-aiplatform google-cloud-bigquery"
      ]
    },
@@ -14,3 +14,40 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).

+
+[Serving PyTorch image models with prebuilt containers on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb)
+
+```
+Learn how to package and deploy a PyTorch image classification model using a prebuilt Vertex AI container with TorchServe for serving online and batch predictions.
+
+The steps performed include:
+
+- Download a pretrained image model from PyTorch
+- Create a custom model handler
+- Package model artifacts in a model archive file
+- Upload model for deployment
+- Deploy model for prediction
+- Make online predictions
+- Make batch predictions
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Pre-built containers for prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers).
+
+
+[Train and deploy PyTorch models with prebuilt containers on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/pytorch_train_deploy_models_with_prebuilt_containers.ipynb)
+
+```
+Learn how to build, train and deploy a PyTorch image classification model using prebuilt containers for custom training and prediction.
+
+The steps performed include:
+
+- Package training application into a Python source distribution
+- Configure and run training job in a prebuilt container
+- Package model artifacts in a model archive file
+- Upload model for deployment
+- Deploy model using a prebuilt container for prediction
+- Make online predictions
+
+```
+
@@ -15,6 +15,26 @@ The steps performed are:
 &nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).


+[Get started with TabNet builtin algorithm for training tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/get_started_with_tabnet.ipynb)
+
+```
+Learn how to run `Vertex AI TabNet` built algorithm for training custom tabular models.
+
+The steps performed include:
+
+- Get the training data.
+- Configure training parameters for the `Vertex AI TabNet` container.
+- Train the model using `Vertex AI Training` using CSV data.
+- Upload the model as a `Vertex AI Model` resource.
+- Deploy the `Vertex AI Model` resource to a `Vertex AI Endpoint` resource.
+- Make a prediction with the deployed model.
+- Hyperparameter tuning the `Vertex AI TabNet` model.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+
 [Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)

 ```
@@ -14,6 +14,8 @@ The steps performed are:

 &nbsp;&nbsp;&nbsp;Learn more about [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction).

+&nbsp;&nbsp;&nbsp;Learn more about [Prophet for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-prophet).
+

 [TabNet Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb)

@@ -10,3 +10,19 @@ Learn how to use `Vertex AI Vizier` to optimize a multi-objective study.

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview).

+
+[Get started with Vertex AI Vizier](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/get_started_vertex_vizier.ipynb)
+
+```
+Learn how to use `Vertex AI Vizier` for when training with `Vertex AI`.
+
+The steps performed include:
+
+- Hyperparameter tuning with Random algorithm.
+- Hyperparameter tuning with Vizier (Bayesian) algorithm.
+- Suggesting trials and updating results for Vizier study
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview).
+
@@ -81,7 +81,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/managed/visualize-data-bigquery).

 &nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).

@@ -127,7 +127,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml#machine_learning_directly_in).


 [Inventory prediction on ecommerce data using Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/inventory-prediction/inventory_prediction.ipynb)
@@ -194,7 +194,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml#machine_learning_directly_in).


 [Sentiment Analysis using AutoML Natural Language and Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/sentiment_analysis/Sentiment_Analysis.ipynb)
@@ -218,7 +218,7 @@ The steps performed are:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/tutorials/text-classification-automl/training).
+&nbsp;&nbsp;&nbsp;Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).


 [Digest and analyze data from BigQuery with Dataproc](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_bigquery.ipynb)
Author	SHA1	Message	Date
Andrew Ferlitsch	0688753f23	fix: remove pandas inmport	2023-05-05 15:24:49 +00:00
Andrew Ferlitsch	238c82b61f	update: use util to read from bucket	2023-05-04 21:22:44 +00:00
Andrew Ferlitsch	df79e810ff	fix: revert to JSON	2023-05-04 21:02:44 +00:00
Andrew Ferlitsch	7831d443df	update: NotbookExecutionResult updates	2023-05-04 18:08:36 +00:00
Andrew Ferlitsch	a3d190a4a4	review: JSON simplifing	2023-05-04 17:49:25 +00:00
Andrew Ferlitsch	f868bd6fbb	review: JSON simplifing	2023-05-04 16:51:54 +00:00
Andrew Ferlitsch	72d89a826e	fix: use json format	2023-05-03 19:42:08 +00:00
Andrew Ferlitsch	f89b096643	fix: build_id required	2023-05-03 17:58:33 +00:00
Andrew Ferlitsch	eee2b916f8	fix: review	2023-05-03 17:33:46 +00:00
Andrew Ferlitsch	0ba366f4cb	fix: biz logic	2023-05-02 21:01:26 +00:00
Andrew Ferlitsch	febb38f29d	fix: biz logic	2023-05-02 20:50:08 +00:00
Andrew Ferlitsch	f0e8e5ec61	fix: review	2023-05-01 21:56:43 +00:00
Andrew Ferlitsch	d61d8e81cf	fix: review	2023-05-01 20:13:01 +00:00
Andrew Ferlitsch	50abb8ac15	fix: review	2023-05-01 20:00:41 +00:00
Andrew Ferlitsch	c08b27f30b	fix: BUILD_ID	2023-05-01 19:53:46 +00:00
Andrew Ferlitsch	cbd8688c00	fix: BUILD_ID	2023-05-01 18:24:38 +00:00
Andrew Ferlitsch	a206e27a48	fix: BUILD_ID	2023-05-01 18:04:26 +00:00
Andrew Ferlitsch	290289cfb9	fix: BUILD_ID	2023-05-01 17:25:55 +00:00
Andrew Ferlitsch	bcc4b57d78	fix: BUILD_ID	2023-05-01 17:03:17 +00:00
Andrew Ferlitsch	341674f08b	fix: BUILD_ID	2023-05-01 16:35:25 +00:00
Andrew Ferlitsch	c88efc023f	fix: BUILD_ID	2023-05-01 16:07:56 +00:00
Andrew Ferlitsch	a1954476b7	fix: flaky	2023-04-28 19:50:21 +00:00
Andrew Ferlitsch	2079f5281c	debug: duration nit	2023-04-28 15:49:24 +00:00
Andrew Ferlitsch	2551d6dc51	debug: duration nit	2023-04-28 15:32:53 +00:00
Andrew Ferlitsch	efe4d623c7	debug: casting	2023-04-28 14:15:54 +00:00
Andrew Ferlitsch	4b57466385	debug: casting	2023-04-28 04:24:11 +00:00
Andrew Ferlitsch	33de79d983	debug: casting	2023-04-28 04:03:42 +00:00
Andrew Ferlitsch	529a461b1b	debug: casting	2023-04-28 03:21:04 +00:00
Andrew Ferlitsch	3c9c21c204	debug: accum	2023-04-28 00:45:39 +00:00
Andrew Ferlitsch	bb69d6482c	debug: accum	2023-04-28 00:09:07 +00:00
Andrew Ferlitsch	d3ddcbf936	debug: accum	2023-04-27 23:26:39 +00:00
Andrew Ferlitsch	fe8bdd2bb1	debug: accum	2023-04-27 23:07:41 +00:00
Andrew Ferlitsch	7c7e81e58a	debug: accum	2023-04-27 22:04:28 +00:00
Andrew Ferlitsch	10c08c88c6	debug: accum	2023-04-27 21:45:31 +00:00
Andrew Ferlitsch	5b385a8517	debug: accum	2023-04-27 21:26:19 +00:00
Andrew Ferlitsch	dca60a6d81	debug: indexer	2023-04-27 21:07:11 +00:00
Andrew Ferlitsch	4f636e4b66	debug: indxer	2023-04-27 20:36:08 +00:00
Andrew Ferlitsch	922cec34e9	debug: read csv	2023-04-27 20:12:02 +00:00
Andrew Ferlitsch	58f282c431	debug: read csv	2023-04-27 19:53:29 +00:00
Andrew Ferlitsch	92c05baa80	debug: read csv	2023-04-27 19:34:29 +00:00
Andrew Ferlitsch	3fc657a65b	debug: read csv	2023-04-27 19:32:57 +00:00
Andrew FerlitschandGitHub	18c4d05fa7	Update requirements.txt	2023-04-27 12:10:27 -07:00
Andrew FerlitschandGitHub	7383d06d6b	Update requirements.txt	2023-04-27 11:50:14 -07:00
Andrew Ferlitsch	d490e7afec	debug: load	2023-04-27 18:31:01 +00:00
Andrew Ferlitsch	991ce4a7bf	debug: import issues	2023-04-27 17:34:05 +00:00
Andrew Ferlitsch	357442990b	debug: pandas problem	2023-04-27 17:14:42 +00:00
Andrew Ferlitsch	f6d3d87914	debug: matching notebook name	2023-04-27 16:52:52 +00:00
Andrew Ferlitsch	ba426fd189	debug: not accumulating	2023-04-27 16:28:54 +00:00
Andrew Ferlitsch	c483767f7d	debug: not accumulating	2023-04-27 16:28:07 +00:00
Andrew Ferlitsch	2aee13899b	debug: not accumulating	2023-04-27 16:27:17 +00:00
Andrew FerlitschandGitHub	1b2663a2a2	Update requirements.txt	2023-04-27 09:09:25 -07:00
Andrew Ferlitsch	724349332a	debug: not accumulating	2023-04-27 15:50:33 +00:00
Andrew Ferlitsch	4dd7885518	debug: not accumulating	2023-04-27 15:08:41 +00:00
Andrew Ferlitsch	718b6438b5	debug: not accumulating	2023-04-27 14:11:48 +00:00
Andrew Ferlitsch	42436fc5f4	debug: not accumulating	2023-04-26 23:41:29 +00:00
Andrew Ferlitsch	e80d660631	debug: no entries written	2023-04-26 23:22:07 +00:00
Andrew Ferlitsch	047959a82e	debug: no entries written	2023-04-26 22:58:27 +00:00
Andrew Ferlitsch	dee4822298	debug: bucket issue	2023-04-26 22:34:38 +00:00
Andrew Ferlitsch	73929c5681	debug: bucket issue	2023-04-26 22:14:40 +00:00
Andrew Ferlitsch	1bc349c40f	debug: bucket issue	2023-04-26 21:54:27 +00:00
Andrew Ferlitsch	b715c9063c	debug: bucket issue	2023-04-26 20:59:16 +00:00
Andrew Ferlitsch	d28c7941ab	debug: bucket issue	2023-04-26 20:35:09 +00:00
Andrew Ferlitsch	018865c59b	debug: bucket issue	2023-04-26 19:48:48 +00:00
Andrew Ferlitsch	aff204e286	fix: 2nd try at bucket issue	2023-04-26 19:26:14 +00:00
Andrew Ferlitsch	6eb7ee259a	fix: 2nd try at bucket issue	2023-04-26 19:04:26 +00:00
Andrew Ferlitsch	47b3d72158	fix: ci/cd test on openin artifacts bucket	2023-04-26 18:34:01 +00:00
Andrew Ferlitsch	1e8a5b581d	fix: tuning args	2023-04-26 16:45:11 +00:00
Andrew Ferlitsch	9515375c49	fix: gs bucket ptrfix	2023-04-26 16:25:39 +00:00
Andrew Ferlitsch	6d23ea0423	fix: args for results file	2023-04-26 15:35:54 +00:00
Andrew Ferlitsch	1f9dbb5543	fix: use args for where to store results	2023-04-26 15:25:37 +00:00
Andrew Ferlitsch	63ebfa12ad	update: accumulator support	2023-04-25 22:22:18 +00:00
Andrew Ferlitsch	e57c8b8fcb	fix: test percent	2023-04-25 18:22:51 +00:00
Andrew Ferlitsch	df59b7400a	update: record tallied results to GCS bucket	2023-04-25 17:59:05 +00:00
Andrew FerlitschandGitHub	8cd372c2d1	fix: nan (#1799 ) * fix: nan * fix: syntax error * fix: syntax error * fix: syntax error	2023-04-25 17:31:09 +00:00
Xiang XuandGitHub	447d79a165	add feature to clip (#1800 )	2023-04-24 23:58:06 +00:00
Andrew FerlitschandGitHub	3d461a8458	outdated (#1798 )	2023-04-24 23:54:49 +00:00
Lav Rai GitHub Andrew Ferlitsch	228e2034f7	Add detectron2 notebook (#1783 ) * Add model garden pytorch detectron2 notebook * Add model garden pytorch detectron2 notebook * Add model garden pytorch detectron2 notebook * Add model garden pytorch detectron2 notebook --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2023-04-24 22:03:56 +00:00
Andrew FerlitschandGitHub	fd3cc2a2cc	update: test_percent and dry_run for weekly regr (#1795 ) * update: test_percent for weekly regr * update: add dry_run and test_percent * fix: review comment	2023-04-24 21:41:21 +00:00
Andrew FerlitschandGitHub	76a1f49aaa	improve install (#1797 )	2023-04-24 19:49:46 +00:00
Andrew FerlitschandGitHub	cb41dfc8bd	test: flaky script (#1794 )	2023-04-22 14:53:19 +00:00
Andrew FerlitschandGitHub	9b5e734dca	test: list of passing notebooks (#1785 ) * test: list of passing notebooks * test: list of failing notebooks	2023-04-21 19:17:43 +00:00
Andrew FerlitschandGitHub	274ed4ef49	update: search index for migration (#1793 )	2023-04-21 15:45:13 +00:00
dstnluong-googleandGitHub	92498e8148	Add 5 additional models to list of verified models. (#1791 )	2023-04-21 15:23:50 +00:00
Andrew FerlitschandGitHub	ce48820cc1	fix: links after rename UJ7 (#1789 )	2023-04-21 12:46:03 +00:00
Andrew FerlitschandGitHub	b59e74be8a	fix: links after rename UJ9 (#1780 )	2023-04-21 12:45:28 +00:00
Andrew FerlitschandGitHub	47579bdb10	fix: links after rename UJ10 (#1779 )	2023-04-21 12:44:57 +00:00
Andrew FerlitschandGitHub	761ffc7b17	fix: links after rename UJ2 (#1778 )	2023-04-21 12:44:23 +00:00
Andrew FerlitschandGitHub	99cd99b385	fix: links after rename UJ3 (#1777 )	2023-04-21 12:43:48 +00:00
Andrew FerlitschandGitHub	be783a2ed1	fix: links after rename UJ6 (#1776 )	2023-04-21 12:43:25 +00:00
Andrew FerlitschandGitHub	7523b575ab	fix: links after rename UJ4 (#1775 )	2023-04-21 12:42:39 +00:00
Andrew FerlitschandGitHub	4f5a637cd0	fix: links after rename UJ5 (#1773 )	2023-04-21 12:42:06 +00:00
Andrew FerlitschandGitHub	0701ca01a8	fix: links after rename 1 (#1772 )	2023-04-21 12:41:39 +00:00
Andrew FerlitschandGitHub	78e0a67cd9	fix: links after rename UJ11 (#1781 )	2023-04-20 22:59:08 +00:00
KCFindstrandGitHub	4ef75656a1	Fix typos and invalid properties in TIMM notebook (#1786 )	2023-04-20 22:38:43 +00:00
Xiang XuandGitHub	f5299da9b0	fix notes (#1787 )	2023-04-20 22:37:19 +00:00
Andrew FerlitschandGitHub	b986daa72d	update: searchable index (#1784 )	2023-04-20 20:46:54 +00:00
KCFindstrandGitHub	e987dce2ad	Add Model Garden Pytorch TIMM notebook (#1760 ) * Add model garden pytorch timm notebook * Add CODEOWNERS for model garden timm notebook * Fix lint errors on TIMM notebook * Fix TIMM notebook	2023-04-20 20:02:22 +00:00
Andrew FerlitschandGitHub	f2f98cc8ed	update: next round of index (#1782 )	2023-04-20 19:57:30 +00:00
Xiang XuandGitHub	0661fcc121	fix blip2 (#1771 )	2023-04-20 19:20:47 +00:00
Andrew FerlitschandGitHub	f4c0fc7781	fix: rename UJ1 (#1757 ) * fix: rename UJ1 * fix: lint	2023-04-20 18:22:47 +00:00
Andrew FerlitschandGitHub	8e3db693cb	fix: UJ9 (#1770 )	2023-04-20 18:22:15 +00:00
Andrew FerlitschandGitHub	0349a9552d	fix: UJ8 (#1769 )	2023-04-20 18:21:45 +00:00
Andrew FerlitschandGitHub	ac1f03dd71	fix: rename UJ7 (#1768 )	2023-04-20 18:21:16 +00:00
Andrew FerlitschandGitHub	cb18a37b0a	fix: rename UJ6 (#1767 )	2023-04-20 18:20:51 +00:00
Andrew FerlitschandGitHub	4c8ec05f22	fix: rename UJ5 (#1766 )	2023-04-20 18:20:28 +00:00
Andrew FerlitschandGitHub	c17df33c7c	fix: rename UJ4 (#1765 ) * fix: rename UJ4 * fix: lint	2023-04-20 18:20:03 +00:00
Andrew FerlitschandGitHub	543cd66c16	fix: rename UJ3 (#1764 )	2023-04-20 18:19:36 +00:00
Andrew FerlitschandGitHub	f50dac63eb	fix: rename UJ2 (#1763 )	2023-04-20 18:19:08 +00:00
Andrew FerlitschandGitHub	df979c1c65	fix: rename UJ15 (#1762 )	2023-04-20 18:18:21 +00:00
Andrew FerlitschandGitHub	7b49c23eb6	fix: rename UJ14 (#1761 ) * fix: rename UJ14 * fix: lint	2023-04-20 18:17:51 +00:00
Andrew FerlitschandGitHub	965835122b	fix: rename UJ11 (#1759 )	2023-04-20 18:17:22 +00:00
Andrew FerlitschandGitHub	54a34a7122	fix: rename UJ10 (#1758 )	2023-04-20 18:17:00 +00:00
Xiang XuandGitHub	a701f3b08c	fix controlnet (#1756 )	2023-04-20 04:44:53 +00:00
Connor McCarthy GitHub Andrew Ferlitsch	59a220cb58	fix: pin kfp and gcpc < 2.0 in rapid_prototyping_bqml_automl (#1755 ) * pin kfp in rapid_prototyping_bqml_automl * downgrade component version --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@gmail.com>	2023-04-20 04:36:39 +00:00