migration: move to pipelines folder

.cloud-build/requirements.txt

@@ -11,3 +11,4 @@ google-cloud-storage
 google-cloud-build
 ratemate
 GitPython
+google-api-core==2.10

notebooks/community/ml_ops/stage5/get_started_with_vertex_endpoint_and_shared_vm.ipynb

@@ -1028,9 +1028,6 @@
         "deployment_resource_pool.dedicated_resources.min_replica_count = MIN_NODES\n",
         "deployment_resource_pool.dedicated_resources.max_replica_count = MAX_NODES\n",
         "deployment_resource_pool.dedicated_resources.machine_spec.machine_type = DEPLOY_COMPUTE\n",
-        "if DEPLOY_NGPU:\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_type = DEPLOY_GPU\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_count = DEPLOY_NGPU\n",
         "\n",
         "request = aip_beta.CreateDeploymentResourcePoolRequest(\n",
         "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",

notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/benchmark.py

@@ -112,7 +112,7 @@ def benchmark(
 
     results = []
     for qps in qps_list:
-        num_requests = int(max(qps * duration_sec, 10))
+        num_requests = max(qps * duration_sec, 10)
         requests_for_qps = list(
             itertools.islice(itertools.cycle(requests), num_requests)
         )

notebooks/notebook_template_review.py

@@ -12,10 +12,6 @@
         --errors-codes: A list of error codes to report errors. Otherwise, all errors are reported.
         --errors-csv: Report errors in CSV format
         
-        # options for automatic fixing
-        --fix: Automatic fix
-        --fix-codes: A list of fix codes to fix. Otherwise, all fix codes are enabled.
-        
         # index generatation
         --repo: Generate index in markdown format
         --web: Generate index in HTML format
@@ -23,7 +19,6 @@
         --desc: Add description to index
         --steps: Add steps to index
         --uses: Add "resources" used to index
-        --linkback: Add linkback to index
         
     Format of CSV file for notebooks to review:
     

notebooks/official/bigquery_ml/bqml-online-prediction.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook is aimed at data analysts and data scientists who have data in BigQuery, want to train a model using BigQuery ML, register the model to Vertex AI Model Registry, and deploy it to an endpoint for real-time prediction. \n",
         "\n",
-        "Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
       ]
     },
     {

notebooks/official/datasets/get_started_bq_datasets.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# Get started with BigQuery datasets\n",
+        "# E2E ML on GCP: MLOps stage 1 : data management: get started with BigQuery datasets\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -61,7 +61,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with BigQuery datasets.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with BigQuery datasets.\n",
         "\n",
         "Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro)."
       ]

notebooks/official/datasets/get_started_with_data_labeling.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# Get started with Vertex AI Data Labeling\n",
+        "# E2E ML on GCP: MLOps stage 1 : formalization: get started with Vertex AI Data Labeling\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -62,7 +62,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with Vertex AI Data Labeling service.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with Vertex AI Data Labeling service.\n",
         "\n",
         "Learn more about [Vertex AI Data Labeling](https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job)."
       ]

notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb

@@ -65,7 +65,7 @@
         "\n",
         "The goal of the tutorial is to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.\n",
         "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
       ]
     },
     {

notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb

@@ -67,7 +67,7 @@
         "\n",
         "TabNet uses a machine learning technique called sequential attention to select which model features to reason from at each step in the model. This mechanism makes it possible to explain how the model arrives at its predictions and helps it learn more accurate models. Thanks to this design, TabNet not only outperforms other neural networks and decision trees but also provides interpretable feature attributions. Releasing TabNet as a First Party Trainer in Vertex AI means you'll be able to easily take advantage of TabNet's architecture and explainability and use it to train models on your own data. \n",
         "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview)."
       ]
     },
     {

notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook showcases how to run the TabNet algorithm using Vertex AI Tabular Workflows.\n",
         "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
       ]
     },
     {

notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook showcases how to run the Wide & Deep algorithm using Vertex AI Tabular Workflows.\n",
         "\n",
-        "Learn more about [Tabular Workflow for Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep)."
+        "Learn more about [Vertex AI Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
       ]
     },
     {

notebooks/official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook is written for data analysts and data scientists who have data in BigQuery and want to perform exploratory data analysis to gather insights from that data in an interactive environment.\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery](https://cloud.google.com/bigquery)."
       ]
     },
     {

notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb

@@ -92,7 +92,7 @@
         "\n",
         "This tutorial shows you how to train, evaluate a propensity model in BigQuery ML to predict user retention on a mobile game, based on app measurement data from Google Analytics 4.\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
       ]
     },
     {

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

@@ -87,7 +87,7 @@
         "\n",
         "*Note: This notebook file was developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.*\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
       ]
     },
     {