Merge branch 'main' into instance_schema

* Update build_model_experimentation_lineage_with_prebuild_code.ipynb

* Update build_model_experimentation_lineage_with_prebuild_code.ipynb

* Update build_model_experimentation_lineage_with_prebuild_code.ipynb

* Update build_model_experimentation_lineage_with_prebuild_code.ipynb

* Update build_model_experimentation_lineage_with_prebuild_code.ipynb

Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>

notebooks/community/CODEOWNERS

@@ -33,3 +33,4 @@
 /notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb @fhirschmann
 /notebooks/community/vertex-ai-samples/notebooks/community/model_registry/vertex_ai_model_registry_bqml_custom_model_versioning.ipynb @inardini
 /notebooks/community/vertex-ai-samples/notebooks/community/model_registry/vertex_ai_model_registry_automl_model_versioning.ipynb @inardini
+/notebooks/community/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb @halio-g

notebooks/community/ml_ops/README.md

@@ -14,5 +14,5 @@ The purpose of this set of notebooks and markdown files is to demonstrate Google
 4. [Evaluation](stage4)
 5. [Deployment](stage5)
 6. [Serving](stage6)
-7. Monitoring
+7. Monitoring(stage7)
 8. Continuous Training

notebooks/community/ml_ops/setup.md

@@ -0,0 +1,43 @@
+## Before you begin
+
+### Set up your Google Cloud project
+
+**The following steps are required, regardless of your notebook environment.**
+
+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 $300 free credit towards your compute/storage costs.
+
+1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).
+
+1. [Enable the Vertex AI, BigQuery, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,bigquery,compute_component,storage_component).
+
+1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).
+
+1. Enter your project ID in the cell below. Then run the cell to make sure the
+Cloud SDK uses the right project for all the commands in this notebook.
+
+**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.
+
+### Set up your local development environment
+
+**If you are using Colab or Vertex AI Workbench Notebooks**, your environment already meets all the requirements to run this notebook. You can skip this step.
+
+**Otherwise**, make sure your environment meets this notebook's requirements. You need the following:
+
+- The Cloud Storage SDK
+- Python 3
+- virtualenv
+- Jupyter notebook running in a virtual environment with Python 3
+
+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:
+
+1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).
+
+2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).
+
+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.
+
+4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.
+
+5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.
+
+6. Open this notebook in the Jupyter Notebook Dashboard.

notebooks/community/ml_ops/setup.py

@@ -0,0 +1,112 @@
+import os
+import sys
+import argparse
+import subprocess
+import random
+import string
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--bucket', dest='bucket_required', action='store_true', 
+                    default=False, help='Bucket required')
+parser.add_argument('--email', dest='email_required', action='store_true', 
+                    default=False, help='Email required')
+parser.add_argument('--sa', dest='sa_required', action='store_true', 
+                    default=False, help='Service account required')
+parser.add_argument('--packages', dest='extra_packages',
+                    default='', type=str, help='additional required packages')
+args = parser.parse_args()
+
+extra_pkgs = args.extra_packages
+
+
+# Installation
+
+
+# The Vertex AI Workbench Notebook product has specific requirements
+IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
+IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
+    "/opt/deeplearning/metadata/env_version"
+)
+IS_COLAB = "google.colab" in sys.modules
+
+# Vertex AI Notebook requires dependencies to be installed with '--user'
+USER_FLAG = ""
+if IS_WORKBENCH_NOTEBOOK:
+    USER_FLAG = "--user"
+
+# not used
+'''
+print("Installing packages")
+os.system(f"pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform {args.extra_packages}")
+print("Done installation")
+'''
+
+# Authenticate
+if IS_COLAB:
+    from google.colab import auth as google_auth
+
+    google_auth.authenticate_user()
+
+
+# project ID
+if IS_WORKBENCH_NOTEBOOK:
+    shell_output = subprocess.check_output("gcloud config list --format 'value(core.project)' 2>/dev/null", shell=True)
+    PROJECT_ID = shell_output[0:-1].decode('utf-8')
+    print("PROJECT ID: ", PROJECT_ID)
+else:
+    PROJECT_ID = input("Enter PROJECT_ID: ")
+    os.system(f"gcloud config set project {PROJECT_ID}")
+
+# email
+if args.email_required:
+    shell_output = subprocess.check_output("gcloud config list --format 'value(core.account)' 2>/dev/null", shell=True)
+    EMAIL_ADDR = shell_output[0:-1].decode('utf-8')
+    if EMAIL_ADDR == '':
+            EMAIL_ADDR = input("Enter Email Address: ")
+    print("EMAIL_ADDR: ", EMAIL_ADDR)
+
+# region
+shell_output = subprocess.check_output("gcloud config list --format 'value(ai.region)'", shell=True)
+REGION = shell_output[0:-1].decode('utf-8')
+if REGION == '':
+    REGION = input("Enter REGION: ")
+print("REGION: ", REGION)
+
+# multi-region
+MULTI_REGION = REGION.split('-')[0]
+
+    
+# UUID
+# Generate a uuid of a specifed length(default=8)
+def generate_uuid(length: int = 8) -> str:
+    return "".join(random.choices(string.ascii_lowercase + string.digits, k=length))
+
+
+UUID = generate_uuid()
+print("UUID", UUID)
+    
+# Bucket
+if args.bucket_required:
+    BUCKET_NAME = PROJECT_ID + "aip-" + UUID
+    BUCKET_URI = f"gs://{BUCKET_NAME}"
+    os.system(f"gsutil mb -l {REGION} {BUCKET_URI}")
+    print("BUCKET_URI", BUCKET_URI)
+    
+    
+# Project Number
+if args.sa_required:
+    if IS_WORKBENCH_NOTEBOOK:
+        shell_output = subprocess.check_output("gcloud auth list 2>/dev/null", shell=True)
+        SERVICE_ACCOUNT = shell_output[:-1].decode('utf-8').split('\n')[2].strip()
+        PROJECT_NUMBER = SERVICE_ACCOUNT.split('-')[0]
+    else:
+        shell_output = subprocess.check_output(f"gcloud projects describe {PROJECT_ID}", shell=True)
+        try:
+            PROJECT_NUMBER = shell_output[:-1].decode('utf-8').split('\n')[7].split(':')[-1].strip().replace("'", "")
+            SERVICE_ACCOUNT = f"{PROJECT_NUMBER}-compute@developer.gserviceaccount.com"
+        except:
+            PROJECT_NUMBER = input("Enter project number: ")
+            SERVICE_ACCOUNT = f"{PROJECT_NUMBER}-compute@developer.gserviceaccount.com"
+    
+    print("SERVICE_ACCOUNT", SERVICE_ACCOUNT)
+    print("PROJECT_NUMBER", PROJECT_NUMBER)

notebooks/community/ml_ops/stage1/get_started_bq_datasets.ipynb

@@ -120,7 +120,7 @@
         "    - XGBoost model training:\n",
         "      - Use BigQuery ML built-in XGBoost training.\n",
         "      - Alternatively, create a DMatrix generator from CSV files extracted from BigQuery table.\n",
-        "    - Pytorch model training:\n",
+        "    - PyTorch model training:\n",
         "        - Extract the BigQuery to a pandas dataframe.\n",
         "        - Preprocess the data in the dataframe.\n",
         "        - Create a DataLoader generator from the pandas dataframe.\n",
@@ -191,13 +191,8 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "# Install the packages\n",
-        "! pip3 install --upgrade pyarrow $USER_FLAG -q\n",
-        "! pip3 install --upgrade google-cloud-bigquery $USER_FLAG -q\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q\n",
-        "! pip3 install -U xgboost $USER_FLAG -q\n",
-        "! pip3 install -U tensorflow $USER_FLAG -q\n",
-        "! pip3 install -U tensorflow-io==0.18 $USER_FLAG -q"
+        "extra_pkgs = \"tensorflow tensorflow-io==0.18 pyarrow xgboost google-cloud-bigquery\"\n",
+        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform $extra_pkgs"
       ]
     },
     {
@@ -219,9 +214,9 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "import sys\n",
         "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
+        "if \"google.colab\" in sys.modules:\n",
         "    # Automatically restart kernel after installs\n",
         "    import IPython\n",
         "\n",
@@ -232,274 +227,42 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "84cd83853240"
+        "id": "fc8fb52b5cca"
       },
       "source": [
-        "## Before you begin\n",
+        "### Common setup\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 $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "1. [Enable the Vertex AI, BigQuery, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,bigquery,compute_component,storage_component).\n",
-        "\n",
-        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
-        "\n",
-        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "project_id"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
+        "Now, execute the common setup for the notebook tutorials."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "001a0fcd5d78"
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+        "# Common code setup for notebook tutorials\n",
+        "\n",
+        "! wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/setup.py -O setup.py\n",
+        "\n",
+        "%run setup.py --bucket"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "autoset_project_id"
+        "id": "d809f07a8935"
       },
       "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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "# Other Common setup instructions for notebook tutorials\n",
         "\n",
-        "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",
+        "! wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/setup.md -O setup.md\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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\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\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "77c385f0db59"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
-        "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
-        "\n",
-        "**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",
-        "1. **Click Create service account**.\n",
-        "\n",
-        "2. In the **Service account name** field, enter a name, and click **Create**.\n",
-        "\n",
-        "3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
-        "\n",
-        "5. 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": "535223fa4b84"
-      },
-      "outputs": [],
-      "source": [
-        "# 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 Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\n",
-        "    if \"google.colab\" in sys.modules:\n",
-        "        IS_COLAB = True\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 ''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:custom"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "When you create a dataset resource using the Vertex SDK, you can provide a Cloud Storage bucket that contains the data. Vertex AI creates the dataset resource from the data. In this tutorial, Vertex AI also creates a dataset resource from your data in the Cloud Storage bucket.\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."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "outputs": [],
-      "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
-      ]
-    },
-    {
-      "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": "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_URI"
+        "%load setup.md"
       ]
     },
     {
@@ -620,7 +383,7 @@
       "outputs": [],
       "source": [
         "dataset = aiplatform.TabularDataset.create(\n",
-        "    display_name=\"NOAA historical weather data\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"NOAA historical weather data\" + \"_\" + UUID,\n",
         "    bq_source=[IMPORT_FILE],\n",
         "    labels={\"user_metadata\": BUCKET_URI[5:]},\n",
         ")\n",
@@ -695,7 +458,7 @@
         "gcs_source = IMPORT_FILES\n",
         "\n",
         "dataset = aiplatform.TabularDataset.create(\n",
-        "    display_name=\"NOAA historical weather data\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"NOAA historical weather data\" + \"_\" + UUID,\n",
         "    gcs_source=gcs_source,\n",
         "    labels={\"user_metadata\": BUCKET_URI[5:]},\n",
         ")\n",
@@ -737,10 +500,10 @@
         "    or BQ_MY_DATASET is None\n",
         "    or BQ_MY_DATASET == \"[your-dataset-name]\"\n",
         "):\n",
-        "    BQ_MY_DATASET = \"mlops_dataset_\" + TIMESTAMP\n",
+        "    BQ_MY_DATASET = \"mlops_dataset_\" + UUID\n",
         "\n",
         "if BQ_MY_TABLE == \"\" or BQ_MY_TABLE is None or BQ_MY_TABLE == \"[your-view-name]\":\n",
-        "    BQ_MY_TABLE = \"mlops_view_\" + TIMESTAMP"
+        "    BQ_MY_TABLE = \"mlops_view_\" + UUID"
       ]
     },
     {

notebooks/community/ml_ops/stage1/get_started_dataflow.ipynb

@@ -44,7 +44,7 @@
         "        </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_dataflow.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/community/ml_ops/stage1/get_started_dataflow.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",
@@ -186,13 +186,9 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "! pip3 install -U tensorflow==2.5 $USER_FLAG -q\n",
-        "! pip3 install -U tensorflow-data-validation==1.2 $USER_FLAG -q\n",
-        "! pip3 install -U tensorflow-transform==1.2 $USER_FLAG -q\n",
-        "! pip3 install -U tensorflow-io==0.18 $USER_FLAG -q\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q\n",
-        "! pip3 install --upgrade google-cloud-bigquery $USER_FLAG -q\n",
-        "! pip3 install --upgrade apache-beam[gcp] $USER_FLAG -q"
+        "extra_pkgs = \"tensorflow==2.5 tensorflow-data-validation==1.2 tensorflow-transform==1.2 \\\n",
+        "              tensorflow-io==0.18 pyarrow pandas apache-beam[gcp] google-cloud-bigquery\"\n",
+        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform $extra_pkgs"
       ]
     },
     {
@@ -214,9 +210,9 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
+        "import sys\n",
         "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
+        "if \"google.colab\" in sys.modules:\n",
         "    # Automatically restart kernel after installs\n",
         "    import IPython\n",
         "\n",
@@ -227,279 +223,42 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "84cd83853240"
+        "id": "fc8fb52b5cca"
       },
       "source": [
-        "## Before you begin\n",
+        "### Common setup\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 $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "1. [Enable the Vertex AI, BigQuery, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,bigquery,compute_component,storage_component).\n",
-        "\n",
-        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
-        "\n",
-        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "project_id"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
+        "Now, execute the common setup for the notebook tutorials."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "set_project_id"
+        "id": "001a0fcd5d78"
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+        "# Common code setup for notebook tutorials\n",
+        "\n",
+        "! wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/setup.py -O setup.py\n",
+        "\n",
+        "%run setup.py --bucket"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "autoset_project_id"
+        "id": "d809f07a8935"
       },
       "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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "source": [
-        "#### Region\n",
+        "# Other Common setup instructions for notebook tutorials\n",
         "\n",
-        "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",
+        "! wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/setup.md -O setup.md\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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\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\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "77c385f0db59"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
-        "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
-        "\n",
-        "**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",
-        "1. **Click Create service account**.\n",
-        "\n",
-        "2. In the **Service account name** field, enter a name, and click **Create**.\n",
-        "\n",
-        "3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
-        "\n",
-        "5. 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": "535223fa4b84"
-      },
-      "outputs": [],
-      "source": [
-        "# 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 Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\n",
-        "    if \"google.colab\" in sys.modules:\n",
-        "        IS_COLAB = True\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 ''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:custom"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "When you submit a custom training job using the Vertex SDK, you upload a Python package\n",
-        "containing your training code to a Cloud Storage bucket. Vertex AI runs\n",
-        "the code from this package. In this tutorial, Vertex AI also saves the\n",
-        "trained model that results from your job in the same bucket. You can then\n",
-        "create an `Endpoint` resource based on this output in order to serve\n",
-        "online predictions.\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."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "outputs": [],
-      "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
-      ]
-    },
-    {
-      "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": "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_URI"
+        "%load setup.md "
       ]
     },
     {
@@ -1319,7 +1078,7 @@
       },
       "outputs": [],
       "source": [
-        "delete_storage = True\n",
+        "delete_storage = False\n",
         "\n",
         "if delete_storage or os.getenv(\"IS_TESTING\"):\n",
         "    if \"BUCKET_URI\" in globals():\n",

notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb

@@ -33,12 +33,12 @@
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb\">\n",
         "     <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb\">\n",
         "<img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",

notebooks/community/ml_ops/stage2/get_started_bqml_training.ipynb

@@ -73,7 +73,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `BigQueryML` (BQML) for training with `Vertex AI`.\n",
+        "In this tutorial, you learn how to use `BigQueryML` for training with `Vertex AI`.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
@@ -84,12 +84,12 @@
         "The steps performed include:\n",
         "\n",
         "- Create a local BigQuery table in your project\n",
-        "- Train a BQML model\n",
-        "- Evaluate the BQML model\n",
-        "- Export the BQML model as a cloud model\n",
+        "- Train a BigQuery ML model\n",
+        "- Evaluate the BigQuery ML model\n",
+        "- Export the BigQuery ML model as a cloud model\n",
         "- Upload the exported model as a `Vertex AI Model` resource\n",
-        "- Hyperparameter tune a BQML model with `Vertex AI Vizier`\n",
-        "- Automatically register a BQML model to `Vertex AI Model Registry`"
+        "- Hyperparameter tune a BigQuery ML model with `Vertex AI Vizier`\n",
+        "- Automatically register a BigQuery ML model to `Vertex AI Model Registry`"
       ]
     },
     {
@@ -749,9 +749,9 @@
         "id": "bqml_create_model"
       },
       "source": [
-        "### Train BQML model\n",
+        "### Train BigQuery ML model\n",
         "\n",
-        "Next, you create and train a BQML tabular classification model from the public dataset penguins and store the model in your project using the `CREATE MODEL` statement. The model configuration is specified in the `OPTIONS` statement as follows:\n",
+        "Next, you create and train a BigQuery ML tabular classification model from the public dataset penguins and store the model in your project using the `CREATE MODEL` statement. The model configuration is specified in the `OPTIONS` statement as follows:\n",
         "\n",
         "- `model_type`: The type and archictecture of tabular model to train, e.g., DNN classification.\n",
         "- `labels`: The column which are the labels.\n",
@@ -800,9 +800,9 @@
         "id": "bqml_eval_model"
       },
       "source": [
-        "### Evaluate the trained BQML model\n",
+        "### Evaluate the trained BigQuery ML model\n",
         "\n",
-        "Next, retrieve the model evaluation for the trained BQML model.\n",
+        "Next, retrieve the model evaluation for the trained BigQuery ML model.\n",
         "\n",
         "Learn more about [The ML.EVALUATE function](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate)."
       ]
@@ -833,9 +833,9 @@
         "id": "bqml_export_model"
       },
       "source": [
-        "### Export the model from BQML\n",
+        "### Export the model from BigQuery ML\n",
         "\n",
-        "The model you trained in BQML is a TensorFlow model. Next, you export the TensorFlow model artifacts in TF.SavedModel format."
+        "The model you trained in BigQuery ML is a TensorFlow model. Next, you export the TensorFlow model artifacts in TF.SavedModel format."
       ]
     },
     {
@@ -1028,9 +1028,9 @@
         "id": "bqml_create_model:vizier"
       },
       "source": [
-        "### Hyperparameter Tune and train a BQML model\n",
+        "### Hyperparameter Tune and train a BigQuery ML model\n",
         "\n",
-        "Next, you train a BQML tabular classification model with hyperparameter tuning using the `Vertex AI Vizier` service. The hyperparameter settings are specified in the `OPTIONS` statement as follows:\n",
+        "Next, you train a BigQuery ML tabular classification model with hyperparameter tuning using the `Vertex AI Vizier` service. The hyperparameter settings are specified in the `OPTIONS` statement as follows:\n",
         "\n",
         "- `HPARAM_TUNING_ALGORITHM`: The algorithm for selecting the next trial parameters.\n",
         "- `num_trials`: The number of trials.\n",
@@ -1083,9 +1083,9 @@
         "id": "bqml_eval_model"
       },
       "source": [
-        "### Evaluate the BQML trained model\n",
+        "### Evaluate the BigQuery ML trained model\n",
         "\n",
-        "Next, retrieve the model evaluation results for the trained BQML model.\n",
+        "Next, retrieve the model evaluation results for the trained BigQuery ML model.\n",
         "\n",
         "Learn more about [The ML.EVALUATE function](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate)."
       ]
@@ -1142,9 +1142,9 @@
         "id": "bqml_create_model:xai"
       },
       "source": [
-        "### Train a BQML model with Explainability\n",
+        "### Train a BigQuery ML model with Explainability\n",
         "\n",
-        "Next, you train the same BQML model, but this time you enable Vertex AI Explainability on the model predictions by adding the option:\n",
+        "Next, you train the same BigQuery ML model, but this time you enable Vertex AI Explainability on the model predictions by adding the option:\n",
         "\n",
         "- `ENABLE_GLOBAL_EXPLAIN`"
       ]

notebooks/community/ml_ops/stage2/get_started_vertex_experiments.ipynb

@@ -87,7 +87,7 @@
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
         "- `Vertex AI Experiments`\n",
-        "- `Vertex AI ML Metadata`\n",
+        "- `Vertex ML Metadata`\n",
         "- `Vertex AI Training`\n",
         "\n",
         "The steps performed include:\n",

notebooks/community/ml_ops/stage2/get_started_vertex_tensorboard.ipynb

@@ -44,7 +44,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/notebook_template.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/community/ml_ops/stage2/get_started_vertex_tensorboard.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/community/ml_ops/stage2/get_started_vertex_training_lightgbm.ipynb

@@ -48,7 +48,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_lightgbm.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_lightgbm.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",

notebooks/community/ml_ops/stage2/get_started_vertex_training_pytorch.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# E2E ML on GCP: MLOps stage 2 : experimentation: get started with Vertex AI Training for Pytorch\n",
+        "# E2E ML on GCP: MLOps stage 2 : experimentation: get started with Vertex AI Training for PyTorch\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -44,7 +44,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/notebook_template.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/community/ml_ops/stage2/get_started_vertex_training_pytorch.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",
@@ -62,7 +62,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with Vertex AI Training for Pytorch."
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with Vertex AI Training for PyTorch."
       ]
     },
     {
@@ -73,7 +73,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Training` for training a Pytorch custom model.\n",
+        "In this tutorial, you learn how to use `Vertex AI Training` for training a PyTorch custom model.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
@@ -97,7 +97,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [CIFAR10 dataset](https://pytorch.org/vision/stable/datasets.html#cifar) from [Pytorch Datasets](https://pytorch.org/vision/stable/datasets.html). The version of the dataset is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
+        "The dataset used for this tutorial is the [CIFAR10 dataset](https://pytorch.org/vision/stable/datasets.html#cifar) from [PyTorch Datasets](https://pytorch.org/vision/stable/datasets.html). The version of the dataset is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
       ]
     },
     {
@@ -672,17 +672,17 @@
         "id": "pytorch_intro"
       },
       "source": [
-        "## Introduction to Pytorch training\n",
+        "## Introduction to PyTorch training\n",
         "\n",
-        "The Pytorch package supports both single node and distributed model training.\n",
+        "The PyTorch package supports both single node and distributed model training.\n",
         "\n",
-        "Once you have trained a Pytorch model, you will want to save it at a Cloud Storage location, so it can subsequently be uploaded to a `Vertex AI Model` resource.\n",
-        "The Pytorch package does not have support to save the model to a Cloud Storage location. Instead, you will do the following steps to save to a Cloud Storage location.\n",
+        "Once you have trained a PyTorch model, you will want to save it at a Cloud Storage location, so it can subsequently be uploaded to a `Vertex AI Model` resource.\n",
+        "The PyTorch package does not have support to save the model to a Cloud Storage location. Instead, you will do the following steps to save to a Cloud Storage location.\n",
         "\n",
         "1. Save the in-memory model to the local filesystem (e.g., model.pth).\n",
         "2. Use gsutil to copy the local copy to the specified Cloud Storage location.\n",
         "\n",
-        "*Note*: You can do hyperparameter tuning with a Pytorch model."
+        "*Note*: You can do hyperparameter tuning with a PyTorch model."
       ]
     },
     {
@@ -1069,9 +1069,9 @@
         "id": "docker_write,prediction,pytorch"
       },
       "source": [
-        "### Make Pytorch container for prediction\n",
+        "### Make PyTorch container for prediction\n",
         "\n",
-        "Currently, Vertex AI does not have a predefined container for making predictions with a deployed Pytorch model. No problem, you can assemble your own custom container. Typically, one would base the container on the `Torch Server`. For demonstration purpose, you build a placeholder container (not complete) that includes the latest `Torch Server` image, and push it to the `Container Registry`."
+        "Currently, Vertex AI does not have a predefined container for making predictions with a deployed PyTorch model. No problem, you can assemble your own custom container. Typically, one would base the container on the `Torch Server`. For demonstration purpose, you build a placeholder container (not complete) that includes the latest `Torch Server` image, and push it to the `Container Registry`."
       ]
     },
     {

notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb

@@ -43,7 +43,7 @@
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
-        "    <a href=\"https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.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",
@@ -109,7 +109,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is [California Housing Dataset](https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html). The data contains information from the 1990 California census. The data set is publicly available from Google Cloud Storage at `gs://cloud-samples-data/ai-platform-unified/datasets/tabular/california-housing-tabular-regression.csv`. The dataset is used to train a Random Forest regressor to predict a median housing price, given a longitude and lattitude along with data from the corresponding census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).\n"
+        "The dataset used for this tutorial is [California Housing Dataset](https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html). The data contains information from the 1990 California census. The data set is publicly available from Cloud Storage at `gs://cloud-samples-data/ai-platform-unified/datasets/tabular/california-housing-tabular-regression.csv`. The dataset is used to train a Random Forest regressor to predict a median housing price, given a longitude and lattitude along with data from the corresponding census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).\n"
       ]
     },
     {

notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb

@@ -44,7 +44,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.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/community/ml_ops/stage2/get_started_vertex_training_sklearn.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/community/ml_ops/stage2/get_started_vertex_training_xgboost.ipynb

@@ -297,25 +297,32 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "timestamp"
+        "id": "06571eb4063b"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "JYtXOocrox9Q"
+        "id": "4e166d927e36"
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\n",
+        "# Generate a uuid of a specifed length(default=8)\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -362,12 +369,11 @@
         "import sys\n",
         "\n",
         "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
         "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
         "    \"DL_ANACONDA_HOME\"\n",
         "):\n",
         "    if \"google.colab\" in sys.modules:\n",
-        "        IS_COLAB = True\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
         "        google_auth.authenticate_user()\n",
@@ -402,7 +408,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -413,8 +420,9 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
       ]
     },
     {
@@ -749,6 +757,7 @@
         "import hypertune\n",
         "import argparse\n",
         "import logging\n",
+        "import numpy as np\n",
         "\n",
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.metrics import accuracy_score\n",
@@ -790,16 +799,23 @@
         "def train_model(dtrain):\n",
         "    logging.info(\"Start training ...\")\n",
         "    # Train XGBoost model\n",
-        "    model = xgb.train({}, dtrain, num_boost_round=args.boost_rounds)\n",
+        "    params = {\n",
+        "        'objective': 'multi:softprob',\n",
+        "        'num_class': 3\n",
+        "    }\n",
+        "    model = xgb.train(params, dtrain, num_boost_round=args.boost_rounds)\n",
         "    logging.info(\"Training completed\")\n",
         "    return model\n",
         "\n",
         "def evaluate_model(model, test_data, test_labels):\n",
         "    dtest = xgb.DMatrix(test_data)\n",
         "    pred = model.predict(dtest)\n",
-        "    predictions = [round(value) for value in pred]\n",
+        "    predictions = [np.around(value) for value in pred]\n",
         "    # evaluate predictions\n",
-        "    accuracy = accuracy_score(test_labels, predictions)\n",
+        "    try:\n",
+        "        accuracy = accuracy_score(test_labels, predictions)\n",
+        "    except:\n",
+        "        accuracy = 0.0\n",
         "    logging.info(f\"Evaluation completed with model accuracy: {accuracy}\")\n",
         "\n",
         "    # report metric for hyperparameter tuning\n",
@@ -893,7 +909,7 @@
       },
       "outputs": [],
       "source": [
-        "DISPLAY_NAME = \"iris_\" + TIMESTAMP\n",
+        "DISPLAY_NAME = \"iris_\" + UUID\n",
         "\n",
         "job = aip.CustomPythonPackageTrainingJob(\n",
         "    display_name=DISPLAY_NAME,\n",
@@ -932,7 +948,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, TIMESTAMP)\n",
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, UUID)\n",
         "DATASET_DIR = \"gs://cloud-samples-data/ai-platform/iris\"\n",
         "\n",
         "ROUNDS = 20\n",
@@ -983,7 +999,7 @@
       "source": [
         "if TRAIN_GPU:\n",
         "    model = job.run(\n",
-        "        model_display_name=\"iris_\" + TIMESTAMP,\n",
+        "        model_display_name=\"iris_\" + UUID,\n",
         "        args=CMDARGS,\n",
         "        replica_count=1,\n",
         "        machine_type=TRAIN_COMPUTE,\n",
@@ -994,7 +1010,7 @@
         "    )\n",
         "else:\n",
         "    model = job.run(\n",
-        "        model_display_name=\"iris_\" + TIMESTAMP,\n",
+        "        model_display_name=\"iris_\" + UUID,\n",
         "        args=CMDARGS,\n",
         "        replica_count=1,\n",
         "        machine_type=TRAIN_COMPUTE,\n",
@@ -1095,7 +1111,7 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
+        "delete_bucket = True\n",
         "\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil rm -r $BUCKET_URI"

notebooks/community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb

@@ -116,7 +116,7 @@
         "\n",
         "The dataset used for this tutorial is the [Patent PDF Samples with Extracted Structured Data](https://console.cloud.google.com/marketplace/product/global-patents/labeled-patents) from Google Public Data Sets. \n",
         "\n",
-        "This dataset includes data extracted from over 300 patent documents issued in the US and EU. The dataset includes links to Google Cloud Storage blobs for the first page of each patent, in addition to a number of extracted entities. \n",
+        "This dataset includes data extracted from over 300 patent documents issued in the US and EU. The dataset includes links to Cloud Storage blobs for the first page of each patent, in addition to a number of extracted entities. \n",
         "\n",
         "The data is published as a [public dataset](https://cloud.google.com/bigquery/public-data) on `BigQuery`."
       ]

notebooks/community/ml_ops/stage3/get_started_with_airflow_and_vertex_pipelines.ipynb

@@ -568,7 +568,6 @@
       },
       "outputs": [],
       "source": [
-        "import kfp\n",
         "from google.cloud import aiplatform\n",
         "from kfp import dsl\n",
         "from kfp.v2 import compiler\n",
@@ -1000,11 +999,7 @@
         "    from google.auth.transport.requests import Request\n",
         "    from google.oauth2 import id_token\n",
         "\n",
-        "    IAM_SCOPE = \"https://www.googleapis.com/auth/iam\"\n",
-        "    OAUTH_TOKEN_URI = \"https://www.googleapis.com/oauth2/v4/token\"\n",
-        "\n",
         "    data = '{\"replace_microseconds\":\"false\"}'\n",
-        "    context = None\n",
         "\n",
         "    \"\"\"Makes a POST request to the Composer DAG Trigger API\n",
         "\n",

notebooks/community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.ipynb

@@ -44,7 +44,7 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.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/community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.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/community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb

@@ -39,9 +39,9 @@
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb\">\n",
         "<img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> \n",
-        "        Colab logo Run in Colab\n",
+        "        Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",

notebooks/community/ml_ops/stage3/get_started_with_rapid_prototyping_bqml_automl.ipynb

@@ -60,7 +60,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BQML, do an evaluation comparison, for a baseline, before progressing to a custom model.\n",
+        "This tutorial demonstrates how to use Vertex AI Pipelines to rapid prototype a model using both AutoML and BigQuery ML, do an evaluation comparison, for a baseline, before progressing to a custom model.\n",
         "\n",
         "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />"
       ]
@@ -834,7 +834,7 @@
       "source": [
         "### Create component: Split the dataset into train, test and eval\n",
         "\n",
-        "For this pipeline, you set aside a portion of the dataset for test evaluation. While both AutoML and BQML will automatically split then datasets, in this example you will explicitly split the datasets into:\n",
+        "For this pipeline, you set aside a portion of the dataset for test evaluation. While both AutoML and BigQuery ML will automatically split then datasets, in this example you will explicitly split the datasets into:\n",
         "\n",
         "- TRAIN\n",
         "- EVALUATE\n",
@@ -1000,11 +1000,11 @@
         "- Construct the CREATE MODEL query using a static Python function `_create_model_query()`, which runs in the context of the pipeline.\n",
         "- Call the prebuilt component `BigQueryCreateModelOp`, with the constructed query, to train the BigQuery ML model.\n",
         "\n",
-        "For this tutorial, you use a simple linear regression model on BQML. \n",
+        "For this tutorial, you use a simple linear regression model on BigQuery ML. \n",
         "\n",
-        "For a full list of models supported by BQML, look here: [End-to-end user journey for each model](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-e2e-journey).\n",
+        "For a full list of models supported by BigQuery ML, look here: [End-to-end user journey for each model](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-e2e-journey).\n",
         "\n",
-        "As pointed out before, BQML and AutoML use different split terminologies, so we do an adaptation of the <i>split_col</i> column directly on the SELECT portion of the CREATE model query:\n",
+        "As pointed out before, BigQuery ML and AutoML use different split terminologies, so we do an adaptation of the <i>split_col</i> column directly on the SELECT portion of the CREATE model query:\n",
         "\n",
         "> When the value of DATA_SPLIT_METHOD is 'CUSTOM', the corresponding column should be of type BOOL. The rows with TRUE or NULL values are used as evaluation data. Rows with FALSE values are used as training data."
       ]

notebooks/community/ml_ops/stage3/mlops_formalization.ipynb

@@ -658,8 +658,6 @@
       },
       "outputs": [],
       "source": [
-        "from typing import NamedTuple\n",
-        "\n",
         "from kfp import dsl\n",
         "from kfp.v2 import compiler\n",
         "from kfp.v2.dsl import component"
@@ -1866,7 +1864,7 @@
         "        exported_tfrec_prefix=exported_tfrec_prefix,\n",
         "    ).after(dataflow_wait_op)\n",
         "\n",
-        "    dataset_op = gcc_aip.TabularDatasetCreateOp(\n",
+        "    _ = gcc_aip.TabularDatasetCreateOp(\n",
         "        project=project,\n",
         "        display_name=display_name,\n",
         "        bq_source=bq_table,\n",
@@ -2285,7 +2283,7 @@
         "        },\n",
         "    ).after(model_build_op)\n",
         "\n",
-        "    model_upload = ModelUploadOp(\n",
+        "    _ = ModelUploadOp(\n",
         "        project=project,\n",
         "        display_name=display_name,\n",
         "        unmanaged_container_model=import_unmanaged_model_task.outputs[\"artifact\"],\n",
@@ -3205,7 +3203,7 @@
         "\n",
         "    with dsl.Condition(warmup == \"True\", name=\"warmup-model\"):\n",
         "\n",
-        "        warmup_op = gcc_aip.CustomPythonPackageTrainingJobRunOp(\n",
+        "        _ = gcc_aip.CustomPythonPackageTrainingJobRunOp(\n",
         "            project=project,\n",
         "            display_name=display_name,\n",
         "            # Warmup Training\n",
@@ -3249,7 +3247,7 @@
         "            display_name=display_name,\n",
         "        ).after(training_op)\n",
         "\n",
-        "        deploy_op = ModelDeployOp(\n",
+        "        _ = ModelDeployOp(\n",
         "            model=training_op.outputs[\"model\"],\n",
         "            endpoint=endpoint_op.outputs[\"endpoint\"],\n",
         "            dedicated_resources_min_replica_count=1,\n",

notebooks/community/ml_ops/stage4/get_started_with_vertex_ml_metadata.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# E2E ML on GCP: MLOps stage 4 : formalization: get started with Vertex AI ML Metadata\n",
+        "# E2E ML on GCP: MLOps stage 4 : formalization: get started with Vertex ML Metadata\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -62,7 +62,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 4 : formalization: get started with Vertex AI ML Metadata."
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 4 : formalization: get started with Vertex ML Metadata."
       ]
     },
     {
@@ -73,11 +73,11 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI ML Metadata`.\n",
+        "In this tutorial, you learn how to use `Vertex ML Metadata`.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI ML Metadata`\n",
+        "- `Vertex ML Metadata`\n",
         "- `Vertex AI Pipelines`\n",
         "\n",
         "The steps performed include:\n",
@@ -657,7 +657,7 @@
       "source": [
         "## Introduction to Vertex AI Metadata\n",
         "\n",
-        "The `Vertex AI ML Metadata` service provides you with the ability to record, and subsequently search and analyze, the artifacts and corresponding metadata produced by your ML workflows. For example, during experimentation one might desire to record the location of the model artifacts, as artifacts, and the training hyperparameters and evaluation metrics as the corresponding metadata.\n",
+        "The `Vertex ML Metadata` service provides you with the ability to record, and subsequently search and analyze, the artifacts and corresponding metadata produced by your ML workflows. For example, during experimentation one might desire to record the location of the model artifacts, as artifacts, and the training hyperparameters and evaluation metrics as the corresponding metadata.\n",
         "\n",
         "The service supports recording ML metadata both manually and automatically, with the later occurring when you use Vertex AI Pipelines.\n",
         "\n",
@@ -675,9 +675,9 @@
         "\n",
         "### ML artifact lineage\n",
         "\n",
-        "Vertex AI ML Metadata provides the ability to understand changes in the performance of your machine ML system, and analyze the metadata produced by your ML workflow and the lineage of its artifacts. An artifact's lineage includes all the factors that contributed to its creation, as well as artifacts and metadata that descend from this artifact.\n",
+        "Vertex ML Metadata provides the ability to understand changes in the performance of your machine ML system, and analyze the metadata produced by your ML workflow and the lineage of its artifacts. An artifact's lineage includes all the factors that contributed to its creation, as well as artifacts and metadata that descend from this artifact.\n",
         "\n",
-        "Learn more about [Introduction to Vertex AI ML Metadata ](https://cloud.google.com/vertex-ai/docs/ml-metadata/introduction)"
+        "Learn more about [Introduction to Vertex ML Metadata ](https://cloud.google.com/vertex-ai/docs/ml-metadata/introduction)"
       ]
     },
     {

notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb

@@ -1054,36 +1054,17 @@
       ]
     }
   ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "get_started_with_tf_serving_function.ipynb",
-   "toc_visible": true
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "get_started_with_tf_serving_function.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
   },
-  "environment": {
-   "kernel": "python3",
-   "name": "tf2-gpu.2-6.m91",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m91"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

notebooks/community/ml_ops/stage6/get_started_with_xai_and_custom_server.ipynb

@@ -85,7 +85,7 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Locally train a Pytorch tabular classifier.\n",
+        "- Locally train a PyTorch tabular classifier.\n",
         "- Locally test the trained model.\n",
         "- Build a HTTP server using FastAPI.\n",
         "- Create a custom serving container with the trained model and FastAPI server.\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Custom_Predict_SDK_Integration.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions. This is currently a **preview** feature. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).\n",
+        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions.\n",
         "\n",
         "\n",
         "\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Custom_Predict_and_Handler_SDK_Integration.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions. This is currently a **preview** feature. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).\n",
+        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions.\n",
         "\n",
         "\n",
         "\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Custom_Preprocess.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions. This is currently a **preview** feature. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).\n",
+        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a scikit-learn model on Vertex AI Predictions.\n",
         "\n",
         "\n",
         "\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Pytorch_Custom_Predict.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a PyTorch model on Vertex AI Predictions. This is currently a **preview** feature. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).\n",
+        "This tutorial demonstrates how to use Vertex AI SDK to build a custom container that uses the Custom Prediction Routine model server to serve a PyTorch model on Vertex AI Predictions.\n",
         "\n",
         "\n",
         "\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Triton_PyTorch_Local_Prediction.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI SDK to locally test [NVIDIA Triton inference server](https://developer.nvidia.com/nvidia-triton-inference-server) to serve a PyTorch model and deploy it to Vertex AI Predictions. This is currently a **preview** feature. Pre-GA products and features might have limited support, and changes to pre-GA products and features might not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages).\n",
+        "This tutorial demonstrates how to use Vertex AI SDK to locally test [NVIDIA Triton inference server](https://developer.nvidia.com/nvidia-triton-inference-server) to serve a PyTorch model and deploy it to Vertex AI Predictions.\n",
         "\n",
         "\n",
         "### Dataset\n",

notebooks/community/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb

@@ -0,0 +1,619 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7fPc-KWUi2Xd"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2022 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eoXf8TfQoVth"
+      },
+      "source": [
+        "# Convert between Vertex AI Vizier and Open Source Vizier\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/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vizier/conversions_vertex_vizier_and_open_source_vizier.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/community/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb\">\n",
+        "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b397c59391b1"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to migrate code between [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier/overview) and [Open Source(OSS) Vizier](https://oss-vizier.readthedocs.io/). OSS Vizier is a Python-based service for blackbox optimization and research. It allows you to setup an OSS Vizier Server that can host blackbox optimization algorithms for tuning objective functions and defining abstractions and utilities for implementing new optimization algorithms.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AksIKBzZ-nre"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to use `Vertex AI Vizier` to optimize a multi-objective study and convert the code to OSS Vizier.\n",
+        "\n",
+        "The goal is to __`minimize`__ the objective metric:\n",
+        "   ```\n",
+        "   y1 = r*sin(theta)\n",
+        "   ```\n",
+        "\n",
+        "and simultaneously __`maximize`__ the objective metric:\n",
+        "   ```\n",
+        "   y2 = r*cos(theta)\n",
+        "   ```\n",
+        "\n",
+        "so that you will evaluate over the parameter space:\n",
+        "\n",
+        "   - __`r`__ in [0,1],\n",
+        "\n",
+        "   - __`theta`__ in [0, pi/2]\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iMHz63rPbq6P"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install the packages required for executing this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b6f3dc43494b"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# Configure the environment for the Vertex AI Workbench notebook.\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
+        "\n",
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
+        "USER_FLAG = \"\"\n",
+        "if IS_WORKBENCH_NOTEBOOK:\n",
+        "    USER_FLAG = \"--user\"\n",
+        "\n",
+        "! pip3 install google-vizier==0.0.4\n",
+        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "64d24b4fab2c"
+      },
+      "source": [
+        "### 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": "O8AIwN0abq6U"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"IS_TESTING\"):\n",
+        "    # Restart the kernel after pip installs\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "before_you_begin:nogpu"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### GPU runtime\n",
+        "\n",
+        "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 $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
+        "\n",
+        "3. [Enable the Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
+        "\n",
+        "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n",
+        "\n",
+        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
+        "Cloud SDK uses the right project for all the commands in this notebook.\n",
+        "\n",
+        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "set_project_id"
+      },
+      "outputs": [],
+      "source": [
+        "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 Google Cloud 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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "04933ed28eef"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"[your-region]\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jvNx3KyF2Ou0"
+      },
+      "outputs": [],
+      "source": [
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h0SMyUsC-mzi"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated. Skip this step.\n",
+        "\n",
+        "**If you are using Colab**, run the cell below and follow the instructions\n",
+        "when prompted to authenticate your account via oAuth.\n",
+        "\n",
+        "**Otherwise**, follow these steps:\n",
+        "\n",
+        "1. In the Cloud Console, go to the [**Create service account key**\n",
+        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
+        "\n",
+        "2. Click **Create service account**.\n",
+        "\n",
+        "3. In the **Service account name** field, enter a name, and\n",
+        "   click **Create**.\n",
+        "\n",
+        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
+        "into the filter box, and select\n",
+        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+        "\n",
+        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
+        "local environment.\n",
+        "\n",
+        "6. Enter the path to your service account key as the\n",
+        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iTQY9g4mRo6r"
+      },
+      "outputs": [],
+      "source": [
+        "# If you are running this notebook in Colab, run this cell and follow the\n",
+        "# instructions to authenticate your Google Cloud 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 Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\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 Google Cloud\n",
+        "    # account.\n",
+        "    elif not os.getenv(\"IS_TESTING\"):\n",
+        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dax2zrpTi2Xy"
+      },
+      "source": [
+        "### Import libraries and define constants"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xD60d6Q0i2X0"
+      },
+      "outputs": [],
+      "source": [
+        "import datetime\n",
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWuu4wmki2X3"
+      },
+      "source": [
+        "## Tutorial\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KyEjqIdnad0w"
+      },
+      "source": [
+        "This section defines some parameters to create the study and optimize the objective function.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8HCgeF8had77"
+      },
+      "outputs": [],
+      "source": [
+        "# These will be automatically filled in.\n",
+        "STUDY_DISPLAY_NAME = \"{}_study_{}\".format(\n",
+        "    PROJECT_ID.replace(\"-\", \"\"), datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+        ")\n",
+        "\n",
+        "print(\"REGION: {}\".format(REGION))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8NBduXsEaRKr"
+      },
+      "source": [
+        "### Define the parameters\n",
+        "\n",
+        "The following is a sample study configuration, built as a hierarchical python dictionary. It is already filled out. Run the cell to configure the study.\n",
+        "\n",
+        "__`USE_VERTEX_VIZIER`__: Uses Vertex Vizier SDK to do the optimization if True. Use OSS Vizier otherwise.\n",
+        "\n",
+        "__`SUGGESTION_COUNT`__: The number of suggestions (trials) requested in a single request.\n",
+        "\n",
+        "__`MAX_NUM_ITERATIONS`__: The number of iterations to explore before stopping. It is set to 4 to shorten the time to run the code, so don't expect convergence. For convergence, it would likely need to be about 20.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E1VNJ4YBznhR"
+      },
+      "outputs": [],
+      "source": [
+        "USE_VERTEX_VIZIER = True  # @param {type:\"boolean\"}\n",
+        "\n",
+        "MAX_NUM_ITERATIONS = 4  # @param {type:\"integer\"}\n",
+        "\n",
+        "SUGGESTION_COUNT = 2  # @param {type:\"integer\"}\n",
+        "\n",
+        "OWNER = \"owner\"  # @param {type:\"string\"}\n",
+        "\n",
+        "SERVICE_ENDPOINT = \"127.0.0.1:8888\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4_Yvt-7Z8_re"
+      },
+      "source": [
+        "### Import the package and define `create_study` for different sources\n",
+        "\n",
+        "In Vertex Vizier, `project` and `location` are already specified and `Study.create_or_load` is called to create a study. You need to input the owner of your study and the server address in the format [ip:port]. To bring up the OSS Vizier server, please follow the [instructions](https://oss-vizier.readthedocs.io/) on the OSS Vizier website."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0sAHZn1406VR"
+      },
+      "outputs": [],
+      "source": [
+        "if USE_VERTEX_VIZIER:\n",
+        "    from google.cloud import aiplatform\n",
+        "    from google.cloud.aiplatform.vizier import Study, pyvizier\n",
+        "\n",
+        "    def create_study(project, location, display_name, problem):\n",
+        "        aiplatform.init(project=project, location=location)\n",
+        "        study = Study.create_or_load(display_name=display_name, problem=problem)\n",
+        "        return study\n",
+        "\n",
+        "else:\n",
+        "    from vizier.service import clients, pyvizier\n",
+        "\n",
+        "    def create_study(project, location, display_name, problem):\n",
+        "        clients.environment_variables.service_endpoint = SERVICE_ENDPOINT\n",
+        "        study = clients.Study.from_study_config(\n",
+        "            problem, owner=OWNER, study_id=STUDY_DISPLAY_NAME\n",
+        "        )\n",
+        "        return study"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "guvkcQe_-zQf"
+      },
+      "source": [
+        "### Metric evaluation functions\n",
+        "\n",
+        "Next, define some functions to evaluate the two objective metrics.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fjfk5_c900Oz"
+      },
+      "outputs": [],
+      "source": [
+        "# r * sin(theta)\n",
+        "def Metric1Evaluation(r, theta):\n",
+        "    \"\"\"Evaluate the first metric on the trial.\"\"\"\n",
+        "    return r * math.sin(theta)\n",
+        "\n",
+        "\n",
+        "# r * cos(theta)\n",
+        "def Metric2Evaluation(r, theta):\n",
+        "    \"\"\"Evaluate the second metric on the trial.\"\"\"\n",
+        "    return r * math.cos(theta)\n",
+        "\n",
+        "\n",
+        "def CreateMetrics(r, theta):\n",
+        "    # Evaluate both objective metrics for this trial\n",
+        "    y1 = Metric1Evaluation(r, theta)\n",
+        "    y2 = Metric2Evaluation(r, theta)\n",
+        "    print(\n",
+        "        \"[r = {}, theta = {}] => y1 = r*sin(theta) = {}, y2 = r*cos(theta) = {}\".format(\n",
+        "            r, theta, y1, y2\n",
+        "        )\n",
+        "    )\n",
+        "    measurement = pyvizier.Measurement()\n",
+        "    measurement.metrics[\"y1\"] = y1\n",
+        "    measurement.metrics[\"y2\"] = y2\n",
+        "\n",
+        "    # Return the results for this trial\n",
+        "    return measurement"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2DgUIEpZ-_fJ"
+      },
+      "source": [
+        "### Optimization\n",
+        "\n",
+        "The following code defines a study with parameters and metrics, evaluates the metric information based on the suggestions from Vizier, and reports the metrics value back. After a few rounds of iteration, you can get optimal trials by calling `optimal_trials()`. The code is adapt to both Vertex Vizier and OSS Vizier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "s-AHfPOASXXW"
+      },
+      "outputs": [],
+      "source": [
+        "problem = pyvizier.StudyConfig()\n",
+        "problem.algorithm = pyvizier.Algorithm.RANDOM_SEARCH\n",
+        "\n",
+        "# Objective Metrics\n",
+        "problem.metric_information.append(\n",
+        "    pyvizier.MetricInformation(name=\"y1\", goal=pyvizier.ObjectiveMetricGoal.MINIMIZE)\n",
+        ")\n",
+        "problem.metric_information.append(\n",
+        "    pyvizier.MetricInformation(name=\"y2\", goal=pyvizier.ObjectiveMetricGoal.MAXIMIZE)\n",
+        ")\n",
+        "\n",
+        "# Defines the parameters configuration.\n",
+        "root = problem.search_space.select_root()\n",
+        "root.add_float_param(\"r\", 0, 1.0, scale_type=pyvizier.ScaleType.LINEAR)\n",
+        "root.add_float_param(\"theta\", 0, 1.57, scale_type=pyvizier.ScaleType.LINEAR)\n",
+        "\n",
+        "study = create_study(\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
+        "    display_name=STUDY_DISPLAY_NAME,\n",
+        "    problem=problem,\n",
+        ")\n",
+        "\n",
+        "for _ in range(MAX_NUM_ITERATIONS):\n",
+        "    trials = study.suggest(count=SUGGESTION_COUNT)\n",
+        "    for trial in trials:\n",
+        "        materialize_trial = trial.materialize()\n",
+        "        measurement = CreateMetrics(\n",
+        "            materialize_trial.parameters.get_value(\"r\"),\n",
+        "            materialize_trial.parameters.get_value(\"theta\"),\n",
+        "        )\n",
+        "        trial.add_measurement(measurement=measurement)\n",
+        "        trial.complete(measurement=measurement)\n",
+        "\n",
+        "optimal_trials = study.optimal_trials()\n",
+        "print(\"optimal_trials: {}\".format(optimal_trials))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KAxfq9Fri2YV"
+      },
+      "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. You can also manually delete resources that you created by running the following code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zQlLDfvlzYde"
+      },
+      "outputs": [],
+      "source": [
+        "study.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "conversions_vertex_vizier_and_open_source_vizier.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/official/CODEOWNERS

@@ -22,6 +22,7 @@
 /ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb @jialuzh
 /ml_metadata/vertex-pipelines-ml-metadata.ipynb @sararob
 /vizier/gapic-vizier-multi-objective-optimization.ipynb @halio-g
+/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb @halio-g
 /feature_store/gapic-feature-store.ipynb @diemtvu
 /managed_notebooks @GoogleCloudPlatform/notebooks-team
 /pipelines/google_cloud_pipeline_components_bqml_text.ipynb @inardini
@@ -31,7 +32,7 @@
 /custom/custom_training_tensorboard_profiler.ipynb @itseric
 /workbench/spark/spark_sample_notebook.ipynb @bradmiro
 /workbench/spark/spark_ml.ipynb @bradmiro
-/model-registry/bqml-vertexai-model-registry.ipynb @soheilazangeneh
+/model_registry/bqml_vertexai_model_registry.ipynb @soheilazangeneh
 /workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb @alokpattani
 /model_evaluation/automl_tabular_classification_model_evaluation.ipynb @soheilazangeneh
 /model_evaluation/automl_tabular_regression_model_evaluation.ipynb @soheilazangeneh

notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb

@@ -88,7 +88,8 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset you will be using is the [Safe Driver Prediction](https://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction/data?select=train.csv) dataset for predicting the probability of an auto insurance policy holder filing a claim for a given incident."
+        "The dataset you will be using is [Bank Marketing](https://archive.ics.uci.edu/ml/datasets/bank+marketing).\n",
+        "The data is for direct marketing campaigns (phone calls) of a Portuguese banking institution. The binary classification goal is to predict if a client will subscribe a term deposit. For this notebook, we randomly selected 90% of the rows in the original dataset and saved them in a train.csv file hosted on Cloud Storage. To download the file, click [here](https://storage.googleapis.com/cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv)."
       ]
     },
     {
@@ -638,9 +639,9 @@
         "root_dir = os.path.join(BUCKET_URI, \"automl_tabular_pipeline\")\n",
         "prediction_type = \"classification\"\n",
         "optimization_objective = \"minimize-log-loss\"\n",
-        "target_column = \"target\"\n",
+        "target_column = \"deposit\"\n",
         "data_source_csv_filenames = (\n",
-        "    \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/safe-driver/train.csv\"\n",
+        "    \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv\"\n",
         ")\n",
         "data_source_bigquery_table_path = None  # format: bq://bq_project.bq_dataset.bq_table\n",
         "\n",
@@ -659,63 +660,22 @@
         "weight_column = None\n",
         "\n",
         "features = [\n",
-        "    \"ps_ind_01\",\n",
-        "    \"ps_ind_02_cat\",\n",
-        "    \"ps_ind_03\",\n",
-        "    \"ps_ind_04_cat\",\n",
-        "    \"ps_ind_05_cat\",\n",
-        "    \"ps_ind_06_bin\",\n",
-        "    \"ps_ind_07_bin\",\n",
-        "    \"ps_ind_08_bin\",\n",
-        "    \"ps_ind_09_bin\",\n",
-        "    \"ps_ind_10_bin\",\n",
-        "    \"ps_ind_11_bin\",\n",
-        "    \"ps_ind_12_bin\",\n",
-        "    \"ps_ind_13_bin\",\n",
-        "    \"ps_ind_14\",\n",
-        "    \"ps_ind_15\",\n",
-        "    \"ps_ind_16_bin\",\n",
-        "    \"ps_ind_17_bin\",\n",
-        "    \"ps_ind_18_bin\",\n",
-        "    \"ps_reg_01\",\n",
-        "    \"ps_reg_02\",\n",
-        "    \"ps_reg_03\",\n",
-        "    \"ps_car_01_cat\",\n",
-        "    \"ps_car_02_cat\",\n",
-        "    \"ps_car_03_cat\",\n",
-        "    \"ps_car_04_cat\",\n",
-        "    \"ps_car_05_cat\",\n",
-        "    \"ps_car_06_cat\",\n",
-        "    \"ps_car_07_cat\",\n",
-        "    \"ps_car_08_cat\",\n",
-        "    \"ps_car_09_cat\",\n",
-        "    \"ps_car_10_cat\",\n",
-        "    \"ps_car_11_cat\",\n",
-        "    \"ps_car_11\",\n",
-        "    \"ps_car_12\",\n",
-        "    \"ps_car_13\",\n",
-        "    \"ps_car_14\",\n",
-        "    \"ps_car_15\",\n",
-        "    \"ps_calc_01\",\n",
-        "    \"ps_calc_02\",\n",
-        "    \"ps_calc_03\",\n",
-        "    \"ps_calc_04\",\n",
-        "    \"ps_calc_05\",\n",
-        "    \"ps_calc_06\",\n",
-        "    \"ps_calc_07\",\n",
-        "    \"ps_calc_08\",\n",
-        "    \"ps_calc_09\",\n",
-        "    \"ps_calc_10\",\n",
-        "    \"ps_calc_11\",\n",
-        "    \"ps_calc_12\",\n",
-        "    \"ps_calc_13\",\n",
-        "    \"ps_calc_14\",\n",
-        "    \"ps_calc_15_bin\",\n",
-        "    \"ps_calc_16_bin\",\n",
-        "    \"ps_calc_17_bin\",\n",
-        "    \"ps_calc_18_bin\",\n",
-        "    \"ps_calc_19_bin\",\n",
-        "    \"ps_calc_20_bin\",\n",
+        "    \"age\",\n",
+        "    \"job\",\n",
+        "    \"marital\",\n",
+        "    \"education\",\n",
+        "    \"default\",\n",
+        "    \"balance\",\n",
+        "    \"housing\",\n",
+        "    \"loan\",\n",
+        "    \"contact\",\n",
+        "    \"day\",\n",
+        "    \"month\",\n",
+        "    \"duration\",\n",
+        "    \"campaign\",\n",
+        "    \"pdays\",\n",
+        "    \"previous\",\n",
+        "    \"poutcome\",\n",
         "]\n",
         "transformations = generate_auto_transformation(features)\n",
         "transform_config_path = os.path.join(root_dir, f\"transform_config_{uuid.uuid4()}.json\")\n",

notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb

@@ -689,6 +689,7 @@
         "# Training\n",
         "TRAIN_EXECUTION_NAME = \"train\"\n",
         "TARGET = \"category\"\n",
+        "TARGET_LABELS = [\"b\", \"t\", \"e\", \"m\"]\n",
         "FEATURES = \"title\"\n",
         "TEST_SIZE = 0.2\n",
         "SEED = 8\n",
@@ -974,7 +975,8 @@
         "import joblib\n",
         "import pandas as pd\n",
         "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
-        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
+        "from sklearn.metrics import (accuracy_score, confusion_matrix, precision_score,\n",
+        "                             recall_score)\n",
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.naive_bayes import MultinomialNB\n",
         "from sklearn.pipeline import Pipeline\n",
@@ -1049,13 +1051,17 @@
         "    y_pred = model.predict(X_test)\n",
         "\n",
         "    # Store evaluation metrics\n",
-        "    # Store evaluation metrics\n",
-        "    metrics = {\n",
+        "    summary_metrics = {\n",
         "        \"accuracy\": round(accuracy_score(y_test, y_pred), 5),\n",
         "        \"precision\": round(precision_score(y_test, y_pred, average=\"weighted\"), 5),\n",
         "        \"recall\": round(recall_score(y_test, y_pred, average=\"weighted\"), 5),\n",
         "    }\n",
-        "    return metrics\n",
+        "    classification_metrics = {\n",
+        "        \"matrix\": confusion_matrix(y_test, y_pred, labels=TARGET_LABELS).tolist(),\n",
+        "        \"labels\": TARGET_LABELS,\n",
+        "    }\n",
+        "\n",
+        "    return summary_metrics, classification_metrics\n",
         "\n",
         "\n",
         "def save_model(model: Pipeline, save_path: str) -> int:\n",
@@ -1138,13 +1144,20 @@
         "\n",
         "    # Evaluate model\n",
         "    logging.info(\"Evaluate model.\")\n",
-        "    model_metrics = evaluate_model(trained_pipeline, x_val, y_val)\n",
+        "    summary_metrics, classification_metrics = evaluate_model(\n",
+        "        trained_pipeline, x_val, y_val\n",
+        "    )\n",
         "\n",
         "    # Log training metrics and store model artifact ----------------------------\n",
         "\n",
         "    # Log training metrics\n",
         "    logging.info(\"Log training metrics.\")\n",
-        "    vertex_ai.log_metrics(model_metrics)\n",
+        "    vertex_ai.log_metrics(summary_metrics)\n",
+        "    vertex_ai.log_classification_metrics(\n",
+        "        labels=classification_metrics[\"labels\"],\n",
+        "        matrix=classification_metrics[\"matrix\"],\n",
+        "        display_name=\"my-confusion-matrix\",\n",
+        "    )\n",
         "\n",
         "    # Generate first ten predictions\n",
         "    logging.info(\"Generate prediction sample.\")\n",

notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb

@@ -188,9 +188,9 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q\n",
-        "! pip3 install {USER_FLAG} --upgrade google-cloud-storage -q\n",
-        "! pip3 install --upgrade tensorflow $USER_FLAG -q"
+        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform \\\n",
+        "                                             google-cloud-storage \\\n",
+        "                                             tensorflow==2.5"
       ]
     },
     {
@@ -317,7 +317,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -326,9 +329,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### UUID\n",
         "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -339,9 +342,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\n",
+        "# Generate a uuid of length 8\n",
+        "def generate_uuid():\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -353,7 +363,7 @@
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Workbench AI Notebooks**, your environment is already\n",
-        "authenticated. Skip this step.\n",
+        "authenticated.  \n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
@@ -448,7 +458,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
         "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
       ]
     },
@@ -948,7 +958,7 @@
       "outputs": [],
       "source": [
         "job = aip.CustomTrainingJob(\n",
-        "    display_name=\"boston_\" + TIMESTAMP,\n",
+        "    display_name=\"boston_\" + UUID,\n",
         "    script_path=\"custom/trainer/task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
         "    requirements=[\"gcsfs==0.7.1\", \"tensorflow-datasets==4.4\"],\n",
@@ -983,7 +993,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, TIMESTAMP)\n",
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, UUID)\n",
         "\n",
         "EPOCHS = 20\n",
         "STEPS = 100\n",
@@ -1404,7 +1414,7 @@
       "outputs": [],
       "source": [
         "model = aip.Model.upload(\n",
-        "    display_name=\"boston_\" + TIMESTAMP,\n",
+        "    display_name=\"boston_\" + UUID,\n",
         "    artifact_uri=MODEL_DIR,\n",
         "    serving_container_image_uri=DEPLOY_IMAGE,\n",
         "    explanation_parameters=parameters,\n",
@@ -1434,7 +1444,7 @@
       "source": [
         "### Make test items\n",
         "\n",
-        "You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use a portion of the preprocessed evaluation data (x_test) for your batch request."
       ]
     },
     {
@@ -1460,14 +1470,16 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "make_batch_file:automl,tabular,alt"
+        "id": "622926573681"
       },
       "outputs": [],
       "source": [
-        "! gsutil cat $IMPORT_FILE | head -n 1 > tmp.csv\n",
-        "! gsutil cat $IMPORT_FILE | tail -n 10 >> tmp.csv\n",
-        "\n",
-        "! cut -d, -f1-16 tmp.csv > batch.csv\n",
+        "with open(\"batch.csv\", \"w\") as f:\n",
+        "    f.write(\"crim, zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, b, lstat\\n\")\n",
+        "    f.write(str(x_test[0].tolist()).replace(\"[\", \"\").replace(\"]\", \"\"))\n",
+        "    f.write(\"\\n\")\n",
+        "    f.write(str(x_test[1].tolist()))\n",
+        "    f.write(\"\\n\")\n",
         "\n",
         "gcs_input_uri = BUCKET_URI + \"/test.csv\"\n",
         "\n",
@@ -1505,7 +1517,7 @@
         "MAX_NODES = 1\n",
         "\n",
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"boston_\" + TIMESTAMP,\n",
+        "    job_display_name=\"boston_\" + UUID,\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"csv\",\n",
@@ -1539,8 +1551,7 @@
       },
       "outputs": [],
       "source": [
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    batch_predict_job.wait()"
+        "batch_predict_job.wait()"
       ]
     },
     {
@@ -1567,22 +1578,19 @@
       },
       "outputs": [],
       "source": [
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    import tensorflow as tf\n",
+        "bp_iter_outputs = batch_predict_job.iter_outputs()\n",
         "\n",
-        "    bp_iter_outputs = batch_predict_job.iter_outputs()\n",
+        "explanation_results = list()\n",
+        "for blob in bp_iter_outputs:\n",
+        "    if blob.name.split(\"/\")[-1].startswith(\"explanation\"):\n",
+        "        explanation_results.append(blob.name)\n",
         "\n",
-        "    explanation_results = list()\n",
-        "    for blob in bp_iter_outputs:\n",
-        "        if blob.name.split(\"/\")[-1].startswith(\"explanation\"):\n",
-        "            explanation_results.append(blob.name)\n",
-        "\n",
-        "    tags = list()\n",
-        "    for explanation_result in explanation_results:\n",
-        "        gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{explanation_result}\"\n",
-        "        with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
-        "            for line in gfile.readlines():\n",
-        "                print(line)"
+        "tags = list()\n",
+        "for explanation_result in explanation_results:\n",
+        "    gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{explanation_result}\"\n",
+        "    with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
+        "        for line in gfile.readlines():\n",
+        "            print(line)"
       ]
     },
     {
@@ -1616,7 +1624,9 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
+        "    ! gsutil rm -r $BUCKET_URI\n",
+        "\n",
+        "! rm -rf batch.csv custom.tar.gz custom"
       ]
     }
   ],

notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb

@@ -29,7 +29,7 @@
         "id": "title"
       },
       "source": [
-        "# Vertex AI Pipelines: Evaluating Batch Prediction results from Custom Tabular regression model\n",
+        "# Vertex AI Pipelines: Evaluating batch prediction results from Custom Tabular regression model\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -61,7 +61,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to use the Vertex AI regression model evaluation component to evaluate a Custom regression model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. "
+        "This notebook demonstrates how to use the Vertex AI regression model evaluation component to evaluate a custom regression model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. "
       ]
     },
     {
@@ -76,10 +76,10 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI `CustomTrainingJob`\n",
-        "- Vertex AI `BatchPrediction`\n",
-        "- Vertex AI `Pipeline`\n",
-        "- Vertex AI `Model Registry`\n",
+        "- Vertex AI Training (Custom Training)\n",
+        "- Vertex AI Batch Predictions\n",
+        "- Vertex AI Pipelines\n",
+        "- Vertex AI Model Registry\n",
         "\n",
         "\n",
         "The steps performed include:\n",
@@ -89,10 +89,10 @@
         "- Retrieve and load the model artifacts.\n",
         "- View the model evaluation.\n",
         "- Upload the model as a Vertex AI Model resource.\n",
-        "- Import a pre-trained `Vertex AI model resource` into the pipeline\n",
-        "- Run a `batch prediction` job\n",
-        "- Evaulate the model using the `regression evaluation component`\n",
-        "- Import the Classification Metrics to the Vertex AI model resource"
+        "- Import a pre-trained `Vertex AI model resource` into the pipeline.\n",
+        "- Run a `batch prediction` job in the pipeline.\n",
+        "- Evaulate the model using the `regression evaluation component`.\n",
+        "- Import the Regression Metrics to the Vertex AI model resource."
       ]
     },
     {
@@ -191,13 +191,13 @@
         "USER_FLAG = \"\"\n",
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
-        "! pip3 install --upgrade tensorflow $USER_FLAG -q\n",
-        "! pip3 install google-cloud-pipeline-components==1.0.20 {USER_FLAG} -q\n",
-        "! pip3 install --upgrade kfp {USER_FLAG} -q\n",
-        "! pip3 install --upgrade matplotlib {USER_FLAG} -q"
+        "    \n",
+        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform \\\n",
+        "                                    tensorflow \\\n",
+        "                                    google-cloud-pipeline-components \\\n",
+        "                                    kfp \\\n",
+        "                                    matplotlib \\\n",
+        "                                    google-cloud-storage "
       ]
     },
     {
@@ -624,7 +624,7 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex AI SDK for Python\n",
+        "### Initialize Vertex AI SDK for Python\n",
         "\n",
         "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
@@ -659,7 +659,7 @@
         "\n",
         "Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) \n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -753,7 +753,7 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
@@ -795,30 +795,23 @@
         "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tutorial_start:custom"
-      },
-      "source": [
-        "# Training a custom model\n",
-        "\n",
-        "Now you are ready to start creating your own custom model and training for Boston Housing. \n",
-        "\n",
-        "[Learn more about custom model training on Vertex AI](https://cloud.google.com/vertex-ai/docs/training/custom-training)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "examine_training_package"
       },
       "source": [
+        "## Training a custom model\n",
+        "\n",
+        "Now you are ready to start creating your own custom model and training for Boston Housing. \n",
+        "\n",
+        "[Learn more about custom model training on Vertex AI](https://cloud.google.com/vertex-ai/docs/training/custom-training)\n",
+        "\n",
         "### Examine the training package\n",
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, you look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -830,11 +823,13 @@
         "\n",
         "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
         "\n",
-        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replaced the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
+        "The file `trainer/task.py` is the Python script for executing the custom training job. \n",
+        "\n",
+        "**Note:** When you refer to it in the worker pool specification, you replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you will assemble the training package."
+        "In the following cells, you assemble the training package."
       ]
     },
     {
@@ -872,7 +867,7 @@
         "id": "taskpy_contents:boston"
       },
       "source": [
-        "#### Task.py contents\n",
+        "#### Create task.py\n",
         "\n",
         "In the next cell, you write the contents of the training script task.py.  In summary:\n",
         "\n",
@@ -1013,7 +1008,7 @@
         "id": "tarball_training_script"
       },
       "source": [
-        "#### Store the training script on your Cloud Storage bucket\n",
+        "**Store the training script on your Cloud Storage bucket.**\n",
         "\n",
         "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
       ]
@@ -1045,16 +1040,16 @@
         "\n",
         "1) Create a custom training job\n",
         "\n",
-        "2) Run the job.\n",
+        "2) Run the job\n",
         "\n",
-        "#### Create custom training job\n",
+        "#### Create a custom training job\n",
         "\n",
         "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
         "\n",
-        "- `display_name`: The human readable name for the custom training job.\n",
-        "- `container_uri`: The training container image.\n",
-        "- `requirements`: Package requirements for the training container image (e.g., pandas).\n",
-        "- `script_path`: The relative path to the training script."
+        "- `display_name`: The human readable name for the custom training job\n",
+        "- `container_uri`: The training container image\n",
+        "- `requirements`: Package requirements for the training container image (e.g., pandas)\n",
+        "- `script_path`: The relative path to the training script"
       ]
     },
     {
@@ -1081,7 +1076,7 @@
         "id": "prepare_custom_cmdargs"
       },
       "source": [
-        "### Prepare your command-line arguments\n",
+        "#### Prepare your command-line arguments\n",
         "\n",
         "Now define the command-line arguments for your custom training container:\n",
         "\n",
@@ -1128,7 +1123,7 @@
       "source": [
         "#### Run the custom training job\n",
         "\n",
-        "Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
+        "Next, you run the custom job to start the training job by invoking the `run()` method, with the following parameters:\n",
         "\n",
         "- `args`: The command-line arguments to pass to the training script.\n",
         "- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
@@ -1136,7 +1131,7 @@
         "- `accelerator_type`: The hardware accelerator type.\n",
         "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
         "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
-        "- `sync`: Whether to block until completion of the job."
+        "- `sync`: Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed."
       ]
     },
     {
@@ -1175,11 +1170,11 @@
         "id": "ab954a846b61"
       },
       "source": [
-        "## Load the saved model\n",
+        "#### Load the saved model\n",
         "\n",
         "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket.  Now load it from the Cloud Storage bucket, and then you can perform tasks such as model evaluation and make prediction requests.\n",
         "\n",
-        "To load, you use the `tf.saved_model.load()` method passing it the Cloud Storage path where the model is saved -- specified by `MODEL_DIR`."
+        "To load the model, you pass the Cloud Storage path \"MODEL_DIR\" to the `tf.saved_model.load()` method."
       ]
     },
     {
@@ -1199,11 +1194,11 @@
         "id": "serving_function_signature"
       },
       "source": [
-        "## Get the serving function signature\n",
+        "#### Get the serving function signature\n",
         "\n",
         "You can get the signatures of your model's input and output layers by reloading the model into memory, and querying it for the signatures corresponding to each layer.\n",
         "\n",
-        "When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which you use later when you make a prediction request.\n",
+        "When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function which you use later when you make a prediction request.\n",
         "\n",
         "You also need to know the name of the serving function's input and output layer for constructing the explanation metadata **during a later step**."
       ]
@@ -1224,52 +1219,31 @@
         "print(\"Serving function output:\", serving_output)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e9926f55a85d"
-      },
-      "source": [
-        "## Configure feature-based explanations (Optional) "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3276368bae14"
-      },
-      "source": [
-        "**If you want to configure explanations for the model, follow this step else skip this step.**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c67a1509f62d"
-      },
-      "source": [
-        "**To use Vertex Explainable AI with a custom-trained model, you must configure certain options when you create the Model resource that you plan to request explanations from, when you deploy the model, or when you submit a batch explanation job.** \n",
-        "\n",
-        "**If you want to use Vertex Explainable AI with an AutoML tabular model, then you don't need to perform any configuration; Vertex AI automatically configures the model for Vertex Explainable AI.**"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "69d4859c7196"
       },
       "source": [
+        "## Configure feature-based explanations (Optional) \n",
+        "\n",
+        "**For configuring explanations to the model, follow this step. This step is optional.**\n",
+        "\n",
+        "To use Vertex Explainable AI with a custom-trained model, you must configure certain options when you create the Model resource that you plan to request explanations from, or when you deploy the model, or when you submit a batch explanation job.\n",
+        "\n",
+        "If you want to use Vertex Explainable AI with an AutoML tabular model, then you don't need to perform any configuration. Vertex AI automatically configures the model for Vertex Explainable AI.\n",
+        "\n",
         "### Explanation Specification\n",
         "\n",
-        "To get explanations when doing a prediction, you must enable the explanation capability and set corresponding settings when you upload your custom model to an Vertex `Model` resource. These settings are referred to as the explanation metadata, which consists of:\n",
+        "To get explanations when doing a prediction, you must enable the explanation feature and set corresponding settings when you upload your custom model to Vertex AI Model registry. These settings are referred to as the explanation metadata, which consists of:\n",
         "\n",
-        "- `parameters`: This is the specification for the explainability algorithm to use for explanations on your model. You can choose between:\n",
-        "  - Shapley - *Note*, not recommended for image data -- can be very long running\n",
+        "- `parameters`: Specification for the explainability algorithm to use for explanations on your model. You can choose between:\n",
+        "  - Shapley (not recommended for image data as the computation can take long)\n",
         "  - XRAI\n",
         "  - Integrated Gradients\n",
-        "- `metadata`: This is the specification for how the algoithm is applied on your custom model.\n",
+        "- `metadata`: Specification for how the algoithm is applied on your custom model\n",
         "\n",
-        "[Learn more about explanation specification here](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-feature-based#when-creating-or-importing-model)\n",
+        "Learn more about [explanation specification](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-feature-based#when-creating-or-importing-model).\n",
         "\n",
         "\n",
         "\n",
@@ -1344,24 +1318,20 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "beec0356d7b8"
-      },
-      "source": [
-        "## Make instance schema and prediction schema yaml files"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "ed414a2f945a"
       },
       "source": [
-        "#### instance_schema.yaml and prediction_schema.yaml contents\n",
+        "### Make instance schema and prediction schema yaml files\n",
         "\n",
-        "In the next cell, you write the contents of the instance_schema.yaml . You write the information about the prediction instances you give to your batch prediction .\n",
+        "In next cells, you write the contents of **instance_schema.yaml** and **prediction_schema.yaml** files. Content structure is same for both files.\n",
+        "\n",
+        "\n",
+        "#### Make instance schema yaml file\n",
+        "\n",
+        "In the next cell, you write the contents of the instance_schema.yaml . You write the structure about the prediction instances you give to your batch prediction .\n",
         "\n",
         "- Give the title and description.\n",
         "- Give type of the input. In our case input to batch predictin is \n",
@@ -1381,10 +1351,9 @@
       "outputs": [],
       "source": [
         "%%writefile instance_schema.yaml\n",
-        "title: TabularClassification\n",
-        "description: 'Classification Instances.\n",
+        "title: TabularRegression\n",
+        "description: 'Regression Instances.'\n",
         "\n",
-        "  '\n",
         "type: object\n",
         "properties:\n",
         "  dense_input:\n",
@@ -1393,9 +1362,7 @@
         "      type: float\n",
         "      minimum: 0.0\n",
         "      maximum: 1.0\n",
-        "    description: 'Input values to model\n",
-        "\n",
-        "      '\n"
+        "    description: 'Input values to model'\n"
       ]
     },
     {
@@ -1404,7 +1371,7 @@
         "id": "ef75c6f86088"
       },
       "source": [
-        "### Make prediction schema yaml file"
+        "#### Make prediction schema yaml file"
       ]
     },
     {
@@ -1413,15 +1380,13 @@
         "id": "53f324aaf19a"
       },
       "source": [
-        "In the next cell, you write the contents of the prediction_schema.yaml . You write the information about the prediction output you get from your batch prediction job.\n",
+        "In the next cell, you write the contents of the prediction_schema.yaml . You write the structure about the prediction output you get from your batch prediction job.\n",
         "\n",
-        "Contents are same as instance_schema.yaml file\n",
-        "\n",
-        "In our case output from batch prediction job is \n",
+        "In your case, output from batch prediction job is \n",
         "\n",
         "**{\"instance\": {\"dense_input\": [0.02715405449271202, 0.0, 0.027177177369594574, 0.0, 0.0010195195209234953, 0.009660660289227962, 0.1501501500606537, 0.0027548049110919237, 0.036036036908626556, 1.0, 0.03033033013343811, 0.04091591760516167, 0.043618619441986084]}, \"prediction\": [0.522156954]}**\n",
         "\n",
-        "Prediction, you get is **\"prediction\": [0.522156954]**, which is of type array."
+        "Prediction output of batch prediction job is **\"prediction\": [0.522156954]**, which is of type array."
       ]
     },
     {
@@ -1434,8 +1399,8 @@
       "source": [
         "%%writefile prediction_schema.yaml\n",
         "title: TabularRegression\n",
-        "description: 'Regression results.\n",
-        "  '\n",
+        "description: 'Regression results.'\n",
+        "\n",
         "type: array"
       ]
     },
@@ -1445,7 +1410,7 @@
         "id": "ff29b80d8b9c"
       },
       "source": [
-        "Upload both files to your Cloud Storage bucket."
+        "Upload both the files to your Cloud Storage bucket."
       ]
     },
     {
@@ -1466,7 +1431,7 @@
         "id": "upload_model:mbsdk"
       },
       "source": [
-        "## Upload the model\n",
+        "### Upload the model\n",
         "\n",
         "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
         "\n",
@@ -1479,16 +1444,9 @@
         "- `explanation_parameters`: Parameters to configure explaining for `Model`'s predictions.\n",
         "- `explanation_metadata`: Metadata describing the `Model`'s input and output for explanation.\n",
         "\n",
-        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "01fca26f26c3"
-      },
-      "source": [
-        "**If you want to configure explanations for the model, set `explanation_parameters`,  `explanation_metadata` parameters. Else do not set them.**"
+        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method.\n",
+        "\n",
+        "**Note:** If you want to configure explanations for the model, set `explanation_parameters`,  `explanation_metadata` parameters. Else do not set them."
       ]
     },
     {
@@ -1523,7 +1481,7 @@
         "\n",
         "You load the Boston Housing test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the feature data, and the corresponding labels (median value of owner-occupied home).\n",
         "\n",
-        "You don't need the training data, and hence why we loaded it as `(_, _)`.\n",
+        "You don't need the training data, and hence you load it as `(_, _)`.\n",
         "\n",
         "Before you can run the data through the pipeline, you need to preprocess it:\n",
         "\n",
@@ -1591,35 +1549,21 @@
         "        f.write(json.dumps(data) + \"\\n\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6d554697ccba"
-      },
-      "source": [
-        "## Model Evaluation"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "dAYyBa_qw0aT"
       },
       "source": [
+        "## Model Evaluation\n",
+        "\n",
+        "Now you create a pipeline for performing model evaluation.\n",
+        "\n",
         "### Create Pipeline for evaluations\n",
         "\n",
-        "Now, you run a Vertex AI Batch Prediction job and generate evaluations and feature-attributions on its results. \n",
+        "Now, you run a Vertex AI BatchPrediction job and generate evaluations and feature-attributions on its results by creating a Vertex AI pipeline using the components available from the [google-cloud-pipeline-components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.17/index.html) python package. \n",
         "\n",
-        "To do so, you create a Vertex AI pipeline using the components available from the [`google-cloud-pipeline-components`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.17/index.html) python package.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "edb8612865ce"
-      },
-      "source": [
-        "Set a display name for your pipeline."
+        "**Set a display name for your pipeline.**"
       ]
     },
     {
@@ -1658,7 +1602,7 @@
         "id": "51f9c8d3e4ab"
       },
       "source": [
-        "### Define the Pipeline\n",
+        "#### Define the Pipeline\n",
         "\n",
         "While defining the flow of the pipeline, you get the model resource first. Then, you sample the provided source dataset for batch predictions and create a batch prediction. The explanations are enabled while creating the batch prediction job to generate feature attributions. Once the batch prediction job is completed, you get the regression evaluation metrics and the feature attributions from the results.\n",
         "\n",
@@ -1672,28 +1616,17 @@
         "- `ModelEvaluationFeatureAttributionOp`: Compute feature attribution on a trained model’s batch explanation results. Creates a Dataflow job with Apache Beam and TFMA to compute feature attributions. \n",
         "- `ModelImportEvaluationOp`: Imports a model evaluation artifact to an existing Vertex AI Model resource with ModelService.ImportModelEvaluation. \n",
         "\n",
-        "**The pipeline takes about 1 hour to complete.**\n",
         "\n",
-        "Learn more about [Google Cloud Pipeline Model Evaluation components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.20/google_cloud_pipeline_components.experimental.evaluation.html)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9aba67b73868"
-      },
-      "source": [
-        "#### Example workflow\n",
+        "Learn more about [Google Cloud Pipeline Model Evaluation components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.20/google_cloud_pipeline_components.experimental.evaluation.html).\n",
+        "\n",
+        "##### Example workflow\n",
         "\n",
         "1.If this\n",
         "\n",
         "({\"dense_input\": [0.7220525145530701, 0.0, 0.6524873971939087], \"MEDV\": 7.2}\n",
         "{\"dense_input\": [0.004922522697597742, 0.0, 0.3608507513999939], \"MEDV\": 18.8})\n",
         "\n",
-        "\n",
-        "\n",
         "is the input  to data sampler and if sample size is 1,\n",
-        "\n",
         "output is \n",
         "\n",
         "{\"dense_input\": [0.004922522697597742, 0.0, 0.3608507513999939], \"MEDV\": 18.8}\n",
@@ -1708,7 +1641,7 @@
         "\n",
         "4.The output of the batch prediction is given as input for the `ModelEvaluationRegressionOp` component. For a custom model, the ground truth cannot be part of the batch prediction instance, so we provide the output of the data sampler with ground truths to `ModelEvaluationRegressionOp`'s `ground_truth_gcs_source` parameter.\n",
         "\n",
-        "5.In `ModelImportEvaluationOp` We import evaluation metrics and feature attributions to the model.\n"
+        "5.In `ModelImportEvaluationOp`, we import evaluation metrics and feature attributions to the model.\n"
       ]
     },
     {
@@ -1729,14 +1662,9 @@
         "    batch_predict_gcs_source_uris: list,\n",
         "    key_columns: list,\n",
         "    batch_predict_instances_format: str,\n",
+        "    batch_predict_sample_size: int,\n",
         "    batch_predict_predictions_format: str = \"jsonl\",\n",
         "    batch_predict_machine_type: str = \"n1-standard-4\",\n",
-        "    batch_predict_explanation_metadata: dict = {},\n",
-        "    batch_predict_explanation_parameters: dict = {},\n",
-        "    batch_predict_explanation_data_sample_size: int = 10000,\n",
-        "    dataflow_max_num_workers: int = 5,\n",
-        "    dataflow_use_public_ips: bool = True,\n",
-        "    encryption_spec_key_name: str = \"\",\n",
         "):\n",
         "\n",
         "    from google_cloud_pipeline_components.aiplatform import ModelBatchPredictOp\n",
@@ -1755,7 +1683,7 @@
         "        root_dir=root_dir,\n",
         "        gcs_source_uris=batch_predict_gcs_source_uris,\n",
         "        instances_format=batch_predict_instances_format,\n",
-        "        sample_size=batch_predict_explanation_data_sample_size,\n",
+        "        sample_size=batch_predict_sample_size,\n",
         "    )\n",
         "\n",
         "    # Run Data-splitter task\n",
@@ -1779,11 +1707,8 @@
         "        predictions_format=batch_predict_predictions_format,\n",
         "        gcs_destination_output_uri_prefix=root_dir,\n",
         "        machine_type=batch_predict_machine_type,\n",
-        "        encryption_spec_key_name=encryption_spec_key_name,\n",
         "        # Set the explanation parameters\n",
         "        generate_explanation=True,\n",
-        "        explanation_parameters=batch_predict_explanation_parameters,\n",
-        "        explanation_metadata=batch_predict_explanation_metadata,\n",
         "    )\n",
         "\n",
         "    # Run evaluation based on prediction type and feature attribution component.\n",
@@ -1799,9 +1724,6 @@
         "        predictions_format=batch_predict_predictions_format,\n",
         "        prediction_score_column=\"prediction\",\n",
         "        ground_truth_column=target_column_name,\n",
-        "        dataflow_max_workers_num=dataflow_max_num_workers,\n",
-        "        dataflow_use_public_ips=dataflow_use_public_ips,\n",
-        "        encryption_spec_key_name=encryption_spec_key_name,\n",
         "    )\n",
         "\n",
         "    # Get Feature Attributions\n",
@@ -1811,9 +1733,6 @@
         "        root_dir=root_dir,\n",
         "        predictions_format=\"jsonl\",\n",
         "        predictions_gcs_source=batch_explain_task.outputs[\"gcs_output_directory\"],\n",
-        "        dataflow_max_workers_num=dataflow_max_num_workers,\n",
-        "        dataflow_use_public_ips=dataflow_use_public_ips,\n",
-        "        encryption_spec_key_name=encryption_spec_key_name,\n",
         "    )\n",
         "\n",
         "    ModelImportEvaluationOp(\n",
@@ -1830,7 +1749,7 @@
         "id": "RqcRr7USbseH"
       },
       "source": [
-        "### Compile the pipeline\n",
+        "##### Compile the pipeline\n",
         "\n",
         "Next, compile the pipline to the `tabular_regression_pipline.json` file."
       ]
@@ -1849,23 +1768,17 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UCn7EORsbseH"
-      },
-      "source": [
-        "### Define the parameters to run the pipeline\n",
-        "\n",
-        "Specify the required parameters to run the pipeline.\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "zwrhHGm7bseH"
       },
       "source": [
+        "##### Define the parameters to run the pipeline\n",
+        "\n",
+        "Specify the required parameters to run the pipeline.\n",
+        "\n",
+        "\n",
         "To pass the required arguments to the pipeline, you define the following paramters below:\n",
         "\n",
         "- `project`: Project ID.\n",
@@ -1888,6 +1801,7 @@
       "outputs": [],
       "source": [
         "PIPELINE_ROOT = f\"{BUCKET_URI}/pipeline_root/boston_{UUID}\"\n",
+        "batch_predict_sample_size = 5\n",
         "parameters = {\n",
         "    \"project\": PROJECT_ID,\n",
         "    \"location\": REGION,\n",
@@ -1898,7 +1812,7 @@
         "        BUCKET_URI + \"/\" + \"test_file_with_ground_truth.jsonl\"\n",
         "    ],\n",
         "    \"batch_predict_instances_format\": \"jsonl\",\n",
-        "    \"batch_predict_explanation_data_sample_size\": 5,\n",
+        "    \"batch_predict_sample_size\": batch_predict_sample_size,\n",
         "    \"key_columns\": [\"dense_input\"],\n",
         "}"
       ]
@@ -1914,7 +1828,11 @@
         "- `display_name`: The user-defined name of this Pipeline.\n",
         "- `template_path`: The path of PipelineJob or PipelineSpec JSON or YAML file. It can be a local path, a Google Cloud  Storage URI (e.g. \"gs://project.name\"), or an Artifact Registry URI (e.g. \"https://us-central1-kfp.pkg.dev/proj/repo/pack/latest\").\n",
         "- `parameter_values`: The mapping from runtime parameter names to its values that control the pipeline run.\n",
-        "- `enable_caching`: Whether to turn on caching for the run. If this is not set, defaults to the compile time settings, which are True for all tasks by default, while users may specify different caching options for individual tasks. If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time settings.\n"
+        "- `enable_caching`: Whether to turn on caching for the run. If this is not set, defaults to the compile time settings, which are True for all tasks by default, while users may specify different caching options for individual tasks. If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time settings.\n",
+        "\n",
+        "Run the pipeline using the configured `SERVICE_ACCOUNT`\n",
+        "\n",
+        "**The pipeline takes about 1 hour to complete.**\n"
       ]
     },
     {
@@ -1950,27 +1868,13 @@
         "id": "l7DHzescbseI"
       },
       "source": [
-        "### Runtime Graph of Model Evaluation pipeline \n",
+        "##### Runtime Graph of Model Evaluation pipeline\n",
         "\n",
-        "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WSyD50YgbseJ"
-      },
-      "source": [
-        "<img src=\"images/custom_tabular_regression_evaluation_pipeline.PNG\" style=\"height:622px;width:726px\"></img>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EJGzb54mbseJ"
-      },
-      "source": [
-        "### Get the Model Evaluation Results\n",
+        "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n",
+        "\n",
+        "<img src=\"images/custom_tabular_regression_evaluation_pipeline.PNG\" style=\"height:622px;width:726px\"></img>\n",
+        "\n",
+        "### Get the model evaluation results\n",
         "\n",
         "After the evalution pipeline is finished, run the below cell to print the evaluation metrics."
       ]
@@ -2009,7 +1913,9 @@
         "id": "1-oX7xI6bseJ"
       },
       "source": [
-        "### Visualize the metrics\n"
+        "### Visualize the metrics\n",
+        "\n",
+        "After the evalution pipeline is finished, run the below cell to visualize the evaluation metrics."
       ]
     },
     {
@@ -2049,7 +1955,7 @@
         "\n",
         "Feature attributions indicate how much each feature in your model contributed to the predictions for each given instance.\n",
         "\n",
-        "Learn more about [Feature Attributions](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#feature_attributions)\n",
+        "Learn more about [Feature attributions](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#feature_attributions).\n",
         "\n",
         "Run the below cell to get the feature attributions. "
       ]
@@ -2162,7 +2068,6 @@
         "# Delete model resource\n",
         "model.delete()\n",
         "\n",
-        "\n",
         "# Delete the training job\n",
         "train_job.delete()\n",
         "\n",

notebooks/official/model_monitoring/model_monitoring.ipynb

@@ -975,7 +975,7 @@
         "# Sampling rate (optional, default=.8)\n",
         "LOG_SAMPLE_RATE = 0.8  # @param {type:\"number\"}\n",
         "\n",
-        "# Monitoring Interval in seconds (optional, default=1).\n",
+        "# Monitoring Interval in hours (optional, default=1).\n",
         "MONITOR_INTERVAL = 1  # @param {type:\"number\"}\n",
         "\n",
         "# URI to training dataset.\n",

notebooks/official/model_registry/README.md

@@ -1,14 +1,14 @@
 
-[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model-registry/bqml-vertexai-model-registry.ipynb)
+[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_registry/bqml_vertexai_model_registry.ipynb)
 
 Learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions:
 
 The steps performed include:
 
 - Train a model with `BigQuery ML`
-- Upload the model to `Vertex AI Model Registry` 
+- Upload the model to `Vertex AI Model Registry`
 - Create a `Vertex AI Endpoint` resource
 - Deploy the `Model` resource to the `Endpoint` resource
 - Make `prediction` requests to the model endpoint
-- Run `batch prediction` job on the `Model` resource 
+- Run `batch prediction` job on the `Model` resource
 

notebooks/official/model_registry/bqml_vertexai_model_registry.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/model-registry/bqml-vertexai-model-registry.ipynb\" target=\"_blank\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_registry/bqml_vertexai_model_registry.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/model-registry/bqml-vertexai-model-registry.ipynb\" target=\"_blank\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_registry/bqml_vertexai_model_registry.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/model-registry/bqml-vertexai-model-registry.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/model_registry/bqml_vertexai_model_registry.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",
@@ -864,7 +864,7 @@
   "metadata": {
     "colab": {
       "collapsed_sections": [],
-      "name": "bqml-vertexai-model-registry.ipynb",
+      "name": "bqml_vertexai_model_registry.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb

@@ -204,9 +204,9 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "    \n",
-        "!pip3 install {USER_FLAG} --upgrade google-cloud-aiplatform==1.11.0 \\\n",
-        "                                    kfp==1.8.11 \\\n",
-        "                                    google-cloud-pipeline-components==1.0.18 --quiet --no-warn-conflicts"
+        "! pip3 install {USER_FLAG} --upgrade google-cloud-aiplatform==1.18.1 \\\n",
+        "                                     kfp==1.8.14 \\\n",
+        "                                     google-cloud-pipeline-components==1.0.24 --quiet --no-warn-conflicts"
       ]
     },
     {
@@ -317,7 +317,7 @@
       },
       "outputs": [],
       "source": [
-        "! gcloud config set project $PROJECT_ID"
+        "! gcloud config set project $PROJECT_ID --quiet"
       ]
     },
     {
@@ -479,6 +479,39 @@
         "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b109ba134099"
+      },
+      "source": [
+        "### Enable Google Cloud services\n",
+        "\n",
+        "Enable the following services in your project:\n",
+        "\n",
+        "* Artifact Registry\n",
+        "* Cloud Build\n",
+        "* Container Registry\n",
+        "* Dataproc\n",
+        "* Vertex AI\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1faa3afe3686"
+      },
+      "outputs": [],
+      "source": [
+        "! gcloud services enable \\\n",
+        "    artifactregistry.googleapis.com \\\n",
+        "    cloudbuild.googleapis.com \\\n",
+        "    containerregistry.googleapis.com \\\n",
+        "    dataproc.googleapis.com \\\n",
+        "    aiplatform.googleapis.com"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -709,6 +742,7 @@
         "!gcloud artifacts repositories create $REPO_NAME \\\n",
         "    --repository-format=docker \\\n",
         "    --location=$REGION \\\n",
+        "    --quiet \\\n",
         "    --description=\"loan eligibility spark docker repository\""
       ]
     },
@@ -761,6 +795,7 @@
         "PIPELINE_ROOT = f\"{BUCKET_URI}/pipelines\"\n",
         "PIPELINE_PACKAGE_PATH = str(BUILD_PATH / f\"pipeline_{UUID}.json\")\n",
         "RUNTIME_CONTAINER_IMAGE = f\"gcr.io/{PROJECT_ID}/{RUNTIME_IMAGE}:{IMAGE_TAG}\"\n",
+        "SUBNETWORK_URI = f\"projects/{PROJECT_ID}/regions/{REGION}/subnetworks/{SUBNETWORK}\"\n",
         "ML_APPLICATION = \"loan-eligibility\"\n",
         "TASK = \"sparkml\"\n",
         "MODEL_TYPE = \"rfor\"\n",
@@ -1840,10 +1875,10 @@
       },
       "outputs": [],
       "source": [
-        "!gsutil cp $SRC/__init__.py $BUCKET_URI/src/__init__.py\n",
-        "!gsutil cp $SRC/data_preprocessing.py $BUCKET_URI/src/data_preprocessing.py\n",
-        "!gsutil cp $SRC/model_training.py $BUCKET_URI/src/model_training.py\n",
-        "!gsutil cp $SRC/hp_tuning.py $BUCKET_URI/src/hp_tuning.py"
+        "! gsutil cp $SRC/__init__.py $BUCKET_URI/src/__init__.py\n",
+        "! gsutil cp $SRC/data_preprocessing.py $BUCKET_URI/src/data_preprocessing.py\n",
+        "! gsutil cp $SRC/model_training.py $BUCKET_URI/src/model_training.py\n",
+        "! gsutil cp $SRC/hp_tuning.py $BUCKET_URI/src/hp_tuning.py"
       ]
     },
     {
@@ -2117,7 +2152,7 @@
         "        return c_matrix\n",
         "\n",
         "    # Main -------------------------------------------------------------------------------------------------------------------------------\n",
-        "    with open(metrics_path, mode=\"r\") as json_file:\n",
+        "    with open(metrics_path) as json_file:\n",
         "        metrics_dict = json.load(json_file)\n",
         "\n",
         "    area_roc = metrics_dict[\"test_area_roc\"]\n",
@@ -2210,7 +2245,7 @@
       "outputs": [],
       "source": [
         "# Set DEPLOY_MODEL to True\n",
-        "DEPLOY_MODEL = False"
+        "DEPLOY_MODEL = True"
       ]
     },
     {
@@ -2244,7 +2279,7 @@
         "\n",
         "    # Clone and build the scala-sbt cloud builder\n",
         "    ! git clone https://github.com/GoogleCloudPlatform/cloud-builders-community.git\n",
-        "    ! cd ${CWD}/cloud-builders-community/scala-sbt && \\\n",
+        "    ! cd {CWD}/cloud-builders-community/scala-sbt && \\\n",
         "        gcloud builds submit .\n",
         "\n",
         "    # Clone and build the serving container code\n",
@@ -2433,6 +2468,7 @@
         "    model_name: str = MODEL_NAME,\n",
         "    project_id: str = PROJECT_ID,\n",
         "    location: str = REGION,\n",
+        "    subnetwork_uri: str = SUBNETWORK_URI,\n",
         "    deploy_model: bool = DEPLOY_MODEL,\n",
         "    artifact_uri: str = ARTIFACT_URI,\n",
         "    serving_image_uri: str = SERVING_IMAGE_URI,\n",
@@ -2457,6 +2493,7 @@
         "        container_image=custom_container_image,\n",
         "        main_python_file_uri=preprocessing_main_python_file_uri,\n",
         "        args=build_preprocessing_args_op.output,\n",
+        "        subnetwork_uri=subnetwork_uri,\n",
         "    ).after(build_preprocessing_args_op)\n",
         "\n",
         "    # create dataset\n",
@@ -2476,15 +2513,18 @@
         "    ).after(create_dataset_op)\n",
         "\n",
         "    # training model\n",
-        "    model_traning_op = DataprocPySparkBatchOp(\n",
+        "    model_training_op = DataprocPySparkBatchOp(\n",
         "        project=project_id,\n",
         "        location=location,\n",
         "        container_image=custom_container_image,\n",
         "        main_python_file_uri=training_main_python_file_uri,\n",
         "        args=build_training_args_op.output,\n",
+        "        subnetwork_uri=subnetwork_uri,\n",
         "    ).after(build_training_args_op)\n",
         "\n",
-        "    evaluate_model_op = evaluate_model(metrics_uri=metrics_path).after(model_traning_op)\n",
+        "    evaluate_model_op = evaluate_model(metrics_uri=metrics_path).after(\n",
+        "        model_training_op\n",
+        "    )\n",
         "\n",
         "    # evaluate condition\n",
         "    with Condition(\n",
@@ -2508,7 +2548,8 @@
         "            main_python_file_uri=hpt_main_python_file_uri,\n",
         "            args=build_hpt_args_op.output,\n",
         "            runtime_config_properties=HPT_RUNTIME_PROPERTIES,\n",
-        "        ).after(model_traning_op)\n",
+        "            subnetwork_uri=subnetwork_uri,\n",
+        "        ).after(model_training_op)\n",
         "\n",
         "        # evaluate condition to upload and deploy model to Vertex AI\n",
         "        with Condition(\n",
@@ -2628,9 +2669,44 @@
       "source": [
         "### (Optional) Get online predictions from the deployed model\n",
         "\n",
-        "You can request online predictions if the model was deployed to a Vertex AI endpoint. Use the `google-cloud-aiplatform` client library to request predictions, or use `curl` as per below:\n",
+        "You can request online predictions if the model was deployed to a Vertex AI endpoint. Use the `google-cloud-aiplatform` client library to request predictions, or you can use `curl`.\n",
         "\n",
-        "Create the prediction request payload with the instances that you want to predict:"
+        "For this model, the prediction response contains the predicted label (`0 == not eligible`, `1 == eligible`) for each prediction instance that is sent to the endpoint."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4c311f9fc363"
+      },
+      "source": [
+        "The following cell demonstrates how to use the `google-cloud-aiplatform` client library to request predictions from one or more instances."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f6623204ee52"
+      },
+      "outputs": [],
+      "source": [
+        "instances = [\n",
+        "    [214.0, \"360\", \"Rural\", 2.13, 2.21, 0.0, 0.0, 2.31, 2.01, 0.0, 0.0, 0.0, 0.0],\n",
+        "    [213.0, \"360\", \"Semiurban\", 2.03, 2.11, 0.0, 0.0, 2.13, 2.02, 0.0, 0.0, 0.0, 0.0],\n",
+        "]\n",
+        "\n",
+        "endpoint = vertex_ai.Endpoint.list(filter=f'display_name=\"{MODEL_NAME}\"')[-1]\n",
+        "endpoint.predict(instances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1a2104c45e21"
+      },
+      "source": [
+        "To use `curl`, first write the prediction instances to a file:"
       ]
     },
     {
@@ -2656,7 +2732,7 @@
         "id": "b7cbfec4537d"
       },
       "source": [
-        "Use `curl` to send the prediction request to the Vertex AI endpoint. The response contains the predicted label (`0 == not eligible`, `1 == eligible`) for each instance sent in the request payload."
+        "Use `curl` to send the prediction request to the Vertex AI endpoint:"
       ]
     },
     {
@@ -2667,15 +2743,10 @@
       },
       "outputs": [],
       "source": [
-        "ENDPOINT_ID=!(gcloud ai endpoints list \\\n",
-        "              --region={REGION} \\\n",
-        "              --filter=display_name={MODEL_NAME} \\\n",
-        "              --format='value(name)')\n",
-        "\n",
-        "!curl -X POST \\\n",
+        "! curl -X POST \\\n",
         "   -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
         "   -H \"Content-Type: application/json\" \\\n",
-        "   https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/us-central1/endpoints/{ENDPOINT_ID[-1]}:predict \\\n",
+        "   https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint.name}:predict \\\n",
         "   -d \"@instances.json\""
       ]
     },

notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb

@@ -0,0 +1,933 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7fPc-KWUi2Xd"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2022 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eoXf8TfQoVth"
+      },
+      "source": [
+        "<table align=\"left\">\n",
+        "\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.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/training/xgboost_data_parallel_training_on_cpu_using_dask.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://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.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": "AksIKBzZ-nre"
+      },
+      "source": [
+        "# Create a distributed custom training job\n",
+        "## Overview\n",
+        "\n",
+        "This tutorial shows you how to create a distributed custom training job on Vertex AI that can handle large amounts of training data. \n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to create a distributed training job using Vertex AI SDK for Python. You build a custom docker container with simple Dask configuration to run a custom training job.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI SDK`\n",
+        "- `CustomContainerTrainingJob`\n",
+        "- `Artifact Registry`\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Configure the `PROJECT_ID` and `REGION` variables for your Google Cloud project.\n",
+        "- Create a Cloud Storage bucket to store your model artifacts.\n",
+        "- Build a custom Docker container that hosts your training code and push the container image to Artifact Registry.\n",
+        "- Run a Vertex AI SDK CustomContainerTrainingJob\n",
+        "\n",
+        "\n",
+        "### Data \n",
+        "\n",
+        "This tutorial uses the <a href=\"https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\">IRIS dataset</a>, which consists of different types of irises. \n",
+        "\n",
+        "### Costs\n",
+        " \n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "\n",
+        "* Cloud Storage\n",
+        "\n",
+        "* Artifact Registry\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [Artifact Registry](https://cloud.google.com/artifact-registry/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/),\n",
+        "        to generate a cost estimate based on your projected usage.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iMHz63rPbq6P"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install the packages required for executing this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b6f3dc43494b"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# The Vertex AI Workbench Notebook product has specific requirements\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
+        "\n",
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
+        "USER_FLAG = \"\"\n",
+        "if IS_WORKBENCH_NOTEBOOK:\n",
+        "    USER_FLAG = \"--user\"\n",
+        "\n",
+        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "64d24b4fab2c"
+      },
+      "source": [
+        "### 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": "O8AIwN0abq6U"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"IS_TESTING\"):\n",
+        "    # Restart the kernel after pip installs\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "before_you_begin:nogpu"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
+        "\n",
+        "3. [Enable the Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
+        "\n",
+        "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n",
+        "\n",
+        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
+        "Cloud SDK uses the right project for all the commands in this notebook.\n",
+        "\n",
+        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "set_project_id"
+      },
+      "outputs": [],
+      "source": [
+        "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "04933ed28eef"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mtMai39NpNtI"
+      },
+      "source": [
+        "#### UUID\n",
+        "\n",
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OX0_fa3TpOog"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import string\n",
+        "\n",
+        "\n",
+        "# Generate a uuid of a specifed length(default=8)\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h0SMyUsC-mzi"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated. Skip this step.\n",
+        "\n",
+        "**If you are using Colab**, run the cell below and follow the instructions\n",
+        "when prompted to authenticate your account via oAuth.\n",
+        "\n",
+        "**Otherwise**, follow these steps:\n",
+        "\n",
+        "1. In the Cloud Console, go to the [**Create service account key**\n",
+        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
+        "\n",
+        "2. Click **Create service account**.\n",
+        "\n",
+        "3. In the **Service account name** field, enter a name, and\n",
+        "   click **Create**.\n",
+        "\n",
+        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
+        "into the filter box, and select\n",
+        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+        "\n",
+        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
+        "local environment.\n",
+        "\n",
+        "6. Enter the path to your service account key as the\n",
+        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iTQY9g4mRo6r"
+      },
+      "outputs": [],
+      "source": [
+        "# 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 Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\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 ''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h0McDhrTpt-h"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "When you initialize the Vertex AI 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KqdXNlFQqr-u"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\"\n",
+        "\n",
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OxJaSKy7qymT"
+      },
+      "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": "8khtdIkVq0Ra"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil mb -l $REGION $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2PKfdM-Yq1vT"
+      },
+      "source": [
+        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "StOSeEKaq4-7"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil ls -al $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dax2zrpTi2Xy"
+      },
+      "source": [
+        "### Import libraries and define constants"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MjeuNztBrLmr"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xx_z9JQlrNwG"
+      },
+      "source": [
+        "# Create a custom training Python package \n",
+        "\n",
+        "Before you can perform local training, you must a create a training script file and a docker file.\n",
+        "\n",
+        "Create a `trainer` directory for all of your training code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-iYddwztr3g7"
+      },
+      "outputs": [],
+      "source": [
+        "PYTHON_PACKAGE_APPLICATION_DIR = \"trainer\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yjeHKqHwr4rV"
+      },
+      "outputs": [],
+      "source": [
+        "!mkdir -p $PYTHON_PACKAGE_APPLICATION_DIR"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ECBb7Lbqr9Hs"
+      },
+      "source": [
+        "### Write the Training Script"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5EStvMiC9tdS"
+      },
+      "source": [
+        "The `train.py` file checks whether the current node is the chief node or a worker node and runs `dask-scheduler` for the chief node and `dask-worker` for worker nodes. Worker nodes connect to the chief node through the IP address and port number specified in `CLUSTER_SPEC`.\n",
+        "\n",
+        "After the Dask scheduler is set up and connected to worker nodes, call `xgb.dask.train` to train a model through Dask. Once model training is complete, the model is uploaded to `AIP_MODEL_DIR`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "thNtAY2Gsx2h"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile trainer/train.py\n",
+        "from dask.distributed import Client, wait\n",
+        "from xgboost.dask import DaskDMatrix\n",
+        "from google.cloud import storage\n",
+        "import xgboost as xgb\n",
+        "import dask.dataframe as dd\n",
+        "import sys\n",
+        "import os\n",
+        "import subprocess\n",
+        "import time\n",
+        "import json\n",
+        "\n",
+        "IRIS_DATA_FILENAME = 'gs://cloud-samples-data/ai-platform/iris/iris_data.csv'\n",
+        "IRIS_TARGET_FILENAME = 'gs://cloud-samples-data/ai-platform/iris/iris_target.csv'\n",
+        "MODEL_FILE = 'model.bst'\n",
+        "MODEL_DIR = os.getenv(\"AIP_MODEL_DIR\")\n",
+        "XGB_PARAMS = {\n",
+        "    'verbosity': 2,\n",
+        "    'learning_rate': 0.1,\n",
+        "    'max_depth': 8,\n",
+        "    'objective': 'reg:squarederror',\n",
+        "    'subsample': 0.6,\n",
+        "    'gamma': 1,\n",
+        "    'verbose_eval': True,\n",
+        "    'tree_method': 'hist',\n",
+        "    'nthread': 1\n",
+        "}\n",
+        "\n",
+        "\n",
+        "def square(x):\n",
+        "    return x ** 2\n",
+        "\n",
+        "def neg(x):\n",
+        "    return -x\n",
+        "\n",
+        "def launch(cmd):\n",
+        "    \"\"\" launch dask workers\n",
+        "    \"\"\"\n",
+        "    return subprocess.check_call(cmd, stdout=sys.stdout, stderr=sys.stderr, shell=True)\n",
+        "\n",
+        "def get_chief_ip(cluster_config_dict):\n",
+        "    ip_address = cluster_config_dict['cluster']['workerpool0'][0].split(\":\")[0]\n",
+        "    print('The ip address of workerpool 0 is : {}'.format(ip_address))\n",
+        "    return ip_address\n",
+        "\n",
+        "def get_chief_port(cluster_config_dict):\n",
+        "    print(\"The open port is: {}\".format(cluster_config_dict['open_ports'][0]))\n",
+        "    return cluster_config_dict['open_ports'][0]\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "    cluster_config_str = os.environ.get('CLUSTER_SPEC')\n",
+        "    cluster_config_dict  = json.loads(cluster_config_str)\n",
+        "    print(json.dumps(cluster_config_dict, indent=2))\n",
+        "    print('The workerpool type is:', flush=True)\n",
+        "    print(cluster_config_dict['task']['type'], flush=True)\n",
+        "    workerpool_type = cluster_config_dict['task']['type']\n",
+        "    chief_ip = get_chief_ip(cluster_config_dict)\n",
+        "    chief_port = get_chief_port(cluster_config_dict)\n",
+        "    chief_address = \"{}:{}\".format(chief_ip, chief_port)\n",
+        "\n",
+        "    if workerpool_type == \"workerpool0\":\n",
+        "      print('Running the dask scheduler.', flush=True)\n",
+        "      proc_scheduler = launch('dask-scheduler --dashboard --dashboard-address 8888 --port {} &'.format(chief_port))\n",
+        "      print('Done the dask scheduler.', flush=True)\n",
+        "\n",
+        "      client = Client(chief_address)\n",
+        "      print('Waiting the scheduler to be connected.', flush=True)\n",
+        "      client.wait_for_workers(1)\n",
+        "\n",
+        "      X = dd.read_csv(IRIS_DATA_FILENAME, header=None)\n",
+        "      y = dd.read_csv(IRIS_TARGET_FILENAME, header=None)\n",
+        "      X.persist()\n",
+        "      y.persist()\n",
+        "      wait(X)\n",
+        "      wait(y)\n",
+        "      dtrain = DaskDMatrix(client, X, y)\n",
+        "      \n",
+        "      output = xgb.dask.train(client, XGB_PARAMS, dtrain,  num_boost_round=100, evals=[(dtrain, 'train')])\n",
+        "      print(\"Output: {}\".format(output), flush=True)\n",
+        "      print(\"Saving file to: {}\".format(MODEL_FILE), flush=True)\n",
+        "      output['booster'].save_model(MODEL_FILE)\n",
+        "      bucket_name = MODEL_DIR.replace(\"gs://\", \"\").split(\"/\", 1)[0]\n",
+        "      folder = MODEL_DIR.replace(\"gs://\", \"\").split(\"/\", 1)[1]\n",
+        "      bucket = storage.Client().bucket(bucket_name)\n",
+        "      print(\"Uploading file to: {}/{}{}\".format(bucket_name, folder, MODEL_FILE), flush=True)\n",
+        "      blob = bucket.blob('{}{}'.format(folder, MODEL_FILE))\n",
+        "      blob.upload_from_filename(MODEL_FILE)\n",
+        "      print(\"Saved file to: {}/{}\".format(MODEL_DIR, MODEL_FILE), flush=True)\n",
+        "\n",
+        "      client.shutdown()\n",
+        "\n",
+        "    else:\n",
+        "      print('Running the dask worker.', flush=True)\n",
+        "      client = Client(chief_address, timeout=1200)\n",
+        "      print('client: {}.'.format(client), flush=True)\n",
+        "      launch('dask-worker {}'.format(chief_address))\n",
+        "      print('Done with the dask worker.', flush=True)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MxsT4Vaos2W5"
+      },
+      "source": [
+        "### Write the docker file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xD60d6Q0i2X0"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile Dockerfile\n",
+        "FROM us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-9:latest\n",
+        "WORKDIR /root\n",
+        "\n",
+        "RUN apt-get update\n",
+        "RUN apt-get install -y telnet netcat iputils-ping  net-tools\n",
+        "RUN python3.8 -m pip install dask==2022.7.1 distributed==2022.7.1 bokeh==2.1.1 dask-cuda  --upgrade\n",
+        "RUN python3.8 -m pip install 'xgboost>=1.4.2' 'dask-ml[complete]==2022.5.27'  #'dask[complete]==2022.7,1' --upgrade\n",
+        "RUN python3.8 -m pip install gcsfs --upgrade\n",
+        "\n",
+        "\n",
+        "## Make sure gsutil will use the default service account\n",
+        "RUN echo '[GoogleCompute]\\nservice_account = default' > /etc/boto.cfg\n",
+        "\n",
+        "# Copies the trainer code\n",
+        "RUN mkdir /root/trainer\n",
+        "COPY trainer/train.py /root/trainer/train.py\n",
+        "\n",
+        "# Sets up the entry point to invoke the trainer.\n",
+        "ENTRYPOINT [\"python3.8\", \"trainer/train.py\"]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K6Yj8pZWAD7c"
+      },
+      "source": [
+        "## Create a custom training job\n",
+        "\n",
+        "### Build a custom training container\n",
+        "\n",
+        "#### Enable Artifact Registry API\n",
+        "You must enable the Artifact Registry API for your project. You will store your custom training container in Artifact Registry.\n",
+        "\n",
+        "<a href=\"https://cloud.google.com/artifact-registry/docs/enable-service\">Learn more about Enabling service</a>.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hd1j9BHeA81h"
+      },
+      "outputs": [],
+      "source": [
+        "! gcloud services enable artifactregistry.googleapis.com"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SMUUSWBgA_Mb"
+      },
+      "source": [
+        "### Create a private Docker repository\n",
+        "Your first step is to create a Docker repository in Artifact Registry.\n",
+        "\n",
+        "1 - Run the `gcloud artifacts repositories create` command to create a new Docker repository with your region with the description `docker repository`.\n",
+        "\n",
+        "2 - Run the `gcloud artifacts repositories list` command to verify that your repository was created."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0_csN1pAH95F"
+      },
+      "outputs": [],
+      "source": [
+        "PRIVATE_REPO = \"my-docker-repo\"\n",
+        "\n",
+        "! gcloud artifacts repositories create {PRIVATE_REPO} --repository-format=docker --location={REGION} --description=\"Docker repository\"\n",
+        "\n",
+        "! gcloud artifacts repositories list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KPpGuKi-BOAD"
+      },
+      "outputs": [],
+      "source": [
+        "DEPLOY_IMAGE = (\n",
+        "    f\"{REGION}-docker.pkg.dev/\" + PROJECT_ID + f\"/{PRIVATE_REPO}\" + \"/dask_support\"\n",
+        ")\n",
+        "print(\"Deployment:\", DEPLOY_IMAGE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_RgHDDL8BWgz"
+      },
+      "source": [
+        "## Authenticate Docker to your repository\n",
+        "### Configure authentication to your private repo\n",
+        "Before you can push or pull container images to or from your Artifact Registry repository, you must configure Docker to use the gcloud command-line tool to authenticate requests to Artifact Registry for your region. On Colab, you'll have to use Cloud Build as the docker command is not available,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GRLMyQwdKiLr"
+      },
+      "outputs": [],
+      "source": [
+        "if not IS_COLAB:\n",
+        "    ! gcloud auth configure-docker {REGION}-docker.pkg.dev --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XW4EecX1Bj8j"
+      },
+      "source": [
+        "### Set the custom Docker container image\n",
+        "Set the custom Docker container image.\n",
+        "\n",
+        "1. Pull the corresponding CPU or GPU Docker image from Docker Hub.\n",
+        "2. Create a tag for registering the image with Artifact Registry\n",
+        "3. Register the image with Artifact Registry."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z2XTaEDHB9HK"
+      },
+      "outputs": [],
+      "source": [
+        "if not IS_COLAB:\n",
+        "    ! docker build -t $DEPLOY_IMAGE -f Dockerfile .\n",
+        "    ! docker push $DEPLOY_IMAGE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3rzI1ZXQCA30"
+      },
+      "source": [
+        "## Build and push the custom docker container image by using Cloud Build\n",
+        "\n",
+        "Build and push a Docker image with Cloud Build"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AJMhP0kYCEgY"
+      },
+      "outputs": [],
+      "source": [
+        "if IS_COLAB:\n",
+        "    !  gcloud builds submit --timeout=1800s --region={REGION} --tag $DEPLOY_IMAGE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PtUycdZhCJvQ"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QKONAbwtCMgJ"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(\n",
+        "    project=PROJECT_ID,\n",
+        "    staging_bucket=BUCKET_URI,\n",
+        "    location=REGION,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_GB2j39BCXiy"
+      },
+      "source": [
+        "### Run a Vertex AI SDK CustomContainerTrainingJob"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aoEkXaaDepfo"
+      },
+      "outputs": [],
+      "source": [
+        "gcs_output_uri_prefix = f\"{BUCKET_URI}/output\"\n",
+        "replica_count = 2\n",
+        "machine_type = \"n1-standard-4\"\n",
+        "display_name = \"test_display_name\"\n",
+        "\n",
+        "custom_container_training_job = aiplatform.CustomContainerTrainingJob(\n",
+        "    display_name=display_name,\n",
+        "    model_serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest\",\n",
+        "    container_uri=DEPLOY_IMAGE,\n",
+        ")\n",
+        "\n",
+        "custom_container_training_job.run(\n",
+        "    base_output_dir=gcs_output_uri_prefix,\n",
+        "    replica_count=replica_count,\n",
+        "    machine_type=machine_type,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XpLMkJAzDTgx"
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Custom Training Job Name: {custom_container_training_job.resource_name}\")\n",
+        "print(f\"GCS Output URI Prefix: {gcs_output_uri_prefix}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SlsCCIFgDcGy"
+      },
+      "source": [
+        "### View training output artifact"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JjshJ2dcDdkQ"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil ls $gcs_output_uri_prefix/model/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c26XO4bZDnDH"
+      },
+      "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:\n",
+        "\n",
+        "- Cloud Storage Bucket"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ue3SfrMODunu"
+      },
+      "outputs": [],
+      "source": [
+        "# Set this to true only if you'd like to delete your bucket\n",
+        "delete_bucket = False\n",
+        "\n",
+        "! gsutil rm -rf $gcs_output_uri_prefix\n",
+        "\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil rm -r $BUCKET_URI"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "xgboost_data_parallel_training_on_cpu_using_dask.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/official/workbench/spark/spark_ml.ipynb

@@ -203,7 +203,9 @@
         "\n",
         "The Spark job executed in this notebook tutorial is compute intensive. Since the job can take a significant amount time to complete in a standard notebook environment, this notebook tutorial runs on a Dataproc cluster that is created with the Dataproc Component Gateway and Jupyter component installed on the cluster.\n",
         "\n",
-        "**Existing Dataproc with Jupyter cluster?**: If you have a running Dataproc cluster that has the [Component Gateway and Jupyter component installed on the cluster](https://cloud.google.com/dataproc/docs/concepts/components/jupyter#gcloud-command), you can use it in this tutorial. If you plan to use it, skip this step, and go to `Switch your kernel`."
+        "**Existing Dataproc with Jupyter cluster?**: If you have a running Dataproc cluster that has the [Component Gateway and Jupyter component installed on the cluster](https://cloud.google.com/dataproc/docs/concepts/components/jupyter#gcloud-command), you can use it in this tutorial. If you plan to use it, skip this step, and go to `Switch your kernel`.\n",
+        "\n",
+        "Set and name and [compute region](https://cloud.google.com/compute/docs/regions-zones#available) for your new cluster. Your `CLUSTER_NAME` must be **unique within your Google Cloud project**. It must start with a lowercase letter, followed by up to 51 lowercase letters, numbers, and hyphens, and cannot end with a hyphen."
       ]
     },
     {
@@ -226,15 +228,6 @@
         "    print(f\"CLUSTER_REGION: {CLUSTER_REGION}\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XoEqT2Y4DJmf"
-      },
-      "source": [
-        "Your `CLUSTER_NAME` must be **unique within your Google Cloud project**. It must start with a lowercase letter, followed by up to 51 lowercase letters, numbers, and hyphens, and cannot end with a hyphen."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -488,6 +481,15 @@
         "from shapely.geometry import Point"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "71ecd729c4b9"
+      },
+      "source": [
+        "**Note**: After importing libraries, if you see `ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/miniconda3/share/proj failed`, you can ignore it. This is due to a bug with a `geopandas` dependency.\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -519,7 +521,7 @@
         "\n",
         "# Initialize the SparkSession.\n",
         "spark = (\n",
-        "    SparkSession.builder.appName(\"spark-bigquery-polyglot-language-demo\")\n",
+        "    SparkSession.builder.appName(\"spark-ml-taxi-citibike\")\n",
         "    .config(\"spark.jars\", connector)\n",
         "    .getOrCreate()\n",
         ")"
@@ -552,7 +554,7 @@
         ")\n",
         "\n",
         "\n",
-        "# Load NYC_Citibike in Github Acitivity Public dataset from BQ.\n",
+        "# Load NYC_Citibike in Github Activity Public dataset from BQ.\n",
         "bike_df = (\n",
         "    spark.read.format(\"bigquery\")\n",
         "    .option(\"table\", \"bigquery-public-data.new_york_citibike.citibike_trips\")\n",
@@ -1553,6 +1555,8 @@
       },
       "outputs": [],
       "source": [
+        "import os\n",
+        "\n",
         "if not os.getenv(\"IS_TESTING\"):\n",
         "    ! gcloud dataproc clusters delete $CLUSTER_NAME --region=$CLUSTER_REGION -q"
       ]