fix: rename UJ10

.cloud-build/execute_changed_notebooks_cli.py

@@ -17,7 +17,6 @@
 
 import argparse
 import pathlib
-import os
 
 import execute_changed_notebooks_helper
 
@@ -40,19 +39,6 @@ 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.",
@@ -121,49 +107,24 @@ 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()
 
-changed_notebooks = execute_changed_notebooks_helper.get_changed_notebooks(
+notebooks = execute_changed_notebooks_helper.get_changed_notebooks(
     test_paths_file=args.test_paths_file,
     base_branch=args.base_branch,
 )
 
-
-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
+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,
 )

.cloud-build/execute_changed_notebooks_helper.py

@@ -21,15 +21,11 @@ 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, Dict, Any
+from typing import List, Optional
 from utils import util
 
 import execute_notebook_helper
@@ -69,9 +65,7 @@ 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
@@ -86,40 +80,6 @@ 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,
@@ -231,9 +191,7 @@ 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="",
@@ -243,6 +201,7 @@ 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
@@ -288,10 +247,9 @@ def process_and_execute_notebook(
         # Block and wait for the result
         operation_result = operation.result(timeout=timeout_in_seconds)
 
-        result.duration = datetime.datetime.now() - result.start_time
+        result.duration = datetime.datetime.now() - time_start
         result.is_pass = True
         print(f"{notebook} PASSED in {format_timedelta(result.duration)}.")
-
     except Exception as error:
         result.error_message = str(error)
 
@@ -310,7 +268,7 @@ def process_and_execute_notebook(
             except Exception as error:
                 result.error_message = str(error)
 
-        result.duration = datetime.datetime.now() - result.start_time
+        result.duration = datetime.datetime.now() - time_start
         result.is_pass = False
 
         print(
@@ -378,54 +336,12 @@ 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,
@@ -453,10 +369,6 @@ 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):
@@ -559,7 +471,7 @@ def process_and_execute_notebooks(
             print("=" * 100)
 
             build_id = results_sorted[0].build_id
-            logs_bucket_name = (results_sorted[0].logs_bucket).replace("gs://", "")
+            logs_bucket_name = (results_sorted[0].logs_bucket).removeprefix("gs://")
             log_file_name = f"log-{build_id}.txt"
 
             log_contents = util.download_blob_into_memory(
@@ -577,11 +489,6 @@ 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(

.cloud-build/notebook-execution-test-cloudbuild.yaml

@@ -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` --build_id ${BUILD_ID}
+      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_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`
     env:
     - 'IS_TESTING=1'
 timeout: 86400s

.cloud-build/requirements.txt

@@ -3,15 +3,12 @@ numpy
 jupyter
 nbconvert
 papermill
+pandas
 matplotlib
 tabulate
 google-cloud-aiplatform
 google-cloud-storage
 google-cloud-build
-google-cloud-storage
 ratemate
 GitPython
-tqdm
-fsspec
-pandas
-
+tqdm

.cloud-build/test_failing.txt

@@ -1,40 +0,0 @@
-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

.cloud-build/test_passing.txt

@@ -1,80 +0,0 @@
-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

.cloud-build/tests/flaky.py

@@ -1,46 +0,0 @@
-# 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')

notebooks/community/CODEOWNERS

@@ -56,4 +56,3 @@
 /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

notebooks/community/ml_ops/stage3/get_started_vertex_pipelines_xgboost_with_prediction.ipynb

@@ -196,7 +196,7 @@
         "    USER_FLAG = \"--user\"\n",
         "\n",
         "! pip3 install {USER_FLAG} --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                             google-cloud-pipeline-components==1.0.25 \\\n",
+        "                                             google-cloud-pipeline-components \\\n",
         "                                             kfp "
       ]
     },

notebooks/community/model_garden/model_garden_pipeline_templates_bert.ipynb

@@ -1,852 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "copyright"
-      },
-      "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": "title:generic,gcp"
-      },
-      "source": [
-        "# Get started with Model Garden Pipeline Templates for BERT models\n",
-        "\n",
-        "\n",
-        "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_template_pipelines_bert.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_template_pipelines_bert.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/communitymodel_garden/model_garden_template_pipelines_bert.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": "overview:mlops"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "\n",
-        "This tutorial demonstrates how to modify, compile and execute a prebuilt Vertex AI Model Garden pipeline template with Vertex AI Pipelines.\n",
-        "\n",
-        "Learn more about [Create a pipeline template](https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "objective:mlops,stage4,get_started_vertex_model_evaluation"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to use a prebuilt pipeline template with `Vertex AI Pipelines` to fine-tune a BERT text classification model, where the model is accessed from `Vertex AI Model Garden`.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI Pipelines`\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Model Garden`\n",
-        "- `Google Cloud Pipeline Components`\n",
-        "\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Create a user-defined repository in the `Artifact Registry`.\n",
-        "- Upload the prebuilt pipeline template to the `Artifact Registry`.\n",
-        "- Create a pipeline job with the prebuilt pipeline template to fine-tune a BERT model.\n",
-        "- Execute the pipeline using `Vertex AI Pipelines`.\n",
-        "    - Load BERT model from Vertex AI Model Garden\n",
-        "    - Fine-tune train the model\n",
-        "    - Do batch prediction\n",
-        "    - Evaluate the model from the batch prediction results\n",
-        "- Obtain the Vertex AI Model resource from the pipeline artifacts.\n",
-        "- Deploy the model to a Vertex AI Endpoint\n",
-        "- Make a prediction"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bank,lbn"
-      },
-      "source": [
-        "### Model\n",
-        "\n",
-        "This tutorial uses a pre-trained BERT text classification model from `Vertex AI Model Garden`, which is then fine-tuned (transfer learning) on a dataset of text phrases which are classified as either FirstClass or SecondClass.\n",
-        "\n",
-        "Learn more about [BERT pretrained encoder model]( https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "costs"
-      },
-      "source": [
-        "### Costs\n",
-        "\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "* Vertex AI\n",
-        "* Cloud Storage\n",
-        "* Dataflow\n",
-        "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and [Dataflow pricing](https://cloud.google.com/dataflow/pricing)\n",
-        "and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing this notebook.\n",
-        "\n",
-        "*Note:* This tutorial requires KFP 2.x."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
-        "                         google-cloud-pipeline-components \\\n",
-        "                         kfp==2.0.0b15"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "restart"
-      },
-      "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D-ZBOjErv5mM"
-      },
-      "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": "before_you_begin"
-      },
-      "source": [
-        "## Before you begin\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "before_you_begin:nogpu"
-      },
-      "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": "set_project_id"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c4ccf556d4ea"
-      },
-      "source": [
-        "### Enable APIs\n",
-        "\n",
-        "You can enable the required APIs using `gcloud`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "619529337e6d"
-      },
-      "outputs": [],
-      "source": [
-        "! gcloud services enable compute.googleapis.com         \\\n",
-        "                         containerregistry.googleapis.com  \\\n",
-        "                         aiplatform.googleapis.com  \\\n",
-        "                         artifactregistry.googleapis.com"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "IS_COLAB = False\n",
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()\n",
-        "# IS_COLAB=True"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:mbsdk"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "outputs": [],
-      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "set_service_account"
-      },
-      "source": [
-        "#### Service Account\n",
-        "\n",
-        "**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_service_account"
-      },
-      "outputs": [],
-      "source": [
-        "SERVICE_ACCOUNT = \"[your-service-account]\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_service_account"
-      },
-      "outputs": [],
-      "source": [
-        "if (\n",
-        "    SERVICE_ACCOUNT == \"\"\n",
-        "    or SERVICE_ACCOUNT is None\n",
-        "    or SERVICE_ACCOUNT == \"[your-service-account]\"\n",
-        "):\n",
-        "    # Get your service account from gcloud\n",
-        "    if not IS_COLAB:\n",
-        "        shell_output = !gcloud auth list 2>/dev/null\n",
-        "        SERVICE_ACCOUNT = shell_output[2].replace(\"*\", \"\").strip()\n",
-        "\n",
-        "    if IS_COLAB:\n",
-        "        shell_output = ! gcloud projects describe  $PROJECT_ID\n",
-        "        project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
-        "        SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
-        "\n",
-        "    print(\"Service Account:\", SERVICE_ACCOUNT)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "set_service_account:pipelines"
-      },
-      "source": [
-        "#### Set service account access for Vertex AI Pipelines\n",
-        "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_service_account:pipelines"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator  \n",
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_vars"
-      },
-      "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
-        "### Import libraries and define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_kfp"
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "import google.cloud.aiplatform as aiplatform\n",
-        "from kfp.registry import RegistryClient"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9b773e8d2bd2"
-      },
-      "source": [
-        "## Create repo in Artifact Registry\n",
-        "\n",
-        "First, you create your own (user-defined) repository in the `Artifact Registry`. You use this repository to upload and retrieve your pipeline templates.\n",
-        "\n",
-        "The name of your repo is `quickstart-kfp-repo`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "520de849cee2"
-      },
-      "outputs": [],
-      "source": [
-        "REPO_NAME = \"quickstart-kfp-repo\"\n",
-        "\n",
-        "! gcloud artifacts repositories create {REPO_NAME} --location={REGION} --repository-format=KFP"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1611d3517c0f"
-      },
-      "source": [
-        "### Upload the pipeline template\n",
-        "\n",
-        "Next, you instantiate a client interface to the Artifact Registry. Then with the `upload_pipeline()` method you upload your pipeline template."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "72db37f6d67c"
-      },
-      "outputs": [],
-      "source": [
-        "BERT_YAML = \"gs://cloud-samples-data/vertex-ai/dataset-management/datasets/bert_finetuning/pipeline.yaml\"\n",
-        "\n",
-        "! gsutil cp {BERT_YAML} pipeline.yaml\n",
-        "\n",
-        "client = RegistryClient(\n",
-        "    host=f\"https://{REGION}-kfp.pkg.dev/{PROJECT_ID}/quickstart-kfp-repo\"\n",
-        ")\n",
-        "\n",
-        "templateName, versionName = client.upload_pipeline(\n",
-        "    file_name=\"pipeline.yaml\",\n",
-        "    tags=[\"v1\", \"latest\"],\n",
-        "    extra_headers={\n",
-        "        \"description\": \"This is a pipeline template for fine-tuning a BERT model.\"\n",
-        "    },\n",
-        ")\n",
-        "\n",
-        "! rm pipeline.yaml"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "02f82754fc0d"
-      },
-      "source": [
-        "### View your artifacts in your registry\n",
-        "\n",
-        "Next, using the `gcloud artifacts files` command you view the artifacts, inclusive of the pipeline template, in your artifacts repository."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b2f641eb2056"
-      },
-      "outputs": [],
-      "source": [
-        "! gcloud artifacts files list  --repository={REPO_NAME} --location={REGION}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9d5296831cfb"
-      },
-      "source": [
-        "## Load and execute the pipeline job\n",
-        "\n",
-        "Next, you create a Vertex AI Pipeline job from your BERT pipeline template by instantiating a PipelineJob(), with the following parameters:\n",
-        "\n",
-        "- `display_name`: The human readable name for the pipeline job.\n",
-        "- `template_path`: The path to the pipeline template in the Artifact Registry.\n",
-        "- `enable_caching`: On re-runs, use the results from previous successful and unchanged steps.\n",
-        "- `pipeline_root`: A Cloud storage location for storing pipeline results.\n",
-        "- `parameter_values`: The parameters and values that are input to the template pipeline. In this example, they are:\n",
-        "    - `project`: Your project ID.\n",
-        "    - `class_labels`: A list of valid class labels, in cardinal order.\n",
-        "    - `root_dir`: A Cloud Storage scratch area.\n",
-        "    - `training_data_path`: A Cloud Storage location to the training data.\n",
-        "    - `ground_truth_gcs_source_uris`: A Cloud Storage location to evaluation data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a3c502fc7e41"
-      },
-      "outputs": [],
-      "source": [
-        "PIPELINE_ROOT = f\"{BUCKET_URI}/pipeline_root/bert-finetuning\"\n",
-        "\n",
-        "job = aiplatform.PipelineJob(\n",
-        "    display_name=\"bert-finetuning\",\n",
-        "    template_path=f\"https://{REGION}-kfp.pkg.dev/{PROJECT_ID}/quickstart-kfp-repo/{templateName}/{versionName}\",\n",
-        "    pipeline_root=PIPELINE_ROOT,\n",
-        "    enable_caching=False,\n",
-        "    parameter_values={\n",
-        "        \"project\": PROJECT_ID,\n",
-        "        \"class_labels\": [\"FirstClass\", \"SecondClass\", \"[UNK]\"],\n",
-        "        \"root_dir\": BUCKET_URI,\n",
-        "        \"ground_truth_gcs_source_uris\": [\n",
-        "            \"gs://cloud-samples-data/vertex-ai/dataset-management/datasets/bert_finetuning/wide_and_deep_trainer_container_tests_input.jsonl\"\n",
-        "        ],\n",
-        "        \"training_data_path\": \"gs://cloud-samples-data/vertex-ai/dataset-management/datasets/bert_finetuning/wide_and_deep_trainer_container_tests_input.jsonl\",\n",
-        "    },\n",
-        ")\n",
-        "\n",
-        "job.run()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view_pipleline_results:bqml"
-      },
-      "source": [
-        "### View the pipeline results"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "view_pipleline_results:bqml"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_NUMBER = job.gca_resource.name.split(\"/\")[1]\n",
-        "print(PROJECT_NUMBER)\n",
-        "\n",
-        "\n",
-        "def print_pipeline_output(job, output_task_name):\n",
-        "    JOB_ID = job.name\n",
-        "    print(JOB_ID)\n",
-        "    artifact = \"\"\n",
-        "    for _ in range(len(job.gca_resource.job_detail.task_details)):\n",
-        "        TASK_ID = job.gca_resource.job_detail.task_details[_].task_id\n",
-        "        EXECUTE_OUTPUT = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/executor_output.json\"\n",
-        "        )\n",
-        "        GCP_RESOURCES = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/gcp_resources\"\n",
-        "        )\n",
-        "        EVALUATION_METRICS = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/evaluation_metrics\"\n",
-        "        )\n",
-        "        # Check if file exists, 0 is success\n",
-        "        !gsutil -q stat $EXECUTE_OUTPUT\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $EXECUTE_OUTPUT\n",
-        "            artifact = EXECUTE_OUTPUT\n",
-        "            break\n",
-        "        !gsutil -q stat $GCP_RESOURCES\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $GCP_RESOURCES\n",
-        "            artifact = GCP_RESOURCES\n",
-        "            break\n",
-        "        !gsutil -q stat $EVALUATION_METRICS\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $EVALUATION_METRICS\n",
-        "            artifact = EVALUATION_METRICS\n",
-        "            break\n",
-        "\n",
-        "    return artifact\n",
-        "\n",
-        "\n",
-        "print(\"get-vertex-model\")\n",
-        "artifacts = print_pipeline_output(job, \"get-vertex-model\")\n",
-        "output = !gsutil cat $artifacts\n",
-        "print(output)\n",
-        "output = json.loads(output[0])\n",
-        "model_id = output[\"artifacts\"][\"model\"][\"artifacts\"][0][\"metadata\"][\"resourceName\"]\n",
-        "print(\"\\n\\n\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f431a9e6f025"
-      },
-      "source": [
-        "### Delete the pipeline job\n",
-        "\n",
-        "The method 'delete()' will delete the pipeline job."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "00bf554abbc6"
-      },
-      "outputs": [],
-      "source": [
-        "job.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3d183db57ae2"
-      },
-      "source": [
-        "### Deploy the model\n",
-        "\n",
-        "Next, you deploy the model to an endpoint:\n",
-        "\n",
-        "- Use the `model_id` obtained from the pipeline artifacts to instaniate a Vertex AI Model resource instance.\n",
-        "- Deploy the Vertex AI Model resource to a Vertex AI Endpoint resource.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "591ccc049ce5"
-      },
-      "outputs": [],
-      "source": [
-        "model = aiplatform.Model(model_id)\n",
-        "endpoint = model.deploy(\n",
-        "    accelerator_count=1,\n",
-        "    accelerator_type=aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4.name,\n",
-        "    machine_type=\"n1-standard-4\",\n",
-        ")\n",
-        "print(endpoint)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "edb781a92864"
-      },
-      "source": [
-        "### Make a prediction\n",
-        "\n",
-        "Finally, you make a prediction with the deployed model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "72d94012a987"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint.predict([\"this is a test\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cleanup:mbsdk"
-      },
-      "source": [
-        "# Cleaning up\n",
-        "\n",
-        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
-        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
-        "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cleanup:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "delete_bucket = False\n",
-        "\n",
-        "endpoint.undeploy_all()\n",
-        "endpoint.delete()\n",
-        "model.delete()\n",
-        "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI\n",
-        "\n",
-        "! rm -rf custom custom.tar.gz"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "model_garden_pipeline_templates_bert.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

notebooks/community/model_garden/model_garden_pipeline_templates_t5x.ipynb

@@ -1,850 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "copyright"
-      },
-      "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": "title:generic,gcp"
-      },
-      "source": [
-        "# Get started with Model Garden Pipeline Templates for T5X models\n",
-        "\n",
-        "\n",
-        "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_template_pipelines_t5x.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_template_pipelines_t5x.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/communitymodel_garden/model_garden_template_pipelines_t5x.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": "overview:mlops"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "\n",
-        "This tutorial demonstrates how to modify, compile and execute a prebuilt Vertex AI Model Garden pipeline template with Vertex AI Pipelines.\n",
-        "\n",
-        "Learn more about [Create a pipeline template](https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "objective:mlops,stage4,get_started_vertex_model_evaluation"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to use a prebuilt pipeline template with `Vertex AI Pipelines` to fine-tune a T5X text classification model, where the model is accessed from `Vertex AI Model Garden`.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI Pipelines`\n",
-        "- `Vertex AI Training`\n",
-        "- `Vertex AI Model Garden`\n",
-        "- `Google Cloud Pipeline Components`\n",
-        "\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Create a user-defined repository in the `Artifact Registry`.\n",
-        "- Upload the prebuilt pipeline template to the `Artifact Registry`.\n",
-        "- Create a pipeline job with the prebuilt pipeline template to fine-tune a T5X model.\n",
-        "- Execute the pipeline using `Vertex AI Pipelines`.\n",
-        "    - Load T5X model from Vertex AI Model Garden\n",
-        "    - Fine-tune train the model\n",
-        "- Obtain the Vertex AI Model resource from the pipeline artifacts.\n",
-        "- Deploy the model to a Vertex AI Endpoint\n",
-        "- Make a prediction"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bank,lbn"
-      },
-      "source": [
-        "### Model\n",
-        "\n",
-        "This tutorial uses a pre-trained T5 text classification model from `Vertex AI Model Garden`, which is then fine-tuned (transfer learning) on a dataset of text phrases which are classified as either FirstClass or SecondClass.\n",
-        "\n",
-        "Learn more about [Text-to-text transfer transformer](https://github.com/google-research/text-to-text-transfer-transformer). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "costs"
-      },
-      "source": [
-        "### Costs\n",
-        "\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "* Vertex AI\n",
-        "* Cloud Storage\n",
-        "* Dataflow\n",
-        "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and [Dataflow pricing](https://cloud.google.com/dataflow/pricing)\n",
-        "and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing this notebook.\n",
-        "\n",
-        "*Note:* This tutorial requires KFP 2.x."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
-        "                         google-cloud-pipeline-components \\\n",
-        "                         kfp==2.0.0b15"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "restart"
-      },
-      "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D-ZBOjErv5mM"
-      },
-      "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": "before_you_begin"
-      },
-      "source": [
-        "## Before you begin\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "before_you_begin:nogpu"
-      },
-      "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": "set_project_id"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
-        "\n",
-        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c4ccf556d4ea"
-      },
-      "source": [
-        "### Enable APIs\n",
-        "\n",
-        "You can enable the required APIs using `gcloud`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "619529337e6d"
-      },
-      "outputs": [],
-      "source": [
-        "! gcloud services enable compute.googleapis.com         \\\n",
-        "                         containerregistry.googleapis.com  \\\n",
-        "                         aiplatform.googleapis.com  \\\n",
-        "                         artifactregistry.googleapis.com"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gcp_authenticate"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FvQeFm3Gv5mR"
-      },
-      "source": [
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ce6043da7b33"
-      },
-      "outputs": [],
-      "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "IS_COLAB = False\n",
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()\n",
-        "# IS_COLAB=True"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:mbsdk"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "outputs": [],
-      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "set_service_account"
-      },
-      "source": [
-        "#### Service Account\n",
-        "\n",
-        "**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_service_account"
-      },
-      "outputs": [],
-      "source": [
-        "SERVICE_ACCOUNT = \"[your-service-account]\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_service_account"
-      },
-      "outputs": [],
-      "source": [
-        "if (\n",
-        "    SERVICE_ACCOUNT == \"\"\n",
-        "    or SERVICE_ACCOUNT is None\n",
-        "    or SERVICE_ACCOUNT == \"[your-service-account]\"\n",
-        "):\n",
-        "    # Get your service account from gcloud\n",
-        "    if not IS_COLAB:\n",
-        "        shell_output = !gcloud auth list 2>/dev/null\n",
-        "        SERVICE_ACCOUNT = shell_output[2].replace(\"*\", \"\").strip()\n",
-        "\n",
-        "    if IS_COLAB:\n",
-        "        shell_output = ! gcloud projects describe  $PROJECT_ID\n",
-        "        project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
-        "        SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
-        "\n",
-        "    print(\"Service Account:\", SERVICE_ACCOUNT)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "set_service_account:pipelines"
-      },
-      "source": [
-        "#### Set service account access for Vertex AI Pipelines\n",
-        "\n",
-        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_service_account:pipelines"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator  \n",
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_vars"
-      },
-      "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
-        "### Import libraries and define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_kfp"
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "import google.cloud.aiplatform as aiplatform\n",
-        "from kfp.registry import RegistryClient"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk,all"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9b773e8d2bd2"
-      },
-      "source": [
-        "## Create repo in Artifact Registry\n",
-        "\n",
-        "First, you create your own (user-defined) repository in the `Artifact Registry`. You use this repository to upload and retreive your pipeline templates.\n",
-        "\n",
-        "The name of your repo is `quickstart-kfp-repo`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "520de849cee2"
-      },
-      "outputs": [],
-      "source": [
-        "REPO_NAME = \"quickstart-kfp-repo\"\n",
-        "\n",
-        "! gcloud artifacts repositories create {REPO_NAME} --location={REGION} --repository-format=KFP"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1611d3517c0f"
-      },
-      "source": [
-        "### Upload the pipeline template\n",
-        "\n",
-        "Next, you instantiate a client interface to the Artifact Registry. Then with the `upload_pipeline()` method you upload your pipeline template."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7f002e57998a"
-      },
-      "outputs": [],
-      "source": [
-        "T5X_YAML = \"gs://cloud-samples-data/vertex-ai/dataset-management/datasets/t5_finetuning/pipeline.yaml\"\n",
-        "\n",
-        "! gsutil cp {T5X_YAML} pipeline.yaml\n",
-        "\n",
-        "client = RegistryClient(\n",
-        "    host=f\"https://{REGION}-kfp.pkg.dev/{PROJECT_ID}/quickstart-kfp-repo\"\n",
-        ")\n",
-        "\n",
-        "templateName, versionName = client.upload_pipeline(\n",
-        "    file_name=\"pipeline.yaml\",\n",
-        "    tags=[\"v1\", \"latest\"],\n",
-        "    extra_headers={\n",
-        "        \"description\": \"This is a pipeline template for fine-tuning a T5 model.\"\n",
-        "    },\n",
-        ")\n",
-        "\n",
-        "! rm pipeline.yaml"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "02f82754fc0d"
-      },
-      "source": [
-        "### View your artifacts in your registry\n",
-        "\n",
-        "Next, using the `gcloud artifacts files` command you view the artifacts, inclusive of the pipeline template, in your artifacts repository."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b2f641eb2056"
-      },
-      "outputs": [],
-      "source": [
-        "! gcloud artifacts files list  --repository={REPO_NAME} --location={REGION}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "968a46a3cb6d"
-      },
-      "source": [
-        "## Load and execute the pipeline job\n",
-        "\n",
-        "Next, you create a Vertex AI Pipeline job from your T5 pipeline template by instantiating a PipelineJob(), with the following parameters:\n",
-        "\n",
-        "- `display_name`: The human readable name for the pipeline job.\n",
-        "- `template_path`: The path to the pipeline template in the Artifact Registry.\n",
-        "- `enable_caching`: On re-runs, use the results from previous successful and unchanged steps.\n",
-        "- `pipeline_root`: A Cloud storage location for storing pipeline results.\n",
-        "- `parameter_values`: The parameters and values that are input to the template pipeline. In this example, they are:\n",
-        "TODO\n",
-        "    - `project`: Your project ID.\n",
-        "    - `class_labels`: A list of valid class labels, in cardinal order.\n",
-        "    - `root_dir`: A Cloud Storage scratch area.\n",
-        "    - `training_data_path`: A Cloud Storage location to the training data.\n",
-        "    - `ground_truth_gcs_source_uris`: A Cloud Storage location to evaluation data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a3c502fc7e41"
-      },
-      "outputs": [],
-      "source": [
-        "PIPELINE_ROOT = f\"{BUCKET_URI}/pipeline_root/t5_finetuning\"\n",
-        "\n",
-        "job = aiplatform.PipelineJob(\n",
-        "    display_name=\"t5x-finetuning\",\n",
-        "    template_path=f\"https://{REGION}-kfp.pkg.dev/{PROJECT_ID}/quickstart-kfp-repo/{templateName}/{versionName}\",\n",
-        "    pipeline_root=PIPELINE_ROOT,\n",
-        "    enable_caching=False,\n",
-        "    parameter_values={\n",
-        "        \"project_id\": PROJECT_ID,\n",
-        "        \"accelerator_count\": 32,\n",
-        "        \"feature_keys\": \"question\",\n",
-        "        \"label_key\": \"answer\",\n",
-        "        \"training_data_path\": \"gs://cloud-llm-public/tfds/natural_questions_open/1.0.0_shortened/natural_questions_open-train.tfrecord-00000-of-00001\",\n",
-        "        \"validation_data_path\": \"gs://cloud-llm-public/tfds/natural_questions_open/1.0.0_shortened/natural_questions_open-validation.tfrecord-00000-of-00001\",\n",
-        "    },\n",
-        ")\n",
-        "\n",
-        "job.run()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view_pipleline_results:bqml"
-      },
-      "source": [
-        "### View the pipeline results"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "view_pipleline_results:bqml"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_NUMBER = job.gca_resource.name.split(\"/\")[1]\n",
-        "print(PROJECT_NUMBER)\n",
-        "\n",
-        "\n",
-        "def print_pipeline_output(job, output_task_name):\n",
-        "    JOB_ID = job.name\n",
-        "    print(JOB_ID)\n",
-        "    artifact = \"\"\n",
-        "    for _ in range(len(job.gca_resource.job_detail.task_details)):\n",
-        "        TASK_ID = job.gca_resource.job_detail.task_details[_].task_id\n",
-        "        EXECUTE_OUTPUT = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/executor_output.json\"\n",
-        "        )\n",
-        "        GCP_RESOURCES = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/gcp_resources\"\n",
-        "        )\n",
-        "        EVALUATION_METRICS = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/evaluation_metrics\"\n",
-        "        )\n",
-        "        # Check if file exists, 0 is success\n",
-        "        !gsutil -q stat $EXECUTE_OUTPUT\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $EXECUTE_OUTPUT\n",
-        "            artifact = EXECUTE_OUTPUT\n",
-        "            break\n",
-        "        !gsutil -q stat $GCP_RESOURCES\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $GCP_RESOURCES\n",
-        "            artifact = GCP_RESOURCES\n",
-        "            break\n",
-        "        !gsutil -q stat $EVALUATION_METRICS\n",
-        "        if _exit_code == 0:\n",
-        "            ! gsutil cat $EVALUATION_METRICS\n",
-        "            artifact = EVALUATION_METRICS\n",
-        "            break\n",
-        "\n",
-        "    return artifact\n",
-        "\n",
-        "\n",
-        "print(\"model-upload\")\n",
-        "artifacts = print_pipeline_output(job, \"model-upload\")\n",
-        "output = !gsutil cat $artifacts\n",
-        "print(output)\n",
-        "output = json.loads(output[0])\n",
-        "model_id = output[\"artifacts\"][\"model\"][\"artifacts\"][0][\"metadata\"][\"resourceName\"]\n",
-        "print(\"\\n\\n\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f431a9e6f025"
-      },
-      "source": [
-        "### Delete the pipeline job\n",
-        "\n",
-        "The method 'delete()' will delete the pipeline job."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "00bf554abbc6"
-      },
-      "outputs": [],
-      "source": [
-        "job.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3d183db57ae2"
-      },
-      "source": [
-        "### Deploy the model\n",
-        "\n",
-        "Next, you deploy the model to an endpoint:\n",
-        "\n",
-        "- Use the `model_id` obtained from the pipeline artifacts to instantiate a Vertex AI Model resource instance.\n",
-        "- Deploy the Vertex AI Model resource to a Vertex AI Endpoint resource.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "591ccc049ce5"
-      },
-      "outputs": [],
-      "source": [
-        "model = aiplatform.Model(model_id)\n",
-        "endpoint = model.deploy(\n",
-        "    accelerator_count=1,\n",
-        "    accelerator_type=aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_T4.name,\n",
-        "    machine_type=\"n1-standard-4\",\n",
-        ")\n",
-        "print(endpoint)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "edb781a92864"
-      },
-      "source": [
-        "### Make a prediction\n",
-        "\n",
-        "Finally, you make a prediction with the deployed model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "72d94012a987"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint.predict([\"this is a test\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cleanup:mbsdk"
-      },
-      "source": [
-        "# Cleaning up\n",
-        "\n",
-        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
-        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
-        "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cleanup:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "delete_bucket = True\n",
-        "\n",
-        "endpoint.undeploy_all()\n",
-        "endpoint.delete()\n",
-        "model.delete()\n",
-        "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI\n",
-        "\n",
-        "! rm -rf custom custom.tar.gz"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "model_garden_pipeline_templates_t5x.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb

@@ -271,7 +271,7 @@
         "\n",
         "\n",
         "def deploy_model(model_id, task):\n",
-        "    model_name = \"blip2\"\n",
+        "    model_name = \"blip-image-captioning\"\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": "80b3fd2ace09"
+        "id": "12893aa2c5af"
       },
       "source": [
-        "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."
+        "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."
       ]
     },
     {
@@ -409,10 +409,10 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "80b3fd2ace09"
+        "id": "0ac7f8d945e3"
       },
       "source": [
-        "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."
+        "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."
       ]
     },
     {

notebooks/community/model_garden/model_garden_pytorch_blip_image_captioning.ipynb

@@ -332,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_blip_vqa.ipynb

@@ -332,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_clip.ipynb

@@ -314,16 +314,9 @@
       "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."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "99f3c6b404b5"
-      },
-      "source": [
-        "### Zero-shot image classification"
+        "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."
       ]
     },
     {
@@ -339,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,
@@ -369,6 +353,15 @@
         "print(preds)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "db7ffebdb4be"
+      },
+      "source": [
+        "### Clean up resources"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -383,82 +376,6 @@
         "# Delete models.\n",
         "model.delete()"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ddf9e9ec7b58"
-      },
-      "source": [
-        "### Image/text feature embedding"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "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": [
-        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)\n",
-        "\n",
-        "# Delete models.\n",
-        "model.delete()"
-      ]
     }
   ],
   "metadata": {

notebooks/community/model_garden/model_garden_pytorch_controlnet.ipynb

@@ -184,9 +184,7 @@
         "\n",
         "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
         "\n",
-        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs.\n",
-        "\n",
-        "1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console) with `Vertex AI User` and `Storage Object Admin` roles for deploying fine tuned model to Vertex AI endpoint."
+        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs."
       ]
     },
     {
@@ -213,10 +211,7 @@
         "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
-        "GCS_BUCKET = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# The service account you created in step-5 above, it's like \"<account_name>@<project>.iam.gserviceaccount.com\"\n",
-        "SERVICE_ACCOUNT = \"\"  # @param {type:\"string\"}"
+        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -276,15 +271,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "354da31189dc"
       },
       "outputs": [],
       "source": [
         "import base64\n",
-        "import os\n",
-        "from datetime import datetime\n",
         "from io import BytesIO\n",
         "\n",
         "import cv2\n",
@@ -341,6 +334,8 @@
         "        \"MODEL_ID\": model_id,\n",
         "        \"TASK\": task,\n",
         "    }\n",
+        "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
+        "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
@@ -348,6 +343,7 @@
         "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
         "        serving_container_health_route=\"/ping\",\n",
         "        serving_container_environment_variables=serving_env,\n",
+        "        artifact_uri=artifact_uri,\n",
         "    )\n",
         "    model.deploy(\n",
         "        endpoint=endpoint,\n",
@@ -355,7 +351,6 @@
         "        accelerator_type=\"NVIDIA_TESLA_V100\",\n",
         "        accelerator_count=1,\n",
         "        deploy_request_timeout=1800,\n",
-        "        service_account=SERVICE_ACCOUNT,\n",
         "    )\n",
         "    return model, endpoint"
       ]
@@ -415,13 +410,6 @@
         "# 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",
@@ -430,8 +418,6 @@
         "        \"--resolution=512\",\n",
         "        \"--learning_rate=1e-5\",\n",
         "        \"--train_batch_size=2\",\n",
-        "        \"--checkpointing_steps=50000\",\n",
-        "        \"--checkpoints_total_limit=1\",\n",
         "    ],\n",
         "    replica_count=num_nodes,\n",
         "    machine_type=machine_type,\n",
@@ -491,15 +477,6 @@
         ")"
       ]
     },
-    {
-      "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,
@@ -543,11 +520,11 @@
       },
       "outputs": [],
       "source": [
-        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)\n",
-        "\n",
         "# Delete models.\n",
-        "model.delete()"
+        "model.delete()\n",
+        "\n",
+        "# Undeploy model and delete endpoint.\n",
+        "endpoint.delete(force=True)"
       ]
     },
     {
@@ -577,19 +554,10 @@
       "outputs": [],
       "source": [
         "model, endpoint = deploy_model(\n",
-        "    model_id=f\"gs://{GCS_BUCKET}/controlnet/output\", task=\"controlnet\"\n",
+        "    model_id=f\"gs://{GCS_BUCKET}/controlnet/output\", task=\"image-to-image\"\n",
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.ipynb

@@ -352,15 +352,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.ipynb

@@ -332,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_owlvit.ipynb

@@ -351,15 +351,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.ipynb

@@ -24,7 +24,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "99c1c3fc2ca5"
@@ -49,13 +48,12 @@
         "      <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 GPU notebook with preinstalled HuggingFace/transformer libraries is recommended)\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
         "  </td>\n",
         "</table>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "3de7470326a2"
@@ -63,15 +61,13 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates running local inference on [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench).\n",
-        "This notebook also demonstrates finetuning [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex AI for online prediction.\n",
+        "This notebook demonstrates finetuning [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex AI for online prediction.\n",
         "\n",
         "### Objective\n",
         "\n",
-        "- Run local predictions for text-to-image and text-guided-image-to-image with serving dockers.\n",
         "- Finetune the stable-diffusion-v1.5 model with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth).\n",
-        "- Upload the model to [Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
-        "- Deploy the model to a [Vertex AI Endpoint resource](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
+        "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
+        "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
         "- Run online predictions for text-to-image and text-guided-image-to-image.\n",
         "\n",
         "### Costs\n",
@@ -85,114 +81,6 @@
       ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e8a42fa49305"
-      },
-      "source": [
-        "## Local inference (Workbench only)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1169c41b76b3"
-      },
-      "source": [
-        "The quickest and easiest way to use this model locally is by using Vertex AI Workbench with a pre-built custom container that has the necessary packages installed.\n",
-        "\n",
-        "\n",
-        "\n",
-        "1.   Follow [this link](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_stable_diffusion.ipynb) to deploy the notebook to a Vertex AI Workbench Instance.\n",
-        "2.   Select `Create a new Notebook`.\n",
-        "3.   Click `Advanced Options`.\n",
-        "4.   Under **Environment**, select `Custom Container` for `Environment`. \n",
-        "5.   Set `Docker container image` to `us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/transformers-notebook`.\n",
-        "6.   Under **Machine configuration**, select a GPU and select `Install NVIDIA GPU driver automatically for me`.\n",
-        "7.   Click `Create` to create the Vertex AI Workbench instance. \n",
-        "\n",
-        "Once the notebook is ready, simply execute the code block(s) below in the Workbench instance."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1d5ebc91c786"
-      },
-      "source": [
-        "### Text-to-image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d39ed8c97cc5"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "from diffusers import StableDiffusionPipeline\n",
-        "\n",
-        "model_id = \"runwayml/stable-diffusion-v1-5\"\n",
-        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
-        "pipe = pipe.to(\"cuda\")\n",
-        "\n",
-        "prompt = \"a photo of an astronaut riding a horse on mars\"\n",
-        "image = pipe(prompt).images[0]\n",
-        "\n",
-        "display(image)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5aed5ed7b6f6"
-      },
-      "source": [
-        "### Text-guided image-to-image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "faadabb7728f"
-      },
-      "outputs": [],
-      "source": [
-        "from io import BytesIO\n",
-        "\n",
-        "import requests\n",
-        "import torch\n",
-        "from diffusers import StableDiffusionImg2ImgPipeline\n",
-        "from PIL import Image\n",
-        "\n",
-        "device = \"cuda\"\n",
-        "model_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n",
-        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
-        "    model_id_or_path, torch_dtype=torch.float16\n",
-        ")\n",
-        "pipe = pipe.to(device)\n",
-        "\n",
-        "url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n",
-        "\n",
-        "response = requests.get(url)\n",
-        "init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
-        "init_image = init_image.resize((768, 512))\n",
-        "\n",
-        "prompt = \"A fantasy landscape, trending on artstation\"\n",
-        "\n",
-        "images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n",
-        "display(images[0])"
-      ]
-    },
-    {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "264c07757582"
@@ -204,7 +92,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "d73ffa0c0b83"
@@ -238,7 +125,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fb671e75ca7b"
@@ -256,11 +142,12 @@
       "outputs": [],
       "source": [
         "# Install gdown for downloading example training images.\n",
-        "!pip install gdown"
+        "!pip install gdown\n",
+        "# Install gsutil for downloading/uploading data from/to Cloud Storage buckets.\n",
+        "!pip install gsutil"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "5244aac3d929"
@@ -284,7 +171,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "bb7adab99e41"
@@ -298,13 +184,10 @@
         "\n",
         "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
         "\n",
-        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs.\n",
-        "\n",
-        "1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console) with `Vertex AI User` and `Storage Object Admin` roles for deploying fine tuned model to Vertex AI endpoint."
+        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs."
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6c460088b873"
@@ -328,14 +211,10 @@
         "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
         "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
-        "GCS_BUCKET = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# The service account for deploying fine tuned model.\n",
-        "SERVICE_ACCOUNT = \"\"  # @param {type:\"string\"}"
+        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "e828eb320337"
@@ -358,7 +237,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "2cc825514deb"
@@ -383,7 +261,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "0c250872074f"
@@ -401,13 +278,12 @@
       "outputs": [],
       "source": [
         "import base64\n",
-        "import glob\n",
         "import os\n",
         "from datetime import datetime\n",
         "from io import BytesIO\n",
         "\n",
         "import requests\n",
-        "from google.cloud import aiplatform, storage\n",
+        "from google.cloud import aiplatform\n",
         "from PIL import Image\n",
         "\n",
         "\n",
@@ -450,6 +326,8 @@
         "        \"MODEL_ID\": model_id,\n",
         "        \"TASK\": task,\n",
         "    }\n",
+        "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
+        "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
@@ -457,6 +335,7 @@
         "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
         "        serving_container_health_route=\"/ping\",\n",
         "        serving_container_environment_variables=serving_env,\n",
+        "        artifact_uri=artifact_uri,\n",
         "    )\n",
         "    model.deploy(\n",
         "        endpoint=endpoint,\n",
@@ -464,35 +343,11 @@
         "        accelerator_type=\"NVIDIA_TESLA_V100\",\n",
         "        accelerator_count=1,\n",
         "        deploy_request_timeout=1800,\n",
-        "        service_account=SERVICE_ACCOUNT,\n",
         "    )\n",
-        "    return model, endpoint\n",
-        "\n",
-        "\n",
-        "def get_bucket_and_blob_name(filepath):\n",
-        "    # The gcs path is of the form gs://<bucket-name>/<blob-name>\n",
-        "    gs_suffix = filepath.split(\"gs://\", 1)[1]\n",
-        "    return tuple(gs_suffix.split(\"/\", 1))\n",
-        "\n",
-        "\n",
-        "def upload_local_dir_to_gcs(local_dir_path, gcs_dir_path):\n",
-        "    \"\"\"Uploads files in a local directory to a GCS directory.\"\"\"\n",
-        "    client = storage.Client()\n",
-        "    bucket_name = gcs_dir_path.split(\"/\")[2]\n",
-        "    bucket = client.get_bucket(bucket_name)\n",
-        "    for local_file in glob.glob(local_dir_path + \"/**\"):\n",
-        "        if not os.path.isfile(local_file):\n",
-        "            continue\n",
-        "        filename = local_file[1 + len(local_dir_path) :]\n",
-        "        gcs_file_path = os.path.join(gcs_dir_path, filename)\n",
-        "        _, blob_name = get_bucket_and_blob_name(gcs_file_path)\n",
-        "        blob = bucket.blob(blob_name)\n",
-        "        blob.upload_from_filename(local_file)\n",
-        "        print(\"Copied {} to {}.\".format(local_file, gcs_file_path))"
+        "    return model, endpoint"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "e70e3519ff8b"
@@ -502,7 +357,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "0dc65d8f0689"
@@ -527,12 +381,11 @@
         "!gdown --folder https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ\n",
         "\n",
         "# Upload data to Cloud Storage bucket.\n",
-        "upload_local_dir_to_gcs(\"dog\", f\"gs://{GCS_BUCKET}/dreambooth/dog\")\n",
-        "upload_local_dir_to_gcs(\"dog\", f\"gs://{GCS_BUCKET}/dreambooth/dog_class\")"
+        "!gsutil -m cp -r dog/* gs://{GCS_BUCKET}/dreambooth/dog/\n",
+        "!gsutil -m cp -r dog/* gs://{GCS_BUCKET}/dreambooth/dog_class/"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "969cfeb79317"
@@ -602,7 +455,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "bf7f82732e61"
@@ -612,7 +464,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1cc26e68d7b0"
@@ -624,7 +475,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "cd7b56421392"
@@ -634,7 +484,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6d331b1ea337"
@@ -655,22 +504,12 @@
       },
       "outputs": [],
       "source": [
-        "# Set the model_id to \"runwayml/stable-diffusion-v1-5\" to load the OSS pre-trained model.\n",
+        "# Set the model_id to a GCS path, like \"gs://GCS_BUCKET/dreambooth/output\", to load the dreambooth finetuned model above.\n",
         "model, endpoint = deploy_model(\n",
-        "    model_id=f\"gs://{GCS_BUCKET}/dreambooth/output\", task=\"text-to-image\"\n",
+        "    model_id=\"runwayml/stable-diffusion-v1-5\", task=\"text-to-image\"\n",
         ")"
       ]
     },
-    {
-      "attachments": {},
-      "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,
@@ -691,7 +530,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "af21a3cff1e0"
@@ -716,7 +554,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "c1e51f764a60"
@@ -726,7 +563,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fa686a54047c"
@@ -749,16 +585,6 @@
         ")"
       ]
     },
-    {
-      "attachments": {},
-      "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,
@@ -783,7 +609,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ed3795d474b9"

notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.ipynb

@@ -142,7 +142,9 @@
       "outputs": [],
       "source": [
         "# Install gdown for downloading example training images.\n",
-        "!pip install gdown"
+        "!pip install gdown\n",
+        "# Install gsutil for downloading/uploading data from/to Cloud Storage buckets.\n",
+        "!pip install gsutil"
       ]
     },
     {
@@ -182,9 +184,7 @@
         "\n",
         "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
         "\n",
-        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs.\n",
-        "\n",
-        "1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console) with `Vertex AI User` and `Storage Object Admin` roles for deploying fine tuned model to Vertex AI endpoint."
+        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs."
       ]
     },
     {
@@ -210,14 +210,8 @@
         "# The region you want to launch jobs in.\n",
         "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
         "\n",
-        "# The Cloud Storage bucket for storing experiments output.\n",
-        "# Fill it without the 'gs://' prefix.\n",
-        "GCS_BUCKET = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# The service account for deploying fine tuned model.\n",
-        "# The service account looks like:\n",
-        "# '<account_name>@<project>.iam.gserviceaccount.com'\n",
-        "SERVICE_ACCOUNT = \"\"  # @param {type:\"string\"}"
+        "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
+        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -277,20 +271,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "8759e624ebc0"
       },
       "outputs": [],
       "source": [
         "import base64\n",
-        "import glob\n",
         "import os\n",
         "from datetime import datetime\n",
         "from io import BytesIO\n",
         "\n",
         "import requests\n",
-        "from google.cloud import aiplatform, storage\n",
+        "from google.cloud import aiplatform\n",
         "from PIL import Image\n",
         "\n",
         "\n",
@@ -333,6 +326,8 @@
         "        \"MODEL_ID\": model_id,\n",
         "        \"TASK\": task,\n",
         "    }\n",
+        "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
+        "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
         "    model = aiplatform.Model.upload(\n",
         "        display_name=model_name,\n",
         "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
@@ -340,6 +335,7 @@
         "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
         "        serving_container_health_route=\"/ping\",\n",
         "        serving_container_environment_variables=serving_env,\n",
+        "        artifact_uri=artifact_uri,\n",
         "    )\n",
         "    model.deploy(\n",
         "        endpoint=endpoint,\n",
@@ -347,31 +343,8 @@
         "        accelerator_type=\"NVIDIA_TESLA_V100\",\n",
         "        accelerator_count=1,\n",
         "        deploy_request_timeout=1800,\n",
-        "        service_account=SERVICE_ACCOUNT,\n",
         "    )\n",
-        "    return model, endpoint\n",
-        "\n",
-        "\n",
-        "def get_bucket_and_blob_name(filepath):\n",
-        "    # The gcs path is of the form gs://<bucket-name>/<blob-name>\n",
-        "    gs_suffix = filepath.split(\"gs://\", 1)[1]\n",
-        "    return tuple(gs_suffix.split(\"/\", 1))\n",
-        "\n",
-        "\n",
-        "def upload_local_dir_to_gcs(local_dir_path, gcs_dir_path):\n",
-        "    \"\"\"Uploads files in a local directory to a GCS directory.\"\"\"\n",
-        "    client = storage.Client()\n",
-        "    bucket_name = gcs_dir_path.split(\"/\")[2]\n",
-        "    bucket = client.get_bucket(bucket_name)\n",
-        "    for local_file in glob.glob(local_dir_path + \"/**\"):\n",
-        "        if not os.path.isfile(local_file):\n",
-        "            continue\n",
-        "        filename = local_file[1 + len(local_dir_path) :]\n",
-        "        gcs_file_path = os.path.join(gcs_dir_path, filename)\n",
-        "        _, blob_name = get_bucket_and_blob_name(gcs_file_path)\n",
-        "        blob = bucket.blob(blob_name)\n",
-        "        blob.upload_from_filename(local_file)\n",
-        "        print(\"Copied {} to {}.\".format(local_file, gcs_file_path))"
+        "    return model, endpoint"
       ]
     },
     {
@@ -408,8 +381,17 @@
         "!gdown --folder https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ\n",
         "\n",
         "# Upload data to Cloud Storage bucket.\n",
-        "upload_local_dir_to_gcs(\"dog\", f\"gs://{GCS_BUCKET}/dreambooth/dog\")\n",
-        "upload_local_dir_to_gcs(\"dog\", f\"gs://{GCS_BUCKET}/dreambooth/dog_class\")"
+        "!gsutil -m cp -r dog/* gs://{GCS_BUCKET}/dreambooth/dog/\n",
+        "!gsutil -m cp -r dog/* gs://{GCS_BUCKET}/dreambooth/dog_class/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "969cfeb79317"
+      },
+      "source": [
+        "**NOTE**: If the upload step fails due to lacking of permission, you need to [grant the Storage Object Admin role](https://cloud.google.com/storage/docs/access-control/using-iam-permissions) for the Cloud account of the notebook."
       ]
     },
     {
@@ -522,21 +504,12 @@
       },
       "outputs": [],
       "source": [
-        "# Set the model_id to \"runwayml/stable-diffusion-inpainting\" to load the OSS pre-trained model.\n",
+        "# Set the model_id to a GCS path, like \"gs://GCS_BUCKET/dreambooth/output\", to load the dreambooth finetuned model above.\n",
         "model, endpoint = deploy_model(\n",
-        "    model_id=f\"gs://{GCS_BUCKET}/dreambooth/output\", task=\"image-inpainting\"\n",
+        "    model_id=\"runwayml/stable-diffusion-inpainting\", task=\"image-inpainting\"\n",
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_timm.ipynb

@@ -1,559 +0,0 @@
-{
-  "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).\n",
-        "\n",
-        "1. [Create a service account](https://cloud.google.com/iam/docs/service-accounts-create#iam-service-accounts-create-console) with `Vertex AI User` and `Storage Object Admin` roles for deploying fine tuned model to Vertex AI endpoint."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "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. Please select a model from the verified model list above.\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\"}\n",
-        "\n",
-        "# The service account for deploying fine tuned model. It looks like:\n",
-        "# '<account_name>@<project>.iam.gserviceaccount.com'\n",
-        "# Follow step 6 above to create this account.\n",
-        "SERVICE_ACCOUNT = \"\"  # @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 prebuilt 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\"  # @param {type:\"string\"}\n",
-        "num_gpus = 4  # @param {type:\"integer\"}\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\"  # @param {type:\"string\"}\n",
-        "num_gpus = 2  # @param {type:\"integer\"}\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 prebuilt serving 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\"}"
-      ]
-    },
-    {
-      "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",
-        "    service_account=SERVICE_ACCOUNT,\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 = \"test.jpg\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Alternatively, uncomment the following line to download a cat image for demonstration.\n",
-        "# ! wget http://images.cocodataset.org/val2017/000000039769.jpg -O test.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
-}

notebooks/community/model_garden/model_garden_pytorch_vilt_vqa.ipynb

@@ -332,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_pytorch_vit_gpt2_image_captioning.ipynb

@@ -332,15 +332,6 @@
         ")"
       ]
     },
-    {
-      "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,

notebooks/community/model_garden/model_garden_tfvision_image_classification.ipynb

@@ -118,7 +118,7 @@
       },
       "source": [
         "### Colab Only\n",
-        "Run the following commands for Colab and skip this section if you use Workbench."
+        "Run the following commands for colab and skip this section if you use workbench."
       ]
     },
     {
@@ -181,16 +181,7 @@
         "# The project and bucket are for experiments below.\n",
         "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
         "BUCKET_URI = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# You can choose a region from https://cloud.google.com/about/locations.\n",
-        "# Only regions prefixed by \"us\", \"asia\", or \"europe\" are supported.\n",
-        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
-        "REGION_PREFIX = REGION.split(\"-\")[0]\n",
-        "assert REGION_PREFIX in (\n",
-        "    \"us\",\n",
-        "    \"europe\",\n",
-        "    \"asia\",\n",
-        "), f'{REGION} is not supported. It must be prefixed by \"us\", \"asia\", or \"europe\".'\n",
+        "REGION = \"us-central1\"\n",
         "\n",
         "! gcloud config set project $PROJECT_ID\n",
         "\n",
@@ -240,13 +231,13 @@
         "\n",
         "# Data converter constants.\n",
         "DATA_CONVERTER_JOB_PREFIX = \"data_converter\"\n",
-        "DATA_CONVERTER_CONTAINER = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter\"\n",
+        "DATA_CONVERTER_CONTAINER = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter:latest\"\n",
         "DATA_CONVERTER_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "\n",
         "# Training constants.\n",
         "TRAINING_JOB_PREFIX = \"train\"\n",
-        "TRAIN_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss\"\n",
+        "TRAIN_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss:latest\"\n",
         "TRAIN_MACHINE_TYPE = \"n1-highmem-16\"\n",
         "TRAIN_ACCELERATOR_TYPE = \"NVIDIA_TESLA_P100\"\n",
         "TRAIN_NUM_GPU = 1\n",
@@ -256,7 +247,7 @@
         "\n",
         "# Export constants.\n",
         "EXPORT_JOB_PREFIX = \"export\"\n",
-        "EXPORT_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving\"\n",
+        "EXPORT_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving:latest\"\n",
         "EXPORT_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "# Prediction constants.\n",
@@ -265,7 +256,9 @@
         "#   and optimized tensorflow runtime dockers: https://cloud.google.com/vertex-ai/docs/predictions/optimized-tensorflow-runtime.\n",
         "# The example in this notebook uses optimized tensorflow runtime dockers.\n",
         "# You can adjust accelerator types and machine types to get faster predictions.\n",
-        "PREDICTION_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        "PREDICTION_CONTAINER_URI = (\n",
+        "    \"us-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        ")\n",
         "SERVING_CONTAINER_ARGS = [\"--allow_precompilation\", \"--allow_compression\"]\n",
         "PREDICTION_ACCELERATOR_TYPE = \"NVIDIA_TESLA_T4\"\n",
         "PREDICTION_MACHINE_TYPE = \"n1-standard-4\"\n",
@@ -314,9 +307,10 @@
         "    endpoint_id: str,\n",
         "    instances: Union[Dict, List[Dict]],\n",
         "    location: str = \"us-central1\",\n",
+        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n",
         "):\n",
         "    # The AI Platform services require regional API endpoints.\n",
-        "    client_options = {\"api_endpoint\": f\"{location}-aiplatform.googleapis.com\"}\n",
+        "    client_options = {\"api_endpoint\": api_endpoint}\n",
         "    # Initialize client that will be used to create and send requests.\n",
         "    # This client only needs to be created once, and can be reused for multiple requests.\n",
         "    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)\n",
@@ -542,7 +536,7 @@
         "# input_train_data_path = ''\n",
         "# input_validation_data_path = ''\n",
         "\n",
-        "experiment = \"ResNet-50\"  # @param [\"ResNet-50\",\"ResNet-RS-50\",\"Efficientnetv2-m\",\"ViT-ti16\",\"ViT-s16\",\"ViT-b16\",\"ViT-l16\"]\n",
+        "experiment = \"ViT-s16\"  # @param [\"ResNet-50\",\"ResNet-RS-50\",\"Efficientnetv2-m\",\"ViT-ti16\",\"ViT-s16\",\"ViT-b16\",\"ViT-l16\"]\n",
         "\n",
         "train_job_name = get_job_name_with_datetime(TRAINING_JOB_PREFIX + \"_\" + OBJECTIVE)\n",
         "model_dir = os.path.join(BUCKET_URI, train_job_name)\n",
@@ -568,7 +562,6 @@
         "        **{\n",
         "            \"experiment\": \"resnet_imagenet\",\n",
         "            \"config_file\": os.path.join(CONFIG_DIR, \"imagenet_resnet50_gpu.yaml\"),\n",
-        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/resnet/resnet-50-i224.tar.gz\",\n",
         "        },\n",
         "    ),\n",
         "    \"ResNet-RS-50\": dict(\n",

notebooks/community/model_garden/model_garden_tfvision_image_object_detection.ipynb

@@ -118,7 +118,7 @@
       },
       "source": [
         "### Colab Only\n",
-        "Run the following commands for Colab and skip this section if you are using Workbench."
+        "Run the following commands for colab and skip this section if you use workbench."
       ]
     },
     {
@@ -181,37 +181,27 @@
         "# The project and bucket are for experiments below.\n",
         "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
         "BUCKET_URI = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# You can choose a region from https://cloud.google.com/about/locations.\n",
-        "# Only regions prefixed by \"us\", \"asia\", or \"europe\" are supported.\n",
-        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
-        "REGION_PREFIX = REGION.split(\"-\")[0]\n",
-        "assert REGION_PREFIX in (\n",
-        "    \"us\",\n",
-        "    \"europe\",\n",
-        "    \"asia\",\n",
-        "), f'{REGION} is not supported. It must be prefixed by \"us\", \"asia\", or \"europe\".'\n",
+        "REGION = \"us-central1\"\n",
         "\n",
         "! gcloud config set project $PROJECT_ID\n",
         "\n",
         "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
-        "CHECKPOINT_BUCKET = os.path.join(BUCKET_URI, \"ckpt\")\n",
         "\n",
         "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
         "\n",
         "# Download config files.\n",
         "CONFIG_DIR = os.path.join(BUCKET_URI, \"config\")\n",
         "! wget https://raw.githubusercontent.com/tensorflow/models/master/official/vision/configs/experiments/retinanet/coco_spinenet49_gpu_multiworker_mirrored.yaml\n",
-        "! gsutil cp coco_spinenet49_gpu_multiworker_mirrored.yaml $CONFIG_DIR/\n",
+        "! gsutil cp coco_spinenet49_gpu_multiworker_mirrored.yaml $CONFIG_DIR\n",
         "\n",
         "! wget https://raw.githubusercontent.com/tensorflow/models/master/official/vision/configs/experiments/retinanet/coco_spinenet96_gpu_multiworker_mirrored.yaml\n",
-        "! gsutil cp coco_spinenet96_gpu_multiworker_mirrored.yaml $CONFIG_DIR/\n",
+        "! gsutil cp coco_spinenet96_gpu_multiworker_mirrored.yaml $CONFIG_DIR\n",
         "\n",
         "! wget https://raw.githubusercontent.com/tensorflow/models/master/official/vision/configs/experiments/retinanet/coco_spinenet143_gpu_multiworker_mirrored.yaml\n",
-        "! gsutil cp coco_spinenet143_gpu_multiworker_mirrored.yaml $CONFIG_DIR/\n",
+        "! gsutil cp coco_spinenet143_gpu_multiworker_mirrored.yaml $CONFIG_DIR\n",
         "\n",
         "! wget https://raw.githubusercontent.com/tensorflow/models/master/official/projects/yolo/configs/experiments/yolov4/detection/scaled_yolov4_1280_gpu.yaml\n",
-        "! gsutil cp scaled_yolov4_1280_gpu.yaml $CONFIG_DIR/"
+        "! gsutil cp scaled_yolov4_1280_gpu.yaml $CONFIG_DIR"
       ]
     },
     {
@@ -235,13 +225,13 @@
         "\n",
         "# Data converter constants.\n",
         "DATA_CONVERTER_JOB_PREFIX = \"data_converter\"\n",
-        "DATA_CONVERTER_CONTAINER = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter\"\n",
+        "DATA_CONVERTER_CONTAINER = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter:latest\"\n",
         "DATA_CONVERTER_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "\n",
         "# Training constants.\n",
         "TRAINING_JOB_PREFIX = \"train\"\n",
-        "TRAIN_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss\"\n",
+        "TRAIN_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss:latest\"\n",
         "TRAIN_MACHINE_TYPE = \"n1-highmem-16\"\n",
         "TRAIN_ACCELERATOR_TYPE = \"NVIDIA_TESLA_V100\"\n",
         "TRAIN_NUM_GPU = 2\n",
@@ -261,7 +251,7 @@
         "\n",
         "# Export constants.\n",
         "EXPORT_JOB_PREFIX = \"export\"\n",
-        "EXPORT_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving\"\n",
+        "EXPORT_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving:latest\"\n",
         "EXPORT_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "# Prediction constants.\n",
@@ -270,7 +260,9 @@
         "#   and optimized tensorflow runtime dockers: https://cloud.google.com/vertex-ai/docs/predictions/optimized-tensorflow-runtime.\n",
         "# The example in this notebook uses optimized tensorflow runtime dockers.\n",
         "# You can adjust accelerator types and machine types to get faster predictions.\n",
-        "PREDICTION_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        "PREDICTION_CONTAINER_URI = (\n",
+        "    \"us-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        ")\n",
         "SERVING_CONTAINER_ARGS = [\"--allow_precompilation\", \"--allow_compression\"]\n",
         "PREDICTION_ACCELERATOR_TYPE = \"NVIDIA_TESLA_T4\"\n",
         "PREDICTION_MACHINE_TYPE = \"n1-standard-4\"\n",
@@ -321,9 +313,10 @@
         "    endpoint_id: str,\n",
         "    instances: Union[Dict, List[Dict]],\n",
         "    location: str = \"us-central1\",\n",
+        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n",
         "):\n",
         "    # The AI Platform services require regional API endpoints.\n",
-        "    client_options = {\"api_endpoint\": f\"{location}-aiplatform.googleapis.com\"}\n",
+        "    client_options = {\"api_endpoint\": api_endpoint}\n",
         "    # Initialize client that will be used to create and send requests.\n",
         "    # This client only needs to be created once, and can be reused for multiple requests.\n",
         "    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)\n",
@@ -476,30 +469,7 @@
         "                font,\n",
         "                display_str_list=[display_str],\n",
         "            )\n",
-        "    return image\n",
-        "\n",
-        "\n",
-        "def upload_checkpoint_to_gcs(checkpoint_url):\n",
-        "    filename = os.path.basename(checkpoint_url)\n",
-        "    checkpoint_name = filename.replace(\".tar.gz\", \"\")\n",
-        "    print(\"Download checkpoint from\", checkpoint_url, \"and store to\", CHECKPOINT_BUCKET)\n",
-        "    ! wget $checkpoint_url -O $filename\n",
-        "    ! mkdir -p $checkpoint_name\n",
-        "    ! tar -xvzf $filename -C $checkpoint_name\n",
-        "\n",
-        "    # Search for relative path to the checkpoint.\n",
-        "    checkpoint_path = None\n",
-        "    for root, dirs, files in os.walk(checkpoint_name):\n",
-        "        for file in files:\n",
-        "            if file.endswith(\".index\"):\n",
-        "                checkpoint_path = os.path.join(root, os.path.splitext(file)[0])\n",
-        "                checkpoint_path = os.path.relpath(checkpoint_path, checkpoint_name)\n",
-        "                break\n",
-        "\n",
-        "    ! gsutil cp -r $checkpoint_name $CHECKPOINT_BUCKET/\n",
-        "    checkpoint_uri = os.path.join(CHECKPOINT_BUCKET, checkpoint_name, checkpoint_path)\n",
-        "    print(\"Checkpoint uploaded to\", checkpoint_uri)\n",
-        "    return checkpoint_uri"
+        "    return image"
       ]
     },
     {
@@ -651,8 +621,8 @@
         "    \"objective\": OBJECTIVE,\n",
         "    \"model_dir\": model_dir,\n",
         "    \"num_classes\": num_classes,\n",
-        "    \"global_batch_size\": 2,\n",
-        "    \"prefetch_buffer_size\": 6,\n",
+        "    \"global_batch_size\": 4,\n",
+        "    \"prefetch_buffer_size\": 12,\n",
         "    \"train_steps\": 2000,\n",
         "    \"input_size\": \"1024,1024\",\n",
         "}\n",
@@ -691,19 +661,9 @@
         "        **{\n",
         "            \"experiment\": \"scaled_yolo\",\n",
         "            \"config_file\": TRAIN_YOLOV4_CONFIG,\n",
-        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/yolo/scaled-yolov4/scaled-yolov4-l-p6-i1280.tar.gz\",\n",
-        "            \"input_size\": \"1280,1280\",\n",
         "        },\n",
         "    ),\n",
         "}\n",
-        "experiment_container_args = experiment_container_args_dict[experiment]\n",
-        "\n",
-        "# Copy checkpoint to GCS bucket if specified.\n",
-        "init_checkpoint = experiment_container_args.get(\"init_checkpoint\")\n",
-        "if init_checkpoint:\n",
-        "    experiment_container_args[\"init_checkpoint\"] = upload_checkpoint_to_gcs(\n",
-        "        init_checkpoint\n",
-        "    )\n",
         "\n",
         "params_override = \"runtime.num_gpus=%s\" % TRAIN_NUM_GPU\n",
         "eval_params_override = \"runtime.num_gpus=1,runtime.distribution_strategy=mirrored\"\n",
@@ -721,7 +681,10 @@
         "                \"--mode=train\",\n",
         "                \"--params_override=%s\" % params_override,\n",
         "            ]\n",
-        "            + [\"--{}={}\".format(k, v) for k, v in experiment_container_args.items()],\n",
+        "            + [\n",
+        "                \"--{}={}\".format(k, v)\n",
+        "                for k, v in experiment_container_args_dict[experiment].items()\n",
+        "            ],\n",
         "        },\n",
         "    },\n",
         "    {},\n",
@@ -739,7 +702,10 @@
         "                \"--mode=continuous_eval\",\n",
         "                \"--params_override=%s\" % eval_params_override,\n",
         "            ]\n",
-        "            + [\"--{}={}\".format(k, v) for k, v in experiment_container_args.items()],\n",
+        "            + [\n",
+        "                \"--{}={}\".format(k, v)\n",
+        "                for k, v in experiment_container_args_dict[experiment].items()\n",
+        "            ],\n",
         "        },\n",
         "    },\n",
         "]\n",
@@ -841,7 +807,8 @@
         "            \"args\": [\n",
         "                \"--objective=%s\" % OBJECTIVE,\n",
         "                \"--input_image_size=1024,1024\",\n",
-        "                \"--experiment=%s\" % experiment_container_args[\"experiment\"],\n",
+        "                \"--experiment=%s\"\n",
+        "                % experiment_container_args_dict[experiment][\"experiment\"],\n",
         "                \"--config_file=%s/params.yaml\" % best_trial_dir,\n",
         "                \"--checkpoint_path=%s/best_ckpt\" % best_trial_dir,\n",
         "                \"--export_dir=%s/best_model\" % model_dir,\n",

notebooks/community/model_garden/model_garden_tfvision_image_segmentation.ipynb

@@ -119,7 +119,7 @@
       "source": [
         "### Colab Only\n",
         "\n",
-        "Run the following commands for Colab and skip this section if you use Workbench."
+        "Run the following commands for colab and skip this section if you use workbench."
       ]
     },
     {
@@ -182,28 +182,18 @@
         "# The project and bucket are for experiments below.\n",
         "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
         "BUCKET_URI = \"\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# You can choose a region from https://cloud.google.com/about/locations.\n",
-        "# Only regions prefixed by \"us\", \"asia\", or \"europe\" are supported.\n",
-        "REGION = \"europe-west4\"  # @param {type:\"string\"}\n",
-        "REGION_PREFIX = REGION.split(\"-\")[0]\n",
-        "assert REGION_PREFIX in (\n",
-        "    \"us\",\n",
-        "    \"europe\",\n",
-        "    \"asia\",\n",
-        "), f'{REGION} is not supported. It must be prefixed by \"us\", \"asia\", or \"europe\".'\n",
+        "REGION = \"us-central1\"\n",
         "\n",
         "! gcloud config set project $PROJECT_ID\n",
         "\n",
         "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
-        "CHECKPOINT_BUCKET = os.path.join(BUCKET_URI, \"ckpt\")\n",
         "\n",
         "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
         "\n",
         "# Download config files.\n",
         "CONFIG_DIR = os.path.join(BUCKET_URI, \"config\")\n",
         "! wget https://raw.githubusercontent.com/tensorflow/models/master/official/vision/configs/experiments/semantic_segmentation/deeplabv3plus_resnet101_cityscapes_gpu_multiworker_mirrored.yaml\n",
-        "! gsutil cp deeplabv3plus_resnet101_cityscapes_gpu_multiworker_mirrored.yaml $CONFIG_DIR/"
+        "! gsutil cp deeplabv3plus_resnet101_cityscapes_gpu_multiworker_mirrored.yaml $CONFIG_DIR"
       ]
     },
     {
@@ -227,13 +217,13 @@
         "\n",
         "# Data converter constants.\n",
         "DATA_CONVERTER_JOB_PREFIX = \"data_converter\"\n",
-        "DATA_CONVERTER_CONTAINER = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter\"\n",
+        "DATA_CONVERTER_CONTAINER = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/data-converter:latest\"\n",
         "DATA_CONVERTER_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "\n",
         "# Training constants.\n",
         "TRAINING_JOB_PREFIX = \"train\"\n",
-        "TRAIN_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss\"\n",
+        "TRAIN_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-oss:latest\"\n",
         "TRAIN_MACHINE_TYPE = \"n1-highmem-16\"\n",
         "TRAIN_ACCELERATOR_TYPE = \"NVIDIA_TESLA_V100\"\n",
         "TRAIN_NUM_GPU = 2\n",
@@ -246,7 +236,7 @@
         "\n",
         "# Export constants.\n",
         "EXPORT_JOB_PREFIX = \"export\"\n",
-        "EXPORT_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving\"\n",
+        "EXPORT_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/tfvision-serving:latest\"\n",
         "EXPORT_MACHINE_TYPE = \"n1-highmem-8\"\n",
         "\n",
         "# Prediction constants.\n",
@@ -255,7 +245,9 @@
         "#   and optimized tensorflow runtime dockers: https://cloud.google.com/vertex-ai/docs/predictions/optimized-tensorflow-runtime.\n",
         "# The example in this notebook uses optimized tensorflow runtime dockers.\n",
         "# You can adjust accelerator types and machine types to get faster predictions.\n",
-        "PREDICTION_CONTAINER_URI = f\"{REGION_PREFIX}-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        "PREDICTION_CONTAINER_URI = (\n",
+        "    \"us-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.2-11:latest\"\n",
+        ")\n",
         "SERVING_CONTAINER_ARGS = [\"--allow_precompilation\", \"--allow_compression\"]\n",
         "PREDICTION_ACCELERATOR_TYPE = \"NVIDIA_TESLA_T4\"\n",
         "PREDICTION_MACHINE_TYPE = \"n1-standard-4\"\n",
@@ -306,9 +298,10 @@
         "    endpoint_id: str,\n",
         "    instances: Union[Dict, List[Dict]],\n",
         "    location: str = \"us-central1\",\n",
+        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n",
         "):\n",
         "    # The AI Platform services require regional API endpoints.\n",
-        "    client_options = {\"api_endpoint\": f\"{location}-aiplatform.googleapis.com\"}\n",
+        "    client_options = {\"api_endpoint\": api_endpoint}\n",
         "    # Initialize client that will be used to create and send requests.\n",
         "    # This client only needs to be created once, and can be reused for multiple requests.\n",
         "    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)\n",
@@ -515,30 +508,7 @@
         "        ]\n",
         "    category_image_color = Image.fromarray(category_image_color_np)\n",
         "\n",
-        "    return score_image_grayscale, category_image_color\n",
-        "\n",
-        "\n",
-        "def upload_checkpoint_to_gcs(checkpoint_url):\n",
-        "    filename = os.path.basename(checkpoint_url)\n",
-        "    checkpoint_name = filename.replace(\".tar.gz\", \"\")\n",
-        "    print(\"Download checkpoint from\", checkpoint_url, \"and store to\", CHECKPOINT_BUCKET)\n",
-        "    ! wget $checkpoint_url -O $filename\n",
-        "    ! mkdir -p $checkpoint_name\n",
-        "    ! tar -xvzf $filename -C $checkpoint_name\n",
-        "\n",
-        "    # Search for relative path to the checkpoint.\n",
-        "    checkpoint_path = None\n",
-        "    for root, dirs, files in os.walk(checkpoint_name):\n",
-        "        for file in files:\n",
-        "            if file.endswith(\".index\"):\n",
-        "                checkpoint_path = os.path.join(root, os.path.splitext(file)[0])\n",
-        "                checkpoint_path = os.path.relpath(checkpoint_path, checkpoint_name)\n",
-        "                break\n",
-        "\n",
-        "    ! gsutil cp -r $checkpoint_name $CHECKPOINT_BUCKET/\n",
-        "    checkpoint_uri = os.path.join(CHECKPOINT_BUCKET, checkpoint_name, checkpoint_path)\n",
-        "    print(\"Checkpoint uploaded to\", checkpoint_uri)\n",
-        "    return checkpoint_uri"
+        "    return score_image_grayscale, category_image_color"
       ]
     },
     {
@@ -695,17 +665,8 @@
         "        \"prefetch_buffer_size\": 12,\n",
         "        \"train_steps\": 500,\n",
         "        \"output_size\": \"1024,2048\",\n",
-        "        \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/deeplabv3plus/dilated-resnet-101-deeplabv3plus.tar.gz\",\n",
         "    }\n",
         "}\n",
-        "experiment_container_args = experiment_container_args_dict[experiment]\n",
-        "\n",
-        "# Copy checkpoint to GCS bucket if specified.\n",
-        "init_checkpoint = experiment_container_args.get(\"init_checkpoint\")\n",
-        "if init_checkpoint:\n",
-        "    experiment_container_args[\"init_checkpoint\"] = upload_checkpoint_to_gcs(\n",
-        "        init_checkpoint\n",
-        "    )\n",
         "\n",
         "worker_pool_specs = [\n",
         "    {\n",
@@ -720,7 +681,10 @@
         "            \"args\": [\n",
         "                \"--mode=train_and_eval\",\n",
         "            ]\n",
-        "            + [\"--{}={}\".format(k, v) for k, v in experiment_container_args.items()],\n",
+        "            + [\n",
+        "                \"--{}={}\".format(k, v)\n",
+        "                for k, v in experiment_container_args_dict[experiment].items()\n",
+        "            ],\n",
         "        },\n",
         "    },\n",
         "]\n",
@@ -823,11 +787,13 @@
         "            \"command\": [],\n",
         "            \"args\": [\n",
         "                \"--objective=%s\" % OBJECTIVE,\n",
-        "                \"--experiment=%s\" % experiment_container_args[\"experiment\"],\n",
+        "                \"--experiment=%s\"\n",
+        "                % experiment_container_args_dict[experiment][\"experiment\"],\n",
         "                \"--config_file=%s/params.yaml\" % best_trial_dir,\n",
         "                \"--checkpoint_path=%s/best_ckpt\" % best_trial_dir,\n",
         "                \"--export_dir=%s/best_model\" % model_dir,\n",
-        "                \"--input_image_size=%s\" % experiment_container_args[\"output_size\"],\n",
+        "                \"--input_image_size=%s\"\n",
+        "                % experiment_container_args_dict[experiment][\"output_size\"],\n",
         "            ],\n",
         "        },\n",
         "    }\n",

notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/tabular_optimized_online_prediction.ipynb

@@ -1479,7 +1479,7 @@
       "source": [
         "For more information about VPC peering in Vertex AI, see https://cloud.google.com/vertex-ai/docs/general/vpc-peering.\n",
         "\n",
-        "**IMPORTANT: you can only setup one VPC peering to servicenetworking.googleapis.com per VPC network.**"
+        "**IMPORTANT: you can only setup one VPC peering to servicenetworking.googleapis.com per project.**"
       ]
     },
     {

notebooks/official/automl/README.md

@@ -57,77 +57,6 @@ The steps performed are:
    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.
-
-```
-
-   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`.
-
-```
-
-   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`.
-
-```
-
-   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)
 
 ```
@@ -143,38 +72,6 @@ The steps performed are:
    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.
-
-```
-
-   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)
 
 ```
@@ -204,7 +101,9 @@ The steps performed include:
 - Create a Vertex AI `TimeSeriesDataset` resource.
 - Train the model.
 - View the model evaluation.
-- Make a batch prediction.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`.
 
 ```
 

notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb

@@ -123,6 +123,39 @@
         "to generate a cost estimate based on your projected usage."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "setup_local"
+      },
+      "source": [
+        "### Set up your local development environment\n",
+        "\n",
+        "If you are using Colab or Google Cloud Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
+        "\n",
+        "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
+        "\n",
+        "- The Cloud Storage SDK\n",
+        "- Git\n",
+        "- Python 3\n",
+        "- virtualenv\n",
+        "- Jupyter notebook running in a virtual environment with Python 3\n",
+        "\n",
+        "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
+        "\n",
+        "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
+        "\n",
+        "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
+        "\n",
+        "3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3.  Activate the virtual environment.\n",
+        "\n",
+        "4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.\n",
+        "\n",
+        "5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
+        "\n",
+        "6. Open this notebook in the Jupyter Notebook Dashboard.\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -131,7 +164,7 @@
       "source": [
         "## Installation\n",
         "\n",
-        "Install the latest version of Vertex AI SDK for Python."
+        "Install the latest version of Vertex SDK for Python."
       ]
     },
     {
@@ -144,8 +177,33 @@
       "source": [
         "import os\n",
         "\n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-storage"
+        "# Google Cloud Notebook\n",
+        "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
+        "    USER_FLAG = \"--user\"\n",
+        "else:\n",
+        "    USER_FLAG = \"\"\n",
+        "\n",
+        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "install_storage"
+      },
+      "source": [
+        "Install the latest GA version of *google-cloud-storage* library as well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "install_storage"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install -U google-cloud-storage $USER_FLAG"
       ]
     },
     {
@@ -154,38 +212,57 @@
         "id": "restart"
       },
       "source": [
-        "### Colab only: Uncomment the following cell to restart the kernel"
+        "### Restart the kernel\n",
+        "\n",
+        "Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "D-ZBOjErv5mM"
+        "id": "restart"
       },
       "outputs": [],
       "source": [
-        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
-        "# import IPython\n",
+        "import os\n",
         "\n",
-        "# app = IPython.Application.instance()\n",
-        "# app.kernel.do_shutdown(True)"
+        "if not os.getenv(\"IS_TESTING\"):\n",
+        "    # Automatically restart kernel after installs\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yfEglUHQk9S3"
+        "id": "before_you_begin:nogpu"
       },
       "source": [
         "## Before you begin\n",
         "\n",
-        "### Set your project ID\n",
+        "### GPU runtime\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)"
+        "This tutorial does not require a GPU runtime.\n",
+        "\n",
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
+        "\n",
+        "3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
+        "\n",
+        "4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
+        "\n",
+        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
+        "Cloud SDK uses the right project for all the commands in this notebook.\n",
+        "\n",
+        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
       ]
     },
     {
@@ -196,10 +273,33 @@
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-        "\n",
-        "# Set the project id\n",
-        "! gcloud config set project {PROJECT_ID}"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "autoset_project_id"
+      },
+      "outputs": [],
+      "source": [
+        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
+        "    # Get your GCP project id from gcloud\n",
+        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
+        "    PROJECT_ID = shell_output[0]\n",
+        "    print(\"Project ID:\", PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "set_gcloud_project_id"
+      },
+      "outputs": [],
+      "source": [
+        "! gcloud config set project $PROJECT_ID"
       ]
     },
     {
@@ -210,7 +310,16 @@
       "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)."
+        "You can also change the `REGION` variable, which is used for operations\n",
+        "throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
+        "\n",
+        "- Americas: `us-central1`\n",
+        "- Europe: `europe-west4`\n",
+        "- Asia Pacific: `asia-east1`\n",
+        "\n",
+        "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
+        "\n",
+        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
       ]
     },
     {
@@ -221,7 +330,33 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "timestamp"
+      },
+      "source": [
+        "#### Timestamp\n",
+        "\n",
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "timestamp"
+      },
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
       ]
     },
     {
@@ -232,54 +367,53 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
         "\n",
-        "**1. Vertex AI Workbench**\n",
-        "* Do nothing as you are already authenticated.\n",
+        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
         "\n",
-        "**2. Local JupyterLab instance, uncomment and run:**"
+        "**Otherwise**, follow these steps:\n",
+        "\n",
+        "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
+        "\n",
+        "**Click Create service account**.\n",
+        "\n",
+        "In the **Service account name** field, enter a name, and click **Create**.\n",
+        "\n",
+        "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+        "\n",
+        "Click Create. A JSON file that contains your key downloads to your local environment.\n",
+        "\n",
+        "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "ce6043da7b33"
+        "id": "gcp_authenticate"
       },
       "outputs": [],
       "source": [
-        "# ! gcloud auth login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0367eac06a10"
-      },
-      "source": [
-        "**3. Colab, uncomment and run:**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "21ad4dbb4a61"
-      },
-      "outputs": [],
-      "source": [
-        "# from google.colab import auth\n",
-        "# auth.authenticate_user()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c13224697bfb"
-      },
-      "source": [
-        "**4. Service account or other**\n",
-        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+        "# If you are running this notebook in Colab, run this cell and follow the\n",
+        "# instructions to authenticate your GCP account. This provides access to your\n",
+        "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
+        "# requests.\n",
+        "\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "# If on Google Cloud Notebook, then don't execute this code\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
+        "    if \"google.colab\" in sys.modules:\n",
+        "        from google.colab import auth as google_auth\n",
+        "\n",
+        "        google_auth.authenticate_user()\n",
+        "\n",
+        "    # If you are running this notebook locally, replace the string below with the\n",
+        "    # path to your service account key and run this cell to authenticate your GCP\n",
+        "    # account.\n",
+        "    elif not os.getenv(\"IS_TESTING\"):\n",
+        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
       ]
     },
     {
@@ -290,7 +424,11 @@
       "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
-        "Create a storage bucket to store intermediate artifacts such as datasets."
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
+        "\n",
+        "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
       ]
     },
     {
@@ -301,7 +439,19 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "autoset_bucket"
+      },
+      "outputs": [],
+      "source": [
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
+        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
       ]
     },
     {
@@ -321,7 +471,27 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_NAME"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "validate_bucket"
+      },
+      "source": [
+        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "validate_bucket"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil ls -al $BUCKET_NAME"
       ]
     },
     {
@@ -368,7 +538,7 @@
       },
       "outputs": [],
       "source": [
-        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
       ]
     },
     {
@@ -460,7 +630,7 @@
       "outputs": [],
       "source": [
         "dataset = aiplatform.TextDataset.create(\n",
-        "    display_name=\"NCBI Biomedical\",\n",
+        "    display_name=\"NCBI Biomedical\" + \"_\" + TIMESTAMP,\n",
         "    gcs_source=[IMPORT_FILE],\n",
         "    import_schema_uri=aiplatform.schema.dataset.ioformat.text.extraction,\n",
         ")\n",
@@ -500,7 +670,7 @@
       "outputs": [],
       "source": [
         "job = aiplatform.AutoMLTextTrainingJob(\n",
-        "    display_name=\"biomedical\", prediction_type=\"extraction\"\n",
+        "    display_name=\"biomedical_\" + TIMESTAMP, prediction_type=\"extraction\"\n",
         ")\n",
         "\n",
         "print(job)"
@@ -537,7 +707,7 @@
       "source": [
         "model = job.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"biomedical\",\n",
+        "    model_display_name=\"biomedical_\" + TIMESTAMP,\n",
         "    training_fraction_split=0.8,\n",
         "    validation_fraction_split=0.1,\n",
         "    test_fraction_split=0.1,\n",
@@ -565,7 +735,7 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=biomedical\")\n",
+        "models = aiplatform.Model.list(filter=\"display_name=biomedical_\" + TIMESTAMP)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
         "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -746,7 +916,7 @@
         "job.delete()\n",
         "\n",
         "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "    ! gsutil -m rm -r $BUCKET_NAME"
       ]
     }
   ],

notebooks/official/custom/README.md

@@ -60,22 +60,6 @@ The steps performed include:
    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.
-
-```
-
-   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)
 
 ```
@@ -93,45 +77,6 @@ The steps performed include:
    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.
-
-```
-
-   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).
-
-```
-
-   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)
 
 ```

notebooks/official/datasets/README.md

@@ -18,8 +18,6 @@ The steps performed include:
 
    Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro).
 
-   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)
 

notebooks/official/experiments/README.md

@@ -56,24 +56,6 @@ The steps performed include:
    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)
 
 ```
@@ -106,18 +88,3 @@ 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.
-
-```
-

notebooks/official/experiments/comparing_local_trained_models.ipynb

@@ -628,7 +628,6 @@
       "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",
@@ -968,12 +967,6 @@
         "\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()"

notebooks/official/experiments/get_started_with_vertex_experiments.ipynb

@@ -192,7 +192,7 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform pandas {USER_FLAG} -q"
+        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q"
       ]
     },
     {

notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb

@@ -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/experiments/get_started_with_vertex_experiments_autologging.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/autologging.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/get_started_with_vertex_experiments_autologging.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/autologging.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/experiments/get_started_with_vertex_experiments_autologging.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/experiments/autologging.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",

notebooks/official/explainable_ai/README.md

@@ -60,7 +60,7 @@ The steps performed include:
    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 explainability](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 explainabilty](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.

notebooks/official/migration/README.md

@@ -1,5 +1,5 @@
 
-[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-classification-batch-online.ipynb)
+[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.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.
@@ -18,14 +18,15 @@ The steps performed include:
    Learn more about [Classification for image data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_images).
 
 
-[AutoML image object detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-image-object-detection-batch-online.ipynb)
+[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)
 
 ```
-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.
+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:
 
-- Train an AutoML object detection model.
+- 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
@@ -34,7 +35,40 @@ The steps performed include:
 
    Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
 
-   Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
+   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.
+
+```
+
+   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+   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.
+
+```
+
+   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+   Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
 
 
 [AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb)
@@ -54,7 +88,55 @@ The steps performed include:
    Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos).
 
 
-[AutoML tabular binary classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-tabular-binary-classification-online-prediction.ipynb)
+[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.*
+
+```
+
+   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+   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.*
+
+```
+
+   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+   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)
 
 ```
 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.
@@ -75,7 +157,26 @@ The steps performed include:
    Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
 
 
-[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb)
+[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
+
+```
+
+   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+   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)
 
 ```
 The objective of this notebook is to build a AutoML Text Classification Model.
@@ -97,7 +198,7 @@ The steps performed include the following:
    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/sdk-automl-text-classification-batch-prediction.ipynb)
+[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
 
 ```
 The objective of this notebook is to build a AutoML Text Entity Extraction model.
@@ -140,72 +241,7 @@ The steps performed include the following:
    Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
 
 
-[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.
-
-```
-
-   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-   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.*
-
-```
-
-   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-   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.*
-
-```
-
-   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-   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)
+[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)
 
 ```
 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.
@@ -224,39 +260,3 @@ The steps performed include:
 
    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
-
-```
-
-   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-   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.
-
-```
-
-   Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
-
-   Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-

notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb

@@ -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/sdk-automl-image-classification-batch-online.ipynb\">\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",
         "      <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/sdk-automl-image-classification-batch-online.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.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/sdk-automl-image-classification-batch-online.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/UJ1 Vertex SDK AutoML Image Classification.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": "sdk-automl-image-classification-batch-online.ipynb",
+      "name": "UJ1 Vertex SDK AutoML Image Classification.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb

@@ -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/sdk-hyperparameter-tuning.ipynb\">\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",
         "      <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/sdk-hyperparameter-tuning.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex 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/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/UJ11 Vertex 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": "sdk-hyperparameter-tuning.ipynb",
+      "name": "UJ11 Vertex SDK Hyperparameter Tuning.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb

@@ -1297,7 +1297,7 @@
   ],
   "metadata": {
     "colab": {
-      "name": "sdk-automl-video-classification-batch-prediction.ipynb",
+      "name": "UJ14 Vertex SDK AutoML Video Classification.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb

@@ -633,9 +633,8 @@
         "    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)"
@@ -1252,7 +1251,7 @@
   ],
   "metadata": {
     "colab": {
-      "name": "sdk-automl-object-tracking-batch-prediction.ipynb",
+      "name": "UJ15 Vertex SDK AutoML Object Tracking.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb

@@ -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/sdk-custom-image-classification-prebuilt-container.ipynb\">\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",
         "      <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/sdk-custom-image-classification-prebuilt-container.ipynb\">\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",
         "      <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/sdk-custom-image-classification-prebuilt-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/UJ2,12 Vertex SDK Custom Image Classification with pre-built training 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": "sdk-custom-image-classification-prebuilt-container.ipynb",
+      "name": "UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb

@@ -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/sdk-custom-image-classification-custom-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-image-classification-custom-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-image-classification-custom-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/UJ3 Vertex SDK Custom Image Classification with custom training 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": "sdk-custom-image-classification-custom-container.ipynb",
+      "name": "UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb

@@ -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/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\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",
         "      <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/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.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/sdk-automl-tabular-binary-classification-online-prediction.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.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": "sdk-automl-tabular-binary-classification-online-prediction.ipynb",
+      "name": "UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb

@@ -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/sdk-automl-image-object-detection-batch-online.ipynb\">\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",
         "      <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/sdk-automl-image-object-detection-batch-online.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.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/sdk-automl-image-object-detection-batch-online.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/UJ5 Vertex SDK AutoML Image Object Detection.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": "sdk-automl-image-object-detection-batch-online.ipynb",
+      "name": "UJ5 Vertex SDK AutoML Image Object Detection.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb

@@ -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/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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",
         "      <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/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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",
         "      <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/sdk-automl-text-classification-batch-prediction.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/UJ6 Vertex SDK AutoML Text Classification.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": "sdk-automl-text-classification-batch-prediction.ipynb",
+      "name": "UJ6 Vertex SDK AutoML Text Classification.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb

@@ -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/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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",
         "      <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/sdk-automl-text-classification-batch-prediction.ipynb\" target='_blank'>\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",
         "      <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/sdk-automl-text-classification-batch-prediction.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/UJ7 Vertex SDK AutoML Text Entity Extraction.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": "sdk-automl-text-entity-extraction-batch-prediction.ipynb",
+      "name": "UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb

@@ -1413,7 +1413,7 @@
   ],
   "metadata": {
     "colab": {
-      "name": "sdk-automl-text-sentiment-analysis-batch-prediction.ipynb",
+      "name": "UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb

@@ -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/sdk-custom-xgboost-prebuilt-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-xgboost-prebuilt-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-xgboost-prebuilt-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/UJ9 Vertex SDK Custom XGBoost with pre-built training 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": "sdk-custom-xgboost-prebuilt-container.ipynb",
+      "name": "UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

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

@@ -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/sdk-custom-scikit-learn-prebuilt-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-scikit-learn-prebuilt-container.ipynb\" target='_blank'>\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",
         "      <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/sdk-custom-scikit-learn-prebuilt-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/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training 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",

notebooks/official/model_evaluation/README.md

@@ -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 Text Classification model on the `Dataset` resource.
+- Train a Automl Tabular 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`.
@@ -152,5 +152,3 @@ The steps performed include:
 
 ```
 
-   Learn more about [Model evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
-

notebooks/official/pipelines/README.md

@@ -23,7 +23,7 @@ The steps performed include:
    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/official/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/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.
@@ -45,10 +45,6 @@ The steps performed include:
 
 ```
 
-   Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-   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)
 
@@ -88,7 +84,7 @@ The steps performed include:
    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 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)
+[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)
 
 ```
 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`.
@@ -114,43 +110,6 @@ The steps performed include:
    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.
-
-```
-
-   Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-   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)
 
 ```
@@ -339,32 +298,6 @@ The steps performed include:
    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)
 
 ```

notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb

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

notebooks/official/prediction/README.md

@@ -14,40 +14,3 @@ The steps performed include:
 
    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
-
-```
-
-   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
-
-```
-

notebooks/official/tabnet/README.md

@@ -15,26 +15,6 @@ The steps performed are:
    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.
-
-```
-
-   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)
 
 ```

notebooks/official/tabular_workflows/README.md

@@ -14,8 +14,6 @@ The steps performed are:
 
    Learn more about [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction).
 
-   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)
 

notebooks/official/vizier/README.md

@@ -10,19 +10,3 @@ Learn how to use `Vertex AI Vizier` to optimize a multi-objective study.
 
    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
-
-```
-
-   Learn more about [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview).
-

notebooks/official/workbench/README.md

@@ -81,7 +81,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/managed/visualize-data-bigquery).
+   Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
    Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
 
@@ -127,7 +127,7 @@ The steps performed include:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml#machine_learning_directly_in).
+   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
 
 
 [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:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml#machine_learning_directly_in).
+   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
 
 
 [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:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
+   Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/tutorials/text-classification-automl/training).
 
 
 [Digest and analyze data from BigQuery with Dataproc](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_bigquery.ipynb)