fix: lint

fix: rename UJ4
fix: pin kfp and gcpc < 2.0 in rapid_prototyping_bqml_automl (#1755 )
2026-09-26 14:42:04 +00:00 · 2023-04-20 04:54:38 +00:00 · 2023-04-20 04:46:37 +00:00 · 2023-04-20 04:36:39 +00:00 · 2023-04-19 23:01:21 +00:00 · 2023-04-19 19:29:50 +00:00
60 changed files with 8667 additions and 466 deletions
@@ -59,7 +59,7 @@ managers: List[ResourceCleanupManager] = [
    TrainingJobCleanupManager(),
    HyperparameterTuningCleanupManager(),
    BatchPredictionJobCleanupManager(),
-    # ExperimentCleanupManager(), # Experiment missing _resource_noun
+    ExperimentCleanupManager(), # Experiment missing _resource_noun
    BucketCleanupManager()
 ]

@@ -131,6 +131,9 @@ class MatchingEngineIndexEndpointResourceCleanupManager(VertexAIResourceCleanupM
 class FeatureStoreCleanupManager(VertexAIResourceCleanupManager):
    vertex_ai_resource = aiplatform.Featurestore

+    def resource_name(self, resource: Any) -> str:
+        return resource.name
+
 class PipelineJobCleanupManager(VertexAIResourceCleanupManager):
    vertex_ai_resource = aiplatform.PipelineJob

@@ -166,6 +169,18 @@ class BatchPredictionJobCleanupManager(VertexAIResourceCleanupManager):
 class ExperimentCleanupManager(VertexAIResourceCleanupManager):
    vertex_ai_resource = aiplatform.Experiment

+    @property
+    def type_name(self) -> str:
+        return "Experiment"
+
+    def resource_name(self, resource: Any) -> str:
+        return resource.name
+
+    def get_seconds_since_modification(self, resource: Any) -> float:
+        update_time = resource._metadata_context.update_time
+        current_time = DatetimeWithNanoseconds.now()
+        return float(current_time.timestamp() - update_time.timestamp())
+
 class BucketCleanupManager(ResourceCleanupManager):
    vertex_ai_resource = storage.bucket.Bucket

@@ -181,7 +196,7 @@ class BucketCleanupManager(ResourceCleanupManager):

    @property
    def type_name(self) -> str:
-        return str(type(self.vertex_ai_resource))
+        return "Bucket"

    def get_seconds_since_modification(self, resource: Any) -> float:
        # Bucket has no last_update property, only time created
@@ -195,7 +210,7 @@ class BucketCleanupManager(ResourceCleanupManager):
    def is_deletable(self, resource: Any) -> bool:
        time_difference = self.get_seconds_since_modification(resource)

-        if self.resource_name(resource).startswith('your-bucket-name'):
+        if not self.resource_name(resource).startswith('your-bucket-name'):
            print(f"Skipping '{resource}' not a Vertex AI notebook bucket")
            return False

@@ -55,3 +55,4 @@
 /notebooks/community/model_garden/model_garden_pytorch_clip.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_pytorch_owlvit.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.ipynb @xiangxu-google
+/notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb @xiangxu-google
@@ -29,22 +29,22 @@
        "id": "title:generic,gcp"
      },
      "source": [
-        "# E2E ML on GCP: MLOps stage 4 : formalization: get started with Vertex ML Metadata\n",
+        "# Get started with Vertex ML Metadata\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_vertex_ml_metadata.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/get_started_with_vertex_ml_metadata.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/ml_ops/stage4/get_started_with_vertex_ml_metadata.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/get_started_with_vertex_ml_metadata.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/ml_ops/stage4/get_started_with_vertex_ml_metadata.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/get_started_with_vertex_ml_metadata.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 +62,9 @@
        "## 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 ML Metadata."
+        "This tutorial demonstrates how to use Vertex ML Metadata.\n",
+        "\n",
+        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata)."
      ]
    },
    {
@@ -144,19 +146,32 @@
      "source": [
        "import os\n",
        "\n",
-        "# The Vertex AI Workbench Notebook product has specific requirements\n",
-        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
-        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
-        "    \"/opt/deeplearning/metadata/env_version\"\n",
-        ")\n",
+        "! pip3 install --upgrade google-cloud-aiplatform[tensorboard] \\\n",
+        "                         google-cloud-pipeline-components  --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "restart"
+      },
+      "source": [
+        "### Colab only: Uncomment the following cell to restart the kernel"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D-ZBOjErv5mM"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
        "\n",
-        "# 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[tensorboard] $USER_FLAG -q\n",
-        "! pip3 install --upgrade google-cloud-pipeline-components $USER_FLAG -q"
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
      ]
    },
    {
@@ -170,63 +185,27 @@
        "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": "restart"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    # Automatically restart kernel after installs\n",
-        "    import IPython\n",
-        "\n",
-        "    app = IPython.Application.instance()\n",
-        "    app.kernel.do_shutdown(True)"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "before_you_begin"
      },
      "source": [
-        "## Before you begin\n",
-        "\n",
-        "### GPU runtime\n",
-        "\n",
-        "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage-component.googleapis.com)\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
-        "\n",
-        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
+        "## Before you begin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "project_id"
+        "id": "before_you_begin:nogpu"
      },
      "source": [
-        "#### Set your project ID\n",
+        "### 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`."
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
      ]
    },
    {
@@ -237,33 +216,10 @@
      },
      "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"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
@@ -274,16 +230,7 @@
      "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)."
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
      ]
    },
    {
@@ -294,34 +241,7 @@
      },
      "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\")"
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
@@ -332,57 +252,70 @@
      "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",
-        "**Click Create service account**.\n",
-        "\n",
-        "In the **Service account name** field, enter a name, and click **Create**.\n",
-        "\n",
-        "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "Click Create. A JSON file that contains your key downloads to your local environment.\n",
-        "\n",
-        "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FvQeFm3Gv5mR"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ad1138a125ea"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "gcp_authenticate"
+        "id": "ce6043da7b33"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0367eac06a10"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "21ad4dbb4a61"
      },
      "outputs": [],
      "source": [
-        "# 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 ''"
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()\n",
+        "# IS_COLAB = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c13224697bfb"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
      ]
    },
    {
@@ -393,11 +326,7 @@
      "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 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."
+        "Create a storage bucket to store intermediate artifacts such as datasets."
      ]
    },
    {
@@ -408,21 +337,7 @@
      },
      "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"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -445,26 +360,6 @@
        "! 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"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -545,19 +440,7 @@
      "source": [
        "### Set up variables\n",
        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
-        "### Import libraries and define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "import google.cloud.aiplatform as aip"
+        "Next, set up some variables used throughout the tutorial."
      ]
    },
    {
@@ -579,7 +462,10 @@
      },
      "outputs": [],
      "source": [
-        "import google.cloud.aiplatform_v1beta1 as aip_beta"
+        "from datetime import datetime\n",
+        "\n",
+        "import google.cloud.aiplatform_v1beta1 as aip_beta\n",
+        "from google.cloud import aiplatform"
      ]
    },
    {
@@ -707,7 +593,7 @@
      "outputs": [],
      "source": [
        "metadata_store = clients[\"metadata\"].create_metadata_store(\n",
-        "    parent=PARENT, metadata_store_id=\"my-metadata-store\"\n",
+        "    parent=PARENT, metadata_store_id=\"my-metadata-store-unique\"\n",
        ")\n",
        "\n",
        "metadata_store_id = str(metadata_store.result())[7:-2]\n",
@@ -1144,7 +1030,7 @@
      "source": [
        "from kfp.v2 import compiler, dsl\n",
        "from kfp.v2.dsl import (Artifact, Dataset, Input, Metrics, Model, Output,\n",
-        "                        OutputPath, component, pipeline)"
+        "                        OutputPath, component)"
      ]
    },
    {
@@ -1196,7 +1082,7 @@
      "outputs": [],
      "source": [
        "@component(\n",
-        "    packages_to_install=[\"google-cloud-bigquery\", \"pandas\", \"pyarrow\"],\n",
+        "    packages_to_install=[\"google-cloud-bigquery\", \"pandas\", \"pyarrow\", \"db-dtypes\"],\n",
        "    base_image=\"python:3.9\",\n",
        "    output_component_file=\"create_dataset.yaml\",\n",
        ")\n",
@@ -1214,7 +1100,7 @@
        "\n",
        "\n",
        "@component(\n",
-        "    packages_to_install=[\"sklearn\", \"pandas\", \"joblib\"],\n",
+        "    packages_to_install=[\"scikit-learn\", \"pandas\", \"joblib\"],\n",
        "    base_image=\"python:3.9\",\n",
        "    output_component_file=\"beans_model_component.yaml\",\n",
        ")\n",
@@ -1298,7 +1184,7 @@
        "    # A name for the pipeline.\n",
        "    name=\"mlmd-pipeline\",\n",
        ")\n",
-        "def pipeline(\n",
+        "def my_pipeline(\n",
        "    bq_table: str = \"\",\n",
        "    output_data_path: str = \"data.csv\",\n",
        "    project: str = PROJECT_ID,\n",
@@ -1332,20 +1218,22 @@
      "source": [
        "NOW = datetime.now().isoformat().replace(\".\", \":\")[:-7]\n",
        "\n",
-        "compiler.Compiler().compile(pipeline_func=pipeline, package_path=\"mlmd_pipeline.json\")\n",
+        "compiler.Compiler().compile(\n",
+        "    pipeline_func=my_pipeline, package_path=\"mlmd_pipeline.json\"\n",
+        ")\n",
        "\n",
-        "run1 = aip.PipelineJob(\n",
+        "run1 = aiplatform.PipelineJob(\n",
        "    display_name=\"mlmd-pipeline\",\n",
        "    template_path=\"mlmd_pipeline.json\",\n",
-        "    job_id=\"mlmd-pipeline-small-{}\".format(TIMESTAMP),\n",
+        "    job_id=\"mlmd-pipeline-small-unique\",\n",
        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\"},\n",
        "    enable_caching=True,\n",
        ")\n",
        "\n",
-        "run2 = aip.PipelineJob(\n",
+        "run2 = aiplatform.PipelineJob(\n",
        "    display_name=\"mlmd-pipeline\",\n",
        "    template_path=\"mlmd_pipeline.json\",\n",
-        "    job_id=\"mlmd-pipeline-large-{}\".format(TIMESTAMP),\n",
+        "    job_id=\"mlmd-pipeline-large-unique\",\n",
        "    parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\"},\n",
        "    enable_caching=True,\n",
        ")\n",
@@ -1382,7 +1270,7 @@
      },
      "outputs": [],
      "source": [
-        "df = aip.get_pipeline_df(pipeline=\"mlmd-pipeline\")\n",
+        "df = aiplatform.get_pipeline_df(pipeline=\"mlmd-pipeline\")\n",
        "print(df)"
      ]
    },
@@ -1466,6 +1354,10 @@
      },
      "outputs": [],
      "source": [
+        "metadata_store_id = (\n",
+        "    f\"projects/{PROJECT_ID}/locations/{REGION}/metadataStores/my-metadata-store-unique\"\n",
+        ")\n",
+        "\n",
        "clients[\"metadata\"].delete_metadata_store(name=metadata_store_id)"
      ]
    },
@@ -0,0 +1,468 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7d9bbf86da5e"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2023 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2bd716bf3e39"
+      },
+      "source": [
+        "# Vertex AI Model Garden - BLIP2\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_blip2.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8cd12648da4"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates deploying the pre-trained [BLIP2](https://huggingface.co/Salesforce/blip2-opt-2.7b) model on Vertex AI for online prediction.\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
+        "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
+        "- Run online predictions for image captioning.\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "264c07757582"
+      },
+      "source": [
+        "## Setup environment\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": "d73ffa0c0b83"
+      },
+      "source": [
+        "### Colab only"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2707b02ef5df"
+      },
+      "outputs": [],
+      "source": [
+        "!pip3 install --upgrade google-cloud-aiplatform"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b60a4d7100bf"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import auth as google_auth\n",
+        "\n",
+        "google_auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0f826ff482a2"
+      },
+      "source": [
+        "### Setup Google Cloud project\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
+        "\n",
+        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8958ebc71868"
+      },
+      "source": [
+        "Fill following variables for experiments environment:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9db30f827a65"
+      },
+      "outputs": [],
+      "source": [
+        "# Cloud project id.\n",
+        "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# The region you want to launch jobs in.\n",
+        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
+        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92f16e22c20b"
+      },
+      "source": [
+        "Initialize Vertex AI API:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1680c257acfb"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform\n",
+        "\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=GCS_BUCKET)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ca48b699d17"
+      },
+      "source": [
+        "### Define constants"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "de9882ea89ea"
+      },
+      "outputs": [],
+      "source": [
+        "# The pre-built serving docker image.\n",
+        "# The model artifacts are embedded within the container, except for model weights which will be downloaded during deployment.\n",
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "10188266a5cd"
+      },
+      "source": [
+        "### Define common functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cac4478ae098"
+      },
+      "outputs": [],
+      "source": [
+        "import base64\n",
+        "import os\n",
+        "from datetime import datetime\n",
+        "from io import BytesIO\n",
+        "\n",
+        "import requests\n",
+        "from google.cloud import aiplatform\n",
+        "from PIL import Image\n",
+        "\n",
+        "\n",
+        "def create_job_name(prefix):\n",
+        "    user = os.environ.get(\"USER\")\n",
+        "    now = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+        "    job_name = f\"{prefix}-{user}-{now}\"\n",
+        "    return job_name\n",
+        "\n",
+        "\n",
+        "def download_image(url):\n",
+        "    response = requests.get(url)\n",
+        "    return Image.open(BytesIO(response.content))\n",
+        "\n",
+        "\n",
+        "def image_to_base64(image, format=\"JPEG\"):\n",
+        "    buffer = BytesIO()\n",
+        "    image.save(buffer, format=format)\n",
+        "    image_str = base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n",
+        "    return image_str\n",
+        "\n",
+        "\n",
+        "def base64_to_image(image_str):\n",
+        "    image = Image.open(BytesIO(base64.b64decode(image_str)))\n",
+        "    return image\n",
+        "\n",
+        "\n",
+        "def image_grid(imgs, rows=2, cols=2):\n",
+        "    w, h = imgs[0].size\n",
+        "    grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n",
+        "    for i, img in enumerate(imgs):\n",
+        "        grid.paste(img, box=(i % cols * w, i // cols * h))\n",
+        "    return grid\n",
+        "\n",
+        "\n",
+        "def deploy_model(model_id, task):\n",
+        "    model_name = \"blip-image-captioning\"\n",
+        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "    serving_env = {\n",
+        "        \"MODEL_ID\": model_id,\n",
+        "        \"TASK\": task,\n",
+        "    }\n",
+        "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
+        "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
+        "    model = aiplatform.Model.upload(\n",
+        "        display_name=model_name,\n",
+        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
+        "        serving_container_ports=[7080],\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
+        "        serving_container_health_route=\"/ping\",\n",
+        "        serving_container_environment_variables=serving_env,\n",
+        "        artifact_uri=artifact_uri,\n",
+        "    )\n",
+        "    model.deploy(\n",
+        "        endpoint=endpoint,\n",
+        "        machine_type=\"n1-standard-8\",\n",
+        "        accelerator_type=\"NVIDIA_TESLA_T4\",\n",
+        "        accelerator_count=1,\n",
+        "        deploy_request_timeout=1800,\n",
+        "    )\n",
+        "    return model, endpoint"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d2d72ecdb8c9"
+      },
+      "source": [
+        "## Upload and deploy models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9448c5f545fa"
+      },
+      "source": [
+        "This section uploads the pre-trained model to Model Registry and deploys it on the Endpoint with 1 T4 GPU.\n",
+        "\n",
+        "The model deployment step will take ~15 minutes to complete.\n",
+        "\n",
+        "Once deployed, you can send images to get descriptions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "19e8aeec969c"
+      },
+      "source": [
+        "### Image captioning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b4b46c28d8b1"
+      },
+      "outputs": [],
+      "source": [
+        "model, endpoint = deploy_model(\n",
+        "    model_id=\"Salesforce/blip2-opt-2.7b\", task=\"image-to-text\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "12893aa2c5af"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. When the model is very large it could add 5~15mins additional time before the endpoint is ready for prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6be655247cb1"
+      },
+      "outputs": [],
+      "source": [
+        "image = download_image(\"http://images.cocodataset.org/val2017/000000039769.jpg\")\n",
+        "display(image)\n",
+        "\n",
+        "instances = [\n",
+        "    {\"image\": image_to_base64(image)},\n",
+        "]\n",
+        "preds = endpoint.predict(instances=instances).predictions\n",
+        "print(preds)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ccf3714dbe9"
+      },
+      "outputs": [],
+      "source": [
+        "# Undeploy model and delete endpoint.\n",
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "01e11d85d504"
+      },
+      "source": [
+        "### VQA (Visual-Question-Answering)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "26018d961cf9"
+      },
+      "outputs": [],
+      "source": [
+        "model, endpoint = deploy_model(\n",
+        "    model_id=\"Salesforce/blip2-opt-2.7b\", task=\"visual-question-answering\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0ac7f8d945e3"
+      },
+      "source": [
+        "NOTE: The model weights will be downloaded after the deployment succeeds. When the model is very large it could add 5~15mins additional time before the endpoint is ready for prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f19c342829fd"
+      },
+      "outputs": [],
+      "source": [
+        "image = download_image(\n",
+        "    \"https://media.newyorker.com/cartoons/63dc6847be24a6a76d90eb99/master/w_1160,c_limit/230213_a26611_838.jpg\"\n",
+        ")\n",
+        "display(image)\n",
+        "\n",
+        "question = \"Question: What are they doing? Answer:\"\n",
+        "instances = [\n",
+        "    {\"image\": image_to_base64(image), \"text\": question},\n",
+        "]\n",
+        "preds = endpoint.predict(instances=instances).predictions\n",
+        "print(question)\n",
+        "print(preds)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "712eb9d0b336"
+      },
+      "outputs": [],
+      "source": [
+        "# Undeploy model and delete endpoint.\n",
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_pytorch_blip2.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip_image_captioning.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip_image_captioning.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_blip_image_captioning.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -367,11 +367,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip_vqa.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_blip_vqa.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_blip_vqa.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -369,11 +369,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_clip.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_clip.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_clip.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -370,11 +370,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_controlnet.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_controlnet.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_controlnet.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -257,7 +257,7 @@
        "TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-train:latest\"\n",
        "\n",
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve\""
      ]
    },
    {
@@ -599,11 +599,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve\""
      ]
    },
    {
@@ -392,11 +392,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_layoutml_document_qa.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -246,7 +246,7 @@
        "\n",
        "def download_image(url):\n",
        "    response = requests.get(url)\n",
-        "    return Image.open(BytesIO(response.content))\n",
+        "    return Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
        "\n",
        "\n",
        "def image_to_base64(image, format=\"JPEG\"):\n",
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -371,11 +371,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_owlvit.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_owlvit.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_owlvit.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -232,6 +232,8 @@
        "from datetime import datetime\n",
        "from io import BytesIO\n",
        "\n",
+        "import matplotlib.patches as patches\n",
+        "import matplotlib.pyplot as plt\n",
        "import requests\n",
        "from google.cloud import aiplatform\n",
        "from PIL import Image\n",
@@ -269,6 +271,23 @@
        "    return grid\n",
        "\n",
        "\n",
+        "def draw_image_with_boxes(image, boxes):\n",
+        "    fig, ax = plt.subplots()\n",
+        "    plt.axis(\"off\")\n",
+        "    ax.imshow(image)\n",
+        "    if len(boxes) == 0:\n",
+        "        return\n",
+        "    boxes = boxes[\"boxes\"]\n",
+        "    for box in boxes:\n",
+        "        x, y = box[\"xmin\"], box[\"ymin\"]\n",
+        "        width, height = box[\"xmax\"] - x, box[\"ymax\"] - y\n",
+        "        rect = patches.Rectangle(\n",
+        "            (x, y), width, height, linewidth=2, edgecolor=\"yellow\", facecolor=\"none\"\n",
+        "        )\n",
+        "        ax.add_patch(rect)\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
        "def deploy_model(model_id, task):\n",
        "    model_name = \"owl-vit\"\n",
        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
@@ -282,7 +301,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -346,7 +365,7 @@
        "    {\"image\": image_to_base64(image), \"text\": \"cat\"},\n",
        "]\n",
        "preds = endpoint.predict(instances=instances).predictions\n",
-        "draw_image_with_boxes(image, preds[0][\"boxes\"])\n",
+        "draw_image_with_boxes(image, preds[0])\n",
        "print(preds)"
      ]
    },
@@ -367,11 +386,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -257,7 +257,7 @@
        "TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-train:latest\"\n",
        "\n",
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve\""
      ]
    },
    {
@@ -546,11 +546,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    },
    {
@@ -625,11 +625,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -257,7 +257,7 @@
        "TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-train:latest\"\n",
        "\n",
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-diffusers-serve\""
      ]
    },
    {
@@ -554,11 +554,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,18 +37,18 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_vilt_vqa.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>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_vilt_vqa.ipynb\">\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_vilt_vqa.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -369,11 +369,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -37,7 +37,6 @@
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
-        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_vit_gpt2_image_captioning.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
@@ -45,10 +44,11 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>                                                                                               <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_vit_gpt2_image_captioning.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_vit_gpt2_image_captioning.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
+        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
@@ -207,7 +207,7 @@
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
-        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve:latest\""
+        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
@@ -282,7 +282,7 @@
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
-        "        serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
+        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
@@ -367,11 +367,11 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
-        "\n",
        "# Undeploy model and delete endpoint.\n",
-        "endpoint.delete(force=True)"
+        "endpoint.delete(force=True)\n",
+        "\n",
+        "# Delete models.\n",
+        "model.delete()"
      ]
    }
  ],
@@ -405,7 +405,7 @@
        "                checkpoint_path = os.path.relpath(checkpoint_path, checkpoint_name)\n",
        "                break\n",
        "\n",
-        "    ! gsutil cp -r $checkpoint_name $CHECKPOINT_BUCKET\n",
+        "    ! gsutil cp -r $checkpoint_name $CHECKPOINT_BUCKET/\n",
        "    checkpoint_uri = os.path.join(CHECKPOINT_BUCKET, checkpoint_name, checkpoint_path)\n",
        "    print(\"Checkpoint uploaded to\", checkpoint_uri)\n",
        "    return checkpoint_uri"
@@ -562,7 +562,7 @@
        "        **{\n",
        "            \"experiment\": \"resnet_imagenet\",\n",
        "            \"config_file\": os.path.join(CONFIG_DIR, \"imagenet_resnet50_gpu.yaml\"),\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"ResNet-RS-50\": dict(\n",
        "        common_args,\n",
@@ -573,13 +573,13 @@
        "            ),\n",
        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/resnet-rs/resnet-rs-50-i160.tar.gz\",\n",
        "            \"input_size\": \"160,160\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"Efficientnetv2-m\": dict(\n",
        "        common_args,\n",
        "        **{\n",
        "            \"experiment\": \"hub_model\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"ViT-ti16\": dict(\n",
        "        common_args,\n",
@@ -588,7 +588,7 @@
        "            \"model_name\": \"vit-ti16\",\n",
        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-ti16.tar.gz\",\n",
        "            \"input_size\": \"224,224\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"ViT-s16\": dict(\n",
        "        common_args,\n",
@@ -597,7 +597,7 @@
        "            \"model_name\": \"vit-s16\",\n",
        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-s16.tar.gz\",\n",
        "            \"input_size\": \"224,224\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"ViT-b16\": dict(\n",
        "        common_args,\n",
@@ -606,7 +606,7 @@
        "            \"model_name\": \"vit-b16\",\n",
        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-b16.tar.gz\",\n",
        "            \"input_size\": \"224,224\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    \"ViT-l16\": dict(\n",
        "        common_args,\n",
@@ -615,7 +615,7 @@
        "            \"model_name\": \"vit-l16\",\n",
        "            \"init_checkpoint\": \"https://storage.googleapis.com/tf_model_garden/vision/vit/vit-deit-imagenet-l16.tar.gz\",\n",
        "            \"input_size\": \"224,224\",\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "}\n",
        "experiment_container_args = experiment_container_args_dict[experiment]\n",
@@ -877,9 +877,12 @@
        "# Delete models.\n",
        "model.delete()\n",
        "# Delete custom and hpt jobs.\n",
-        "data_converter_custom_job.delete()\n",
-        "train_hpt_job.delete()\n",
-        "model_export_custom_job.delete()"
+        "if data_converter_custom_job.list(filter=f'display_name=\"{data_converter_job_name}\"'):\n",
+        "    data_converter_custom_job.delete()\n",
+        "if train_hpt_job.list(filter=f'display_name=\"{train_job_name}\"'):\n",
+        "    train_hpt_job.delete()\n",
+        "if model_export_custom_job.list(filter=f'display_name=\"{model_export_name}\"'):\n",
+        "    model_export_custom_job.delete()"
      ]
    }
  ],
@@ -501,7 +501,7 @@
        "* `input_file_type`: The input file type, such as csv or jsonl.\n",
        "* `split_ratio`: The proportion of data to split into train/validation/test.\n",
        "* `num_shard`: The number of shards for train/validation/test.\n",
-        "* `output_dir`: The output directory, which will container prepared train/test/validation data."
+        "* `output_dir`: The output directory, which will contain prepared train/test/validation data."
      ]
    },
    {
@@ -519,7 +519,7 @@
        ")\n",
        "\n",
        "input_file_path = \"\"  # @param {type:\"string\"}\n",
-        "input_file_type = \"\"  # @param {type:\"string\"}\n",
+        "input_file_type = \"csv\"  # @param ['csv', 'jsonl', 'coco_json']\n",
        "split_ratio = \"0.8,0.1,0.1\"\n",
        "num_shard = \"10,10,10\"\n",
        "data_converter_output_dir = os.path.join(BUCKET_URI, data_converter_job_name)\n",
@@ -582,7 +582,7 @@
      "source": [
        "#### Define the following specifications\n",
        "\n",
-        "* `worker_pool_specs`: Dictionary specifying the machine type and Docker image. This example defines a single node cluster with one `n1-standard-4` machine with two `NVIDIA_TESLA_T4` GPUs.\n",
+        "* `worker_pool_specs`: Dictionary specifying the machine type and Docker image. This example defines a single node cluster with one `n1-highmem-16` machine with two `NVIDIA_TESLA_V100` GPUs.\n",
        "* `parameter_spec`: Dictionary specifying the parameters to optimize. The dictionary key is the string assigned to the command line argument for each hyperparameter in your training application code, and the dictionary value is the parameter specification. The parameter specification includes the type, min/max values, and scale for the hyperparameter.\n",
        "* `metric_spec`: Dictionary specifying the metric to optimize. The dictionary key is the `hyperparameter_metric_tag` that you set in your training application code, and the value is the optimization goal."
      ]
@@ -635,7 +635,7 @@
        "            \"experiment\": \"retinanet_spinenet_coco\",\n",
        "            \"config_file\": TRAIN_SPINENET49_CONFIG,\n",
        "            \"anchor_size\": 4,\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    # retinanet_spinenet96 experiment args.\n",
        "    \"retinanet_spinenet96\": dict(\n",
@@ -644,16 +644,16 @@
        "            \"experiment\": \"retinanet_spinenet_coco\",\n",
        "            \"config_file\": TRAIN_SPINENET96_CONFIG,\n",
        "            \"anchor_size\": 4,\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    # retinanet_spinenet143 experiment args.\n",
        "    \"retinanet_spinenet143\": dict(\n",
        "        common_args,\n",
        "        **{\n",
        "            \"experiment\": \"retinanet_spinenet_coco\",\n",
-        "            \"config_file\": TRAIN_SPINENET96_CONFIG,\n",
+        "            \"config_file\": TRAIN_SPINENET143_CONFIG,\n",
        "            \"anchor_size\": 4,\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "    # scaled_yolo_v4 experiment args.\n",
        "    \"scaled_yolo_v4\": dict(\n",
@@ -661,7 +661,7 @@
        "        **{\n",
        "            \"experiment\": \"scaled_yolo\",\n",
        "            \"config_file\": TRAIN_YOLOV4_CONFIG,\n",
-        "        }\n",
+        "        },\n",
        "    ),\n",
        "}\n",
        "\n",
@@ -946,14 +946,17 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
        "# Undeploy model and delete endpoint.\n",
        "endpoint.delete(force=True)\n",
+        "# Delete models.\n",
+        "model.delete()\n",
        "# Delete custom and hpt jobs.\n",
-        "data_converter_custom_job.delete()\n",
-        "train_hpt_job.delete()\n",
-        "model_export_custom_job.delete()"
+        "if data_converter_custom_job.list(filter=f'display_name=\"{data_converter_job_name}\"'):\n",
+        "    data_converter_custom_job.delete()\n",
+        "if train_hpt_job.list(filter=f'display_name=\"{train_job_name}\"'):\n",
+        "    train_hpt_job.delete()\n",
+        "if model_export_custom_job.list(filter=f'display_name=\"{model_export_name}\"'):\n",
+        "    model_export_custom_job.delete()"
      ]
    }
  ],
@@ -792,6 +792,8 @@
        "                \"--config_file=%s/params.yaml\" % best_trial_dir,\n",
        "                \"--checkpoint_path=%s/best_ckpt\" % best_trial_dir,\n",
        "                \"--export_dir=%s/best_model\" % model_dir,\n",
+        "                \"--input_image_size=%s\"\n",
+        "                % experiment_container_args_dict[experiment][\"output_size\"],\n",
        "            ],\n",
        "        },\n",
        "    }\n",
@@ -915,14 +917,17 @@
      },
      "outputs": [],
      "source": [
-        "# Delete models.\n",
-        "model.delete()\n",
        "# Undeploy model and delete endpoint.\n",
        "endpoint.delete(force=True)\n",
+        "# Delete models.\n",
+        "model.delete()\n",
        "# Delete custom and hpt jobs.\n",
-        "data_converter_custom_job.delete()\n",
-        "train_hpt_job.delete()\n",
-        "model_export_custom_job.delete()"
+        "if data_converter_custom_job.list(filter=f'display_name=\"{data_converter_job_name}\"'):\n",
+        "    data_converter_custom_job.delete()\n",
+        "if train_hpt_job.list(filter=f'display_name=\"{train_job_name}\"'):\n",
+        "    train_hpt_job.delete()\n",
+        "if model_export_custom_job.list(filter=f'display_name=\"{model_export_name}\"'):\n",
+        "    model_export_custom_job.delete()"
      ]
    }
  ],
@@ -162,13 +162,7 @@
      "outputs": [],
      "source": [
        "# Install the packages\n",
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    USER = \"--user\"\n",
-        "else:\n",
-        "    USER = \"\"\n",
-        "! pip3 install {USER} --upgrade google-cloud-aiplatform"
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform"
      ]
    },
    {
@@ -359,7 +353,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = \"gs://your-bucket-name-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -40,4 +40,5 @@
 /model_evaluation/custom_tabular_classification_model_evaluation.ipynb @soheilazangeneh
 /sdk/SDK_FBProphet_Forecasting_Online.ipynb @brianchunkang
 /pipelines/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines @Ark-kun
-/experiments/get_started_with_vertex_experiments_autologging.ipynb @inardini
+/experiments/get_started_with_vertex_experiments_autologging.ipynb @inardini
+/experiments/delete_outdated_tensorboard_experiments.ipynb @inardini
@@ -325,7 +325,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -319,7 +319,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -327,7 +327,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -329,7 +329,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -242,22 +242,7 @@
      "source": [
        "## Notes about service account and permission\n",
        "\n",
-        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts above have the required roles:\n",
-        "\n",
-        "|Service account email|Description|Roles|\n",
-        "|---|---|---|\n",
-        "|PROJECT_NUMBER-compute@developer.gserviceaccount.com|Compute Engine default service account|Dataflow Developer, Dataflow Worker, Storage Admin, BigQuery Data Editor, Vertex AI User, Service Account User|\n",
-        "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
-        "\n",
-        "\n",
-        "1. Goto https://console.cloud.google.com/iam-admin/iam.\n",
-        "2. Check the \"Include Google-provided role grants\" checkbox.\n",
-        "3. Find the above emails.\n",
-        "4. Grant the corresponding roles.\n",
-        "\n",
-        "### Using data source from a different project\n",
-        "- For the BQ data source, grant both service accounts the \"BigQuery Data Viewer\" role.\n",
-        "- For the CSV data source, grant both service accounts the \"Storage Object Viewer\" role.\n"
+        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts have the required roles listed in the [Service accounts for Tabular Workflow for End-to-End AutoML documentation](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#e2e-automl)."
      ]
    },
    {
@@ -301,7 +301,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -799,7 +799,7 @@
        "dataset.delete()\n",
        "\n",
        "# Delete training job\n",
-        "job.delete()"
+        "dag.delete()"
      ]
    }
  ],
@@ -298,7 +298,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -805,7 +805,7 @@
        "dataset.delete()\n",
        "\n",
        "# Delete training job\n",
-        "job.delete()"
+        "dag.delete()"
      ]
    }
  ],
@@ -319,7 +319,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -0,0 +1,994 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "copyright"
+      },
+      "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": "title:generic,gcp"
+      },
+      "source": [
+        "# Get started with Vertex AI Training for XGBoost\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/get_started_vertex_training.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/custom/get_started_vertex_training.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/custom/get_started_vertex_training.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>\n",
+        "<br/><br/><br/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "overview:mlops"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to use Vertex AI Training for XGBoost models.\n",
+        "\n",
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "objective:mlops,stage2,get_started_vertex_training_xgboost"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to use `Vertex AI Training` for training a XGBoost custom model.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Training`\n",
+        "- `Vertex AI Model` resource\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Training using a Python package.\n",
+        "- Report accuracy when hyperparameter tuning.\n",
+        "- Save the model artifacts to Cloud Storage using GCSFuse.\n",
+        "- Create a `Vertex AI Model` resource."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:iris,lcn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [Iris dataset](https://www.tensorflow.org/datasets/catalog/iris) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4fc0ad661ebb"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
+        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "install_mlops"
+      },
+      "source": [
+        "## Installations\n",
+        "\n",
+        "Install the following packages to execute this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ncRJ_Dfdox9L"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install --upgrade google-cloud-aiplatform  -quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e9255e3b156f"
+      },
+      "source": [
+        "### Colab Only: Uncomment the following cell to restart the kernel"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0c0b2427998a"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
+        "\n",
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "435b8e413535"
+      },
+      "source": [
+        "### Before you begin\n",
+        "\n",
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, try the following:\n",
+        "-  Run `gcloud config list`\n",
+        "-  Run `gcloud projects list`\n",
+        "-  See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "be175254a715"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# set the project id\n",
+        "! gcloud config set project $PROJECT_ID"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2e6b8b324ce1"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. \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 = \"us-central1\"  # @param {type: \"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6c43a8673066"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
+        "\n",
+        "**1. Vertex AI Workbench** \n",
+        "- Do nothing as you are already authenticated.\n",
+        "\n",
+        "**2. Local JupyterLab Instance,** uncomment and run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fbc9cd30cc4b"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cd0da2c26879"
+      },
+      "source": [
+        "**3. Colab,** uncomment and run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a336a05c6149"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0461097edfa5"
+      },
+      "source": [
+        "**4. Service Account or other**\n",
+        "- See how to grant Cloud Storage permissions to your service account at [IAM Ch Examples](https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e5755d1a554f"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "\n",
+        "Create a storage bucket to store intermediate artifacts such as datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d2de92accb67"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "create_bucket"
+      },
+      "source": [
+        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aO4sKJfFox9R"
+      },
+      "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": "yWnghzKFox9S"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil ls -al $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "setup_vars"
+      },
+      "source": [
+        "### Set up variables\n",
+        "\n",
+        "Next, set up some variables used throughout the tutorial.\n",
+        "### Import libraries and define constants"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "import_aip:mbsdk"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import google.cloud.aiplatform as aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JZg2sszQox9T"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
+      },
+      "source": [
+        "#### Set hardware accelerators\n",
+        "\n",
+        "You can set hardware accelerators for training and prediction.\n",
+        "\n",
+        "Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 NVIDIA Tesla K80 GPUs allocated to each VM, you would specify:\n",
+        "\n",
+        "    (aiplatform.gapic..AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "\n",
+        "\n",
+        "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
+        "\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. This 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cQUrG4Mbox9T"
+      },
+      "outputs": [],
+      "source": [
+        "TRAIN_GPU, TRAIN_NGPU = (None, None)\n",
+        "\n",
+        "DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "container:training,prediction,xgboost"
+      },
+      "source": [
+        "#### Set pre-built containers\n",
+        "\n",
+        "Set the pre-built Docker container image for training and prediction.\n",
+        "\n",
+        "\n",
+        "For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
+        "\n",
+        "\n",
+        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XujRA5ueox9U"
+      },
+      "outputs": [],
+      "source": [
+        "TRAIN_VERSION = \"xgboost-cpu.1-1\"\n",
+        "DEPLOY_VERSION = \"xgboost-cpu.1-1\"\n",
+        "\n",
+        "TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}:latest\".format(\n",
+        "    REGION.split(\"-\")[0], TRAIN_VERSION\n",
+        ")\n",
+        "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
+        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "machine:training"
+      },
+      "source": [
+        "#### Set machine type\n",
+        "\n",
+        "Next, set the machine type to use for training.\n",
+        "\n",
+        "- Set the variable `TRAIN_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
+        " - `machine type`\n",
+        "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
+        "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
+        "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
+        " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
+        "\n",
+        "*Note: The following is not supported for training:*\n",
+        "\n",
+        " - `standard`: 2 vCPUs\n",
+        " - `highcpu`: 2, 4 and 8 vCPUs\n",
+        "\n",
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UMPFgENkox9U"
+      },
+      "outputs": [],
+      "source": [
+        "TRAIN_COMPUTE = \"n1-standard-4\"\n",
+        "print(\"Train machine type\", TRAIN_COMPUTE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xgboost_intro"
+      },
+      "source": [
+        "## Introduction to XGBoost training\n",
+        "\n",
+        "Once you have trained a XGBoost 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 XGBoost 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.bst).\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 XGBoost model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "examine_training_package:xgboost"
+      },
+      "source": [
+        "### 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",
+        "\n",
+        "- PKG-INFO\n",
+        "- README.md\n",
+        "- setup.cfg\n",
+        "- setup.py\n",
+        "- trainer\n",
+        "  - \\_\\_init\\_\\_.py\n",
+        "  - task.py\n",
+        "\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 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f4wS4eISox9V"
+      },
+      "outputs": [],
+      "source": [
+        "# Make folder for Python training script\n",
+        "! rm -rf custom\n",
+        "! mkdir custom\n",
+        "\n",
+        "# Add package information\n",
+        "! touch custom/README.md\n",
+        "\n",
+        "setup_cfg = \"[egg_info]\\n\\ntag_build =\\n\\ntag_date = 0\"\n",
+        "! echo \"$setup_cfg\" > custom/setup.cfg\n",
+        "\n",
+        "setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n    install_requires=[\\n\\n        'cloudml-hypertune',\\n\\n    ],\\n\\n    packages=setuptools.find_packages())\"\n",
+        "! echo \"$setup_py\" > custom/setup.py\n",
+        "\n",
+        "pkg_info = \"Metadata-Version: 1.0\\n\\nName: Iris tabular classification\\n\\nVersion: 0.0.0\\n\\nSummary: Demostration training script\\n\\nHome-page: www.google.com\\n\\nAuthor: Google\\n\\nAuthor-email: aferlitsch@google.com\\n\\nLicense: Public\\n\\nDescription: Demo\\n\\nPlatform: Vertex\"\n",
+        "! echo \"$pkg_info\" > custom/PKG-INFO\n",
+        "\n",
+        "# Make the training subfolder\n",
+        "! mkdir custom/trainer\n",
+        "! touch custom/trainer/__init__.py"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "taskpy_contents:iris,xgboost"
+      },
+      "source": [
+        "### Create the task script for the Python training package\n",
+        "\n",
+        "Next, you create the `task.py` script for driving the training package. Some noteable steps include:\n",
+        "\n",
+        "- Command-line arguments:\n",
+        "    - `model-dir`: The location to save the trained model. When using Vertex AI custom training, the location will be specified in the environment variable: `AIP_MODEL_DIR`,\n",
+        "    - `dataset_data_url`: The location of the training data to download.\n",
+        "    - `dataset_labels_url`: The location of the training labels to download.\n",
+        "    - `boost-rounds`: Tunable hyperparameter\n",
+        "- Data preprocessing (`get_data()`):\n",
+        "    - Download the dataset and split into training and test.\n",
+        "- Training (`train_model()`):\n",
+        "    - Trains the model\n",
+        "- Evaluation (`evaluate_model()`):\n",
+        "    - Evaluates the model.\n",
+        "    - If hyperparameter tuning, reports the metric for accuracy.\n",
+        "- Model artifact saving\n",
+        "    - Saves the model artifacts and evaluation metrics where the Cloud Storage location specified by `model-dir`.\n",
+        "    \n",
+        "*Note:* The training script uses GCSFuse which mounts the Cloud Storage bucket as a network filesystem, allowing the script to perform filesystem operations (e.g., read and write) within a Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WiSnFuDoox9W"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile custom/trainer/task.py\n",
+        "import datetime\n",
+        "import os\n",
+        "import subprocess\n",
+        "import sys\n",
+        "import pandas as pd\n",
+        "import xgboost as xgb\n",
+        "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",
+        "\n",
+        "parser = argparse.ArgumentParser()\n",
+        "parser.add_argument('--model-dir', dest='model_dir',\n",
+        "                    default=os.getenv('AIP_MODEL_DIR'), type=str, help='Model dir.')\n",
+        "parser.add_argument(\"--dataset-data-url\", dest=\"dataset_data_url\",\n",
+        "                    type=str, help=\"Download url for the training data.\")\n",
+        "parser.add_argument(\"--dataset-labels-url\", dest=\"dataset_labels_url\",\n",
+        "                    type=str, help=\"Download url for the training data labels.\")\n",
+        "parser.add_argument(\"--boost-rounds\", dest=\"boost_rounds\",\n",
+        "                    default=20, type=int, help=\"Number of boosted rounds\")\n",
+        "args = parser.parse_args()\n",
+        "\n",
+        "logging.getLogger().setLevel(logging.INFO)\n",
+        "\n",
+        "def get_data():\n",
+        "    logging.info(\"Downloading training data and labelsfrom: {}, {}\".format(args.dataset_data_url, args.dataset_labels_url))\n",
+        "    # gsutil outputs everything to stderr so we need to divert it to stdout.\n",
+        "    subprocess.check_call(['gsutil', 'cp', args.dataset_data_url, 'data.csv'], stderr=sys.stdout)\n",
+        "    # gsutil outputs everything to stderr so we need to divert it to stdout.\n",
+        "    subprocess.check_call(['gsutil', 'cp', args.dataset_labels_url, 'labels.csv'], stderr=sys.stdout)\n",
+        "\n",
+        "\n",
+        "    # Load data into pandas, then use `.values` to get NumPy arrays\n",
+        "    data = pd.read_csv('data.csv').values\n",
+        "    labels = pd.read_csv('labels.csv').values\n",
+        "\n",
+        "    # Convert one-column 2D array into 1D array for use with XGBoost\n",
+        "    labels = labels.reshape((labels.size,))\n",
+        "\n",
+        "    train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2, random_state=7)\n",
+        "\n",
+        "    # Load data into DMatrix object\n",
+        "    dtrain = xgb.DMatrix(train_data, label=train_labels)\n",
+        "    return dtrain, test_data, test_labels\n",
+        "\n",
+        "def train_model(dtrain):\n",
+        "    logging.info(\"Start training ...\")\n",
+        "    # Train XGBoost model\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 = [np.around(value) for value in pred]\n",
+        "    # evaluate 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",
+        "    hpt = hypertune.HyperTune()\n",
+        "    hpt.report_hyperparameter_tuning_metric(\n",
+        "        hyperparameter_metric_tag='accuracy',\n",
+        "        metric_value=accuracy\n",
+        "    )\n",
+        "    return accuracy\n",
+        "\n",
+        "\n",
+        "dtrain, test_data, test_labels = get_data()\n",
+        "model = train_model(dtrain)\n",
+        "accuracy = evaluate_model(model, test_data, test_labels)\n",
+        "\n",
+        "# GCSFuse conversion\n",
+        "gs_prefix = 'gs://'\n",
+        "gcsfuse_prefix = '/gcs/'\n",
+        "if args.model_dir.startswith(gs_prefix):\n",
+        "    args.model_dir = args.model_dir.replace(gs_prefix, gcsfuse_prefix)\n",
+        "    dirpath = os.path.split(args.model_dir)[0]\n",
+        "    if not os.path.isdir(dirpath):\n",
+        "        os.makedirs(dirpath)\n",
+        "\n",
+        "# Export the classifier to a file\n",
+        "gcs_model_path = os.path.join(args.model_dir, 'model.bst')\n",
+        "logging.info(\"Saving model artifacts to {}\". format(gcs_model_path))\n",
+        "model.save_model(gcs_model_path)\n",
+        "\n",
+        "logging.info(\"Saving metrics to {}/metrics.json\". format(args.model_dir))\n",
+        "gcs_metrics_path = os.path.join(args.model_dir, 'metrics.json')\n",
+        "with open(gcs_metrics_path, \"w\") as f:\n",
+        "    f.write(f\"{'accuracy: {accuracy}'}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tarball_training_script"
+      },
+      "source": [
+        "#### Store 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dnmdycf6ox9X"
+      },
+      "outputs": [],
+      "source": [
+        "! rm -f custom.tar custom.tar.gz\n",
+        "! tar cvf custom.tar custom\n",
+        "! gzip custom.tar\n",
+        "! gsutil cp custom.tar.gz $BUCKET_URI/trainer_iris.tar.gz"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "create_custom_pp_training_job:mbsdk"
+      },
+      "source": [
+        "### Create and run custom training job\n",
+        "\n",
+        "\n",
+        "To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
+        "\n",
+        "#### Create 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",
+        "\n",
+        "- `python_package_gcs_uri`: The location of the Python training package as a tarball.\n",
+        "- `python_module_name`: The relative path to the training script in the Python package.\n",
+        "- `model_serving_container_uri`: The container image for deploying the model.\n",
+        "\n",
+        "*Note:* There is no requirements parameter. You specify any requirements in the `setup.py` script in your Python package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rVEMz1xqox9X"
+      },
+      "outputs": [],
+      "source": [
+        "DISPLAY_NAME = \"iris\"\n",
+        "\n",
+        "job = aiplatform.CustomPythonPackageTrainingJob(\n",
+        "    display_name=DISPLAY_NAME,\n",
+        "    python_package_gcs_uri=f\"{BUCKET_URI}/trainer_iris.tar.gz\",\n",
+        "    python_module_name=\"trainer.task\",\n",
+        "    container_uri=TRAIN_IMAGE,\n",
+        "    model_serving_container_image_uri=DEPLOY_IMAGE,\n",
+        "    project=PROJECT_ID,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "prepare_custom_cmdargs:iris,xgboost"
+      },
+      "source": [
+        "### Prepare your command-line arguments\n",
+        "\n",
+        "Now define the command-line arguments for your custom training container:\n",
+        "\n",
+        "- `args`: The command-line arguments to pass to the executable that is set as the entry point into the container.\n",
+        "  - `--model-dir` : For our demonstrations, we use this command-line argument to specify where to store the model artifacts.\n",
+        "      - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
+        "      - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
+        "  - `--dataset-data-url`: The location of the training data to download.\n",
+        "  - `--dataset-labels-url`: The location of the training labels to download.\n",
+        "  - `--boost-rounds`: Tunable hyperparameter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AoUfpBqVox9Y"
+      },
+      "outputs": [],
+      "source": [
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, \"model\")\n",
+        "DATASET_DIR = \"gs://cloud-samples-data/ai-platform/iris\"\n",
+        "\n",
+        "ROUNDS = 20\n",
+        "\n",
+        "DIRECT = False\n",
+        "if DIRECT:\n",
+        "    CMDARGS = [\n",
+        "        \"--dataset-data-url=\" + DATASET_DIR + \"/iris_data.csv\",\n",
+        "        \"--dataset-labels-url=\" + DATASET_DIR + \"/iris_target.csv\",\n",
+        "        \"--boost-rounds=\" + str(ROUNDS),\n",
+        "        \"--model_dir=\" + MODEL_DIR,\n",
+        "    ]\n",
+        "else:\n",
+        "    CMDARGS = [\n",
+        "        \"--dataset-data-url=\" + DATASET_DIR + \"/iris_data.csv\",\n",
+        "        \"--dataset-labels-url=\" + DATASET_DIR + \"/iris_target.csv\",\n",
+        "        \"--boost-rounds=\" + str(ROUNDS),\n",
+        "    ]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "run_custom_job:mbsdk"
+      },
+      "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",
+        "\n",
+        "- `model_display_name`: The human readable name for the `Model` resource.\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",
+        "- `machine_type`: The machine type for the compute instances.\n",
+        "- `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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JCruQq1aox9Y"
+      },
+      "outputs": [],
+      "source": [
+        "if TRAIN_GPU:\n",
+        "    model = job.run(\n",
+        "        model_display_name=\"iris\",\n",
+        "        args=CMDARGS,\n",
+        "        replica_count=1,\n",
+        "        machine_type=TRAIN_COMPUTE,\n",
+        "        accelerator_type=TRAIN_GPU.name,\n",
+        "        accelerator_count=TRAIN_NGPU,\n",
+        "        base_output_dir=MODEL_DIR,\n",
+        "        sync=False,\n",
+        "    )\n",
+        "else:\n",
+        "    model = job.run(\n",
+        "        model_display_name=\"iris\",\n",
+        "        args=CMDARGS,\n",
+        "        replica_count=1,\n",
+        "        machine_type=TRAIN_COMPUTE,\n",
+        "        base_output_dir=MODEL_DIR,\n",
+        "        sync=False,\n",
+        "    )\n",
+        "\n",
+        "model_path_to_deploy = MODEL_DIR"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "list_job"
+      },
+      "source": [
+        "### List a custom training job"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KBM_KLMSox9Y"
+      },
+      "outputs": [],
+      "source": [
+        "_job = job.list(filter=f\"display_name={DISPLAY_NAME}\")\n",
+        "print(_job)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "custom_job_wait:mbsdk"
+      },
+      "source": [
+        "### Wait for completion of custom training job\n",
+        "\n",
+        "Next, wait for the custom training job to complete. Alternatively, one can set the parameter `sync` to `True` in the `run()` method to block until the custom training job is completed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lHPMHbSyox9Z"
+      },
+      "outputs": [],
+      "source": [
+        "model.wait()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "delete_job"
+      },
+      "source": [
+        "### Delete a custom training job\n",
+        "\n",
+        "After a training job is completed, you can delete the training job with the method `delete()`.  Prior to completion, a training job can be canceled with the method `cancel()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tlYg7Sp-ox9Z"
+      },
+      "outputs": [],
+      "source": [
+        "job.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cleanup:mbsdk"
+      },
+      "source": [
+        "# Cleaning up\n",
+        "\n",
+        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
+        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+        "\n",
+        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
+        "\n",
+        "- Custom Job (Custome Training job is remove in previous step)\n",
+        "- Cloud Storage Bucket"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JyWy23gDox9a"
+      },
+      "outputs": [],
+      "source": [
+        "delete_bucket = True\n",
+        "\n",
+        "model.delete()\n",
+        "\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil rm -r $BUCKET_URI"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "get_started_vertex_training_xgboost.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -0,0 +1,669 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur8xi4C7S06n"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2022 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAPoU8Sm5E6e"
+      },
+      "source": [
+        "# Delete Outdated Experiments in Vertex AI TensorBoard\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/vertex-ai-samples/main/notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "24743cf4a1e1"
+      },
+      "source": [
+        "**_NOTE_**: This notebook has been tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Vertex AI will have a new Vertex AI TensorBoard billing model. From August 2023, it will change from monthly `$300/user` to monthly `$10/GB`. In preparation for this change, users need to delete old Vertex AI TensorBoard Experiments to avoid unnecessary storage costs when the pricing change takes place.\n",
+        "\n",
+        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d975e698c9a4"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to delete outdated TensorBoard Experiments to avoid unnecessary storage costs.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Tensorboard\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- How to delete the TB Experiment with a predefined key-value label pair `<label_key, label_value>`\n",
+        "\n",
+        "- How to delete the TB Experiments created before the  `create_time`\n",
+        "\n",
+        "- How to delete the TB Experiments created before the  `update_time`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "08d289fa873f"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "No dataset is used."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aed92deeb4a0"
+      },
+      "source": [
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), and [Cloud Storage pricing](https://cloud.google.com/storage/pricing),\n",
+        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7EUnXsZhAGF"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install the following packages required to execute this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b4ef9b72d43"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the packages\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"IS_TESTING\"):\n",
+        "    USER = \"--user\"\n",
+        "else:\n",
+        "    USER = \"\"\n",
+        "! pip3 install {USER} --upgrade google-cloud-aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58707a750154"
+      },
+      "source": [
+        "### Colab only: Uncomment the following cell to restart the kernel."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f200f10a1da3"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
+        "\n",
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BF1j6f9HApxa"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+        "\n",
+        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WReHDGG5g0XY"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oM1iC_MfAts1"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QAup21nz6LHk"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBCra4QMA2wR"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "74ccc9e52986"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "de775a3773ba"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "254614fa0c46"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef21552ccea8"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "603adbbf0532"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f6b2ccc891ed"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant permissions to your service account at https://cloud.google.com/marketplace/docs/grant-service-account-access."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "960505627ddf"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PyQmSRbKA8r-"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NUJOpq7_-nt6"
+      },
+      "source": [
+        "### Define constants\n",
+        "\n",
+        "Define variables you use in this tutorial. In particular, you set\n",
+        "\n",
+        "- `CREATE_TIME_CUT` : delete tensorboard experiments that were created before CREATE_TIME_CUT. For example, `2022-12-31`.\n",
+        "\n",
+        "- `UPDATE_TIME_CUT` : delete tensorboard experiments that were created before UPDATE_TIME_CUT. For example, `2022-12-31`.\n",
+        "\n",
+        "- `DETAILED_LOG` : a booled variable to see Tensorboard deletion progress. If True, it shows progress by experiments. Otherwise, it reports progress per 100 experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0AkyoMre-qe5"
+      },
+      "outputs": [],
+      "source": [
+        "CREATE_TIME_CUT = \"\"  # @param {type:\"string\"}\n",
+        "\n",
+        "UPDATE_TIME_CUT = \"\"  # @param {type:\"string\"}\n",
+        "\n",
+        "DETAILED_LOG = True  # @param {type: \"boolean\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n68J5KJ4ERbF"
+      },
+      "source": [
+        "### Define Helpers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JIXC-tVrETET"
+      },
+      "outputs": [],
+      "source": [
+        "def clean_up_by_label(tensorboard_instance, label_key, label_value):\n",
+        "    \"\"\"Delete the TB Experiment with the key-value label pair <label_key, label_value>\"\"\"\n",
+        "    # List tensorboard experiments\n",
+        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
+        "        tensorboard_name=tensorboard_instance.resource_name\n",
+        "    )\n",
+        "    # Get the number of tensorboard experiments\n",
+        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
+        "    # For each experiment\n",
+        "    for i in range(num_tensorboard_experiments):\n",
+        "        tensorboard_experiment = tensorboard_experiments[i]\n",
+        "        if detailed_log or (i % 100 == 0):\n",
+        "            print(\n",
+        "                f\">>>checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
+        "            )\n",
+        "        # Get experiment labels\n",
+        "        labels = tensorboard_experiment.labels\n",
+        "        # Filter by label\n",
+        "        if label_key in labels and labels[label_key] == label_value:\n",
+        "            # Delete experiment\n",
+        "            tensorboard_experiment.delete()\n",
+        "\n",
+        "\n",
+        "def clean_up_by_create_time(tensorboard_instance, create_time_cut):\n",
+        "    \"\"\"Delete the TB Experiment with the `create_time`\"\"\"\n",
+        "    # List tensorboard experiments\n",
+        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
+        "        tensorboard_name=tensorboard_instance.resource_name, order_by=\"create_time\"\n",
+        "    )\n",
+        "    # Get the number of tensorboard experiments\n",
+        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
+        "    # For each experiment\n",
+        "    for i in range(num_tensorboard_experiments):\n",
+        "        tensorboard_experiment = tensorboard_experiments[i]\n",
+        "        if detailed_log or (i % 100 == 0):\n",
+        "            print(\n",
+        "                f\">>> checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
+        "            )\n",
+        "        # Filter by create_time\n",
+        "        if str(tensorboard_experiment.create_time) < create_time_cut:\n",
+        "            # Delete experiment\n",
+        "            tensorboard_experiment.delete()\n",
+        "        else:\n",
+        "            break\n",
+        "\n",
+        "\n",
+        "def clean_up_by_update_time(tensorboard_instance, update_time_cut):\n",
+        "    \"\"\"Delete the TB Experiment with with the `update_time`\"\"\"\n",
+        "    # List tensorboard experiments\n",
+        "    tensorboard_experiments = aiplatform.TensorboardExperiment.list(\n",
+        "        tensorboard_name=tensorboard_instance.resource_name, order_by=\"update_time\"\n",
+        "    )\n",
+        "    # Get the number of tensorboard experiments\n",
+        "    num_tensorboard_experiments = len(tensorboard_experiments)\n",
+        "    # For each experiment\n",
+        "    for i in range(num_tensorboard_experiments):\n",
+        "        tensorboard_experiment = tensorboard_experiments[i]\n",
+        "        if detailed_log or (i % 100 == 0):\n",
+        "            print(\n",
+        "                f\">>> checking TB experiment [{i + 1}/{num_tensorboard_experiments}]: {tensorboard_experiment.resource_name}\"\n",
+        "            )\n",
+        "        # Filter by update_time\n",
+        "        if str(tensorboard_experiment.update_time) < update_time_cut:\n",
+        "            tensorboard_experiment.delete()\n",
+        "        else:\n",
+        "            break"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UCUOlTLYCT0B"
+      },
+      "source": [
+        "## Delete outdated Vertex AI TensorBoard Experiments"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EGfFVHhWDjkl"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "G1l-7Wft3jb6"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e4672ef33691"
+      },
+      "source": [
+        "### Set delete outdated TensorBoard experiments \n",
+        "\n",
+        "Initialize a flag variable to start deleting outdated TensorBoard experiments and a flag variable to choose the deleting method. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8cdc326c9823"
+      },
+      "outputs": [],
+      "source": [
+        "delete_tb_experiments = False  # @param {type: \"boolean\"}\n",
+        "\n",
+        "delete_method = \"\"  # @param [\"by_label\", \"by_create_time\", \"by_update_time\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JjJVARe5V-MG"
+      },
+      "source": [
+        "### Delete an TensorBoard instance\n",
+        "\n",
+        "To delete a Tensorboard instance, you need `TENSORBOARD_INSTANCE` ID which uniquely identifies the Tensorboard instance where you run experiments.\n",
+        "\n",
+        "To get the Tensorboard instance ID, you can either\n",
+        "\n",
+        "- go to the cloud console UI, Vertex AI > Experiments > Tensorboard Instances, or\n",
+        "- use the list command below to list all TensorBoard instances for your project and region.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eqM2pIO8_4Y7"
+      },
+      "outputs": [],
+      "source": [
+        "tensorboard_instances = aiplatform.Tensorboard.list(project=PROJECT_ID, location=REGION)\n",
+        "print(tensorboard_instances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wT_uPArACMPX"
+      },
+      "source": [
+        "Set the tensorboard instance id for which you want to delete experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hUync8E9CLcm"
+      },
+      "outputs": [],
+      "source": [
+        "if delete_tb_experiments:\n",
+        "\n",
+        "    TENSORBOARD_INSTANCE_ID = \"\"  # @param {type:\"string\"}\n",
+        "\n",
+        "    TENSORBOARD_INSTANCE = aiplatform.Tensorboard(\n",
+        "        project=PROJECT_ID, location=REGION, tensorboard_name=TENSORBOARD_INSTANCE_ID\n",
+        "    )\n",
+        "    print(TENSORBOARD_INSTANCE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OOp22fxdHqmT"
+      },
+      "source": [
+        "### Delete a TensorBoard Experiment with the key-value label pair\n",
+        "\n",
+        "You delete a TensorBoard experiment using a predefined `label_key` and `label_value`. For example, you may have assigned `delete` label key and `true` label value to indicate all Tensorboard experiments you want to delete.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N7KajCsjij1g"
+      },
+      "outputs": [],
+      "source": [
+        "LABEL_KEY = \"delete\"  # @param {type:\"string\"}\n",
+        "LABEL_VALUE = \"true\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N45samDzWBO2"
+      },
+      "outputs": [],
+      "source": [
+        "if delete_tb_experiments and delete_method == \"by_label\":\n",
+        "    clean_up_by_label(TENSORBOARD_INSTANCE, LABEL_KEY, LABEL_VALUE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XrEVqlgSIL6K"
+      },
+      "source": [
+        "### Delete a TensorBoard Experiment with `create_time`\n",
+        "\n",
+        "You delete a TensorBoard experiment using `create_time` field"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4_bjNeQ7p87A"
+      },
+      "outputs": [],
+      "source": [
+        "if delete_tb_experiments and delete_method == \"by_create_time\":\n",
+        "    clean_up_by_create_time(TENSORBOARD_INSTANCE, CREATE_TIME_CUT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZGNlSbIrIP_o"
+      },
+      "source": [
+        "### Delete a TensorBoard Experiment with `update_time`\n",
+        "\n",
+        "You delete a TensorBoard Experiment using a predefined `update_time` field"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SDyc_a8XwEve"
+      },
+      "outputs": [],
+      "source": [
+        "if delete_tb_experiments and delete_method == \"by_update_time\":\n",
+        "    clean_up_by_update_time(TENSORBOARD_INSTANCE, UPDATE_TIME_CUT)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "delete_outdated_tensorboard_experiments.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -1410,7 +1410,7 @@
  ],
  "metadata": {
    "colab": {
-      "name": "UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb",
+      "name": "sdk-automl-tabular-binary-classification-online-prediction.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
@@ -604,12 +604,18 @@
        "        epochs=params[\"epochs\"],\n",
        "        dropout_rate=params[\"dropout_rate\"],\n",
        "    )\n",
-        "    aiplatform.log_metrics(\n",
-        "        {metric: values[-1] for metric, values in history.history.items()}\n",
-        "    )\n",
+        "\n",
+        "    for metric, values in history.history.items():\n",
+        "        try:\n",
+        "            aiplatform.log_metrics({metric: values[-1]})\n",
+        "        except:\n",
+        "            aiplatform.log_metrics({metric: 0.0})\n",
        "\n",
        "    loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
-        "    aiplatform.log_metrics({\"eval_loss\": loss, \"eval_mae\": mae, \"eval_mse\": mse})"
+        "    try:\n",
+        "        aiplatform.log_metrics({\"eval_loss\": loss, \"eval_mae\": mae, \"eval_mse\": mse})\n",
+        "    except:\n",
+        "        aiplatform.log_metrics({\"eval_loss\": 0.0, \"eval_mae\": 0.0, \"eval_mse\": 0.0})"
      ]
    },
    {
@@ -351,7 +351,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -330,7 +330,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -201,8 +201,8 @@
        "\n",
        "! pip3 install --upgrade {USER_FLAG} google-cloud-aiplatform \\\n",
        "                                    google-cloud-storage \\\n",
-        "                                    kfp \\\n",
-        "                                    google-cloud-pipeline-components -q"
+        "                                    'kfp<2' \\\n",
+        "                                    'google-cloud-pipeline-components<2' -q"
      ]
    },
    {
@@ -165,11 +165,11 @@
        "# Install the packages\n",
        "USER=''\n",
        "! pip3 install {USER} --upgrade google-cloud-aiplatform \\\n",
-        "                                google-cloud-pipeline-components\n",
+        "                                'google-cloud-pipeline-components<2'\n",
        "! pip3 install {USER}           tensorflow==2.5 \\\n",
        "                                tensorflow_hub\n",
        "  \n",
-        "! pip3 install {USER} --upgrade kfp"
+        "! pip3 install {USER} --upgrade 'kfp<2'"
      ]
    },
    {
@@ -362,7 +362,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -175,7 +175,7 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
        "! pip3 install -U google-cloud-storage {USER_FLAG} -q\n",
-        "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q"
+        "! pip3 install {USER_FLAG} 'kfp<2' 'google-cloud-pipeline-components<2' --upgrade -q"
      ]
    },
    {
@@ -197,10 +197,10 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
        "! pip3 install -U google-cloud-storage {USER_FLAG} -q\n",
-        "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q\n",
+        "! pip3 install {USER_FLAG} 'kfp<2' 'google-cloud-pipeline-components<2' --upgrade -q\n",
        "\n",
        "\n",
-        "! pip3 install --upgrade --force-reinstall $USER_FLAG tensorflow kfp google-cloud-aiplatform google-cloud-storage google-cloud-pipeline-components -q"
+        "! pip3 install --upgrade --force-reinstall $USER_FLAG tensorflow 'kfp<2' google-cloud-aiplatform google-cloud-storage 'google-cloud-pipeline-components<2' -q"
      ]
    },
    {
@@ -224,8 +224,8 @@
        "                            google-cloud-bigquery \\\n",
        "                            pandas \\\n",
        "                            pyarrow \\\n",
-        "                            kfp \\\n",
-        "                            google-cloud-pipeline-components {USER_FLAG} -q \n",
+        "                            'kfp<2' \\\n",
+        "                            'google-cloud-pipeline-components<2' {USER_FLAG} -q \n",
        "! pip3 install db-dtypes {USER_FLAG} -q"
      ]
    },
@@ -149,8 +149,8 @@
        "! pip3 install -U tensorflow-transform==1.2  -q\n",
        "! pip3 install -U tensorflow-io==0.18  -q\n",
        "! pip3 install --upgrade google-cloud-aiplatform[tensorboard] -q\n",
-        "! pip3 install --upgrade google-cloud-pipeline-components  -q\n",
-        "! pip3 install --upgrade kfp -q"
+        "! pip3 install --upgrade 'google-cloud-pipeline-components<2'  -q\n",
+        "! pip3 install --upgrade 'kfp<2' -q"
      ]
    },
    {
@@ -307,7 +307,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -193,7 +193,7 @@
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
-        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform kfp google-cloud-pipeline-components google-cloud-storage"
+        "! pip3 install --upgrade --quiet {USER_FLAG} google-cloud-aiplatform 'kfp<2' 'google-cloud-pipeline-components<2' google-cloud-storage"
      ]
    },
    {
@@ -198,7 +198,7 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
        "                        google-cloud-storage \\\n",
-        "                        $USER kfp google-cloud-pipeline-components -q "
+        "                        $USER 'kfp<2' 'google-cloud-pipeline-components<2' -q "
      ]
    },
    {
@@ -196,7 +196,7 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
-        "! pip3 install $USER kfp google-cloud-pipeline-components --upgrade -q"
+        "! pip3 install $USER 'kfp<2' 'google-cloud-pipeline-components<2' --upgrade -q"
      ]
    },
    {
@@ -198,8 +198,8 @@
        "\n",
        "! pip3 install --upgrade {USER_FLAG} -q google-cloud-aiplatform \\\n",
        "                                        google-cloud-storage {USER_FLAG} \\\n",
-        "                                        kfp \\\n",
-        "                                        google-cloud-pipeline-components"
+        "                                        'kfp<2' \\\n",
+        "                                        'google-cloud-pipeline-components<2'"
      ]
    },
    {
@@ -199,8 +199,8 @@
        "\n",
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
-        "                        google-cloud-pipeline-components \\\n",
-        "                        kfp $USER_FLAG -q"
+        "                        'google-cloud-pipeline-components<2' \\\n",
+        "                        'kfp<2' $USER_FLAG -q"
      ]
    },
    {
@@ -203,7 +203,7 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
        "! pip3 install -U google-cloud-storage {USER_FLAG} -q\n",
-        "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q"
+        "! pip3 install {USER_FLAG} 'kfp<2' 'google-cloud-pipeline-components<2' --upgrade -q"
      ]
    },
    {
@@ -201,7 +201,7 @@
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
        "! pip3 install -U google-cloud-storage {USER_FLAG} -q\n",
-        "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q\n",
+        "! pip3 install {USER_FLAG} 'kfp<2' 'google-cloud-pipeline-components<2' --upgrade -q\n",
        "\n",
        "if os.getenv(\"IS_TESTING\"):\n",
        "    ! pip3 install --upgrade matplotlib $USER_FLAG -q"
@@ -168,11 +168,11 @@
        "# Install the packages\n",
        "USER=''\n",
        "! pip3 install {USER} --upgrade google-cloud-aiplatform \\\n",
-        "                                google-cloud-pipeline-components\n",
+        "                                'google-cloud-pipeline-components<2'\n",
        "! pip3 install {USER}           tensorflow==2.5 \\\n",
        "                                tensorflow_hub\n",
        "\n",
-        "! pip3 install {USER} --upgrade kfp"
+        "! pip3 install {USER} --upgrade 'kfp<2'"
      ]
    },
    {
@@ -363,7 +363,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -128,8 +128,8 @@
        "# Install the packages\n",
        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
        "                        google-cloud-storage \\\n",
-        "                        kfp \\\n",
-        "                        google-cloud-pipeline-components"
+        "                        'kfp<2' \\\n",
+        "                        'google-cloud-pipeline-components<2'"
      ]
    },
    {
@@ -265,7 +265,7 @@
      "source": [
        "# Install Python package dependencies.\n",
        "print(\"Installing libraries\")\n",
-        "! pip3 install {USER_FLAG} --quiet google-cloud-pipeline-components kfp\n",
+        "! pip3 install {USER_FLAG} --quiet 'google-cloud-pipeline-components==1.0.20' 'kfp<2'\n",
        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-aiplatform google-cloud-bigquery"
      ]
    },
@@ -268,22 +268,7 @@
      "source": [
        "## Notes about service account and permission\n",
        "\n",
-        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts above have the required roles:\n",
-        "\n",
-        "|Service account email|Description|Roles|\n",
-        "|---|---|---|\n",
-        "|PROJECT_NUMBER-compute@developer.gserviceaccount.com|Compute Engine default service account|Dataflow Admin, Dataflow Worker, Storage Admin, BigQuery Admin, Vertex AI User|\n",
-        "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
-        "\n",
-        "\n",
-        "1. Goto https://console.cloud.google.com/iam-admin/iam.\n",
-        "2. Check the \"Include Google-provided role grants\" checkbox.\n",
-        "3. Find the above emails.\n",
-        "4. Grant the corresponding roles.\n",
-        "\n",
-        "### Using data source from a different project\n",
-        "- For the BQ data source, grant both service accounts the \"BigQuery Data Viewer\" role.\n",
-        "- For the CSV data source, grant both service accounts the \"Storage Object Viewer\" role.\n"
+        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts have the required roles listed in the [Service accounts for Tabular Workflow for TabNet, and Tabular Workflow for Wide & Deep, and Prophet documentation](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#fte-workflow)."
      ]
    },
    {
@@ -268,22 +268,7 @@
      "source": [
        "## Notes about service account and permission\n",
        "\n",
-        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts above have the required roles:\n",
-        "\n",
-        "|Service account email|Description|Roles|\n",
-        "|---|---|---|\n",
-        "|PROJECT_NUMBER-compute@developer.gserviceaccount.com|Compute Engine default service account|Dataflow Admin, Dataflow Worker, Storage Admin, BigQuery Admin, Vertex AI User|\n",
-        "|service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com|AI Platform Service Agent|Vertex AI Service Agent|\n",
-        "\n",
-        "\n",
-        "1. Goto https://console.cloud.google.com/iam-admin/iam.\n",
-        "2. Check the \"Include Google-provided role grants\" checkbox.\n",
-        "3. Find the above emails.\n",
-        "4. Grant the corresponding roles.\n",
-        "\n",
-        "### Using data source from a different project\n",
-        "- For the BQ data source, grant both service accounts the \"BigQuery Data Viewer\" role.\n",
-        "- For the CSV data source, grant both service accounts the \"Storage Object Viewer\" role."
+        "**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts have the required roles listed in the [Service accounts for Tabular Workflow for TabNet, and Tabular Workflow for Wide & Deep, and Prophet documentation](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#fte-workflow)."
      ]
    },
    {
@@ -296,7 +296,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -300,7 +300,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
Author	SHA1	Message	Date
Andrew Ferlitsch	fdc3b37775	fix: lint	2023-04-20 04:54:38 +00:00
Andrew Ferlitsch	47210148d0	fix: rename UJ4	2023-04-20 04:46:37 +00:00
Connor McCarthy GitHub Andrew Ferlitsch	59a220cb58	fix: pin kfp and gcpc < 2.0 in rapid_prototyping_bqml_automl (#1755 ) * pin kfp in rapid_prototyping_bqml_automl * downgrade component version --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@gmail.com>	2023-04-20 04:36:39 +00:00
Connor McCarthyandGitHub	098ad42f70	pin kfp in google_cloud_pipeline_components_automl_text (#1748 )	2023-04-19 23:01:21 +00:00
Connor McCarthyandGitHub	1f141d0059	pin kfp in google_cloud_pipeline_components_automl_images (#1746 )	2023-04-19 19:29:50 +00:00
Andrew FerlitschandGitHub	e0a1608783	fix: pin kfp and gcpc < 2.0 (#1738 )	2023-04-19 18:37:16 +00:00
Connor McCarthyandGitHub	1a469a09df	pin kfp in google_cloud_pipeline_components_automl_tabular (#1747 )	2023-04-19 18:14:04 +00:00
Connor McCarthyandGitHub	bc3f6d3819	pin kfp in custom_model_training_and_batch_prediction (#1741 )	2023-04-19 16:53:23 +00:00
Andrew FerlitschandGitHub	f088a3ce69	update (#1736 )	2023-04-19 16:52:34 +00:00
Connor McCarthyandGitHub	863660042b	pin kfp in custom_tabular_train_batch_pred_bq_pipeline (#1742 )	2023-04-19 16:51:11 +00:00
Connor McCarthyandGitHub	7162248267	pin kfp in google_cloud_pipeline_components_model_train_upload_deploy (#1749 )	2023-04-19 16:50:43 +00:00
Connor McCarthyandGitHub	2e908a8efa	pin kfp in get_started_with_hpt_pipeline_components (#1743 )	2023-04-19 16:50:13 +00:00
Connor McCarthyandGitHub	588d2c880e	pin kfp in get_started_with_machine_management (#1745 )	2023-04-19 16:49:12 +00:00
Connor McCarthyandGitHub	54f10c7411	pin kfp in google_cloud_pipeline_components_model_upload_predict_evaluate (#1750 )	2023-04-19 16:48:46 +00:00
Connor McCarthyandGitHub	0c2b9e9f45	pin kfp in multicontender_vs_champion_deployment_method (#1753 )	2023-04-19 16:48:21 +00:00
Connor McCarthyandGitHub	fe07d416e5	pin kfp in challenger_vs_blessed_deployment_method (#1739 )	2023-04-19 16:04:05 +00:00
Connor McCarthyandGitHub	9c69cbfd54	pin kfp in control_flow_kfp (#1740 )	2023-04-19 16:03:33 +00:00
Connor McCarthyandGitHub	2a25be6af2	pin kfp in lightweight_functions_component_io_kfp (#1751 )	2023-04-19 16:02:52 +00:00
Connor McCarthyandGitHub	9680a7e772	pin kfp in metrics_viz_run_compare_kfp (#1752 )	2023-04-19 16:02:03 +00:00
Connor McCarthyandGitHub	30527875ed	pin kfp in pipelines_intro_kfp (#1754 )	2023-04-19 16:01:36 +00:00
yishan-pu GitHub Yishan Pu	8c11e19d86	Update service accounts and permissions info in the Wide and Deep notebook (#1737 ) Co-authored-by: Yishan Pu <yishanpu@google.com>	2023-04-19 15:24:34 +00:00
yishan-pu GitHub Yishan Pu	f44f51c06d	Update the service accounts and permissions info in the TabNet notebook (#1734 ) Co-authored-by: Yishan Pu <yishanpu@google.com>	2023-04-18 16:06:42 +00:00
yishan-pu GitHub Yishan Pu	840b537ea6	Update the E2E AutoML Notebook regarding the service accounts and permissions info (#1731 ) Co-authored-by: Yishan Pu <yishanpu@google.com>	2023-04-17 23:16:28 +00:00
Xiang XuandGitHub	d2602b944f	Add blip2 notebook (#1730 ) * add blip2 * resolve comments	2023-04-17 23:14:34 +00:00
Xiang XuandGitHub	bb39135946	fix owlvit (#1729 )	2023-04-17 15:19:36 +00:00
gericdongandGitHub	cb72a56e55	feat: PyTorch train and deploy E2E with pre-built containers (#1728 ) * feat: PyTorch train and deploy E2E with pre-built containers * address review comments * address review comment 2 * suppress gsutil warning messags * Use unique names * Corrected the project id template	2023-04-17 14:56:43 +00:00
Andrew FerlitschandGitHub	eee97362d0	fix: migrate tabnet (#1727 ) * fix: migrate tabnet * fix: describe * updates from review	2023-04-13 21:09:23 +00:00
Ivan NardiniandGitHub	a92c5be0e9	feat: delete outdated tensorboard experiments (#1723 ) * add delete outdated tensorboard experiments notebook * update CODEOWNERS * fix aiplatform import * linter passed * set a flag varible to pass test * linter passed * add andy review * linter passed	2023-04-13 15:57:43 +00:00
Xiang XuandGitHub	80707a3ba7	fix clean (#1725 )	2023-04-12 20:28:00 +00:00
Eric Schmidt GitHub Andrew Ferlitsch	903f69b81c	feat: PyTorch training with GCS data (#1705 ) * feat: PyTorch training with GCS data * fix kernel restart * per reviewer * Added requirements section * fixes for build * lint, build * per reviewer * linter again * per reviewer * per reviewer, linter --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2023-04-12 17:39:39 +00:00
Andrew FerlitschandGitHub	40a31daaef	Revert "update: thread safe limiter (#1718 )" (#1724 ) This reverts commit `37ba322412`.	2023-04-12 16:33:16 +00:00
Andrew FerlitschandGitHub	37ba322412	update: thread safe limiter (#1718 ) * update: thread safe limiter * update: thread safe limiter * fix: missing install	2023-04-12 00:05:36 +00:00
Xiang XuandGitHub	ee43400e89	fix clean (#1719 )	2023-04-11 22:59:00 +00:00
Xiang XuandGitHub	176c3721fd	fix pytorch notebooks (#1717 )	2023-04-11 22:01:56 +00:00
Andrew FerlitschandGitHub	1d6f9bc36b	fix: exception (#1714 ) * fix: exception * fix: exception * fix: exception * fix: exception	2023-04-11 20:40:22 +00:00
dstnluong-googleandGitHub	973ecf95e7	Fix retinanet_spinenet143 experiment args in IOD notebook to use correct config file. (#1716 ) * Minor nit fixes for model garden tfvision IOD notebook. * Sync * Lint * Fix retinanet_spinenet143 experiment args in IOD notebook to use correct config file.	2023-04-11 20:22:29 +00:00
Xiang XuandGitHub	1f2adec703	fix prediction routes (#1715 )	2023-04-11 19:33:17 +00:00
Xiang XuandGitHub	9843c1f063	fix image url (#1711 )	2023-04-11 16:06:14 +00:00
KCFindstrandGitHub	5afdc3524a	Add a trailing slash to Model Garden ICN notebook checkpoint destination URI (#1710 )	2023-04-11 16:04:58 +00:00
Andrew FerlitschandGitHub	770508b4f0	fix: delete experiment and fs (#1709 ) * fix: delete experiment and fs * fix False typo * fix: 2nd try at Falsee typo * fix: update_time	2023-04-10 21:56:20 +00:00
Xiang XuandGitHub	987881e887	fix links (#1708 )	2023-04-10 14:44:21 +00:00
Andrew FerlitschandGitHub	8e53b623e5	fix: migrate vizier notebook (#1706 ) * fix: migrate vizier notebook * fix: review comment	2023-04-07 06:55:57 +00:00
Andrew FerlitschandGitHub	948537e1d4	fix: migrate machine management (#1707 ) * fix: migrate machine management * remove not per review	2023-04-06 20:57:29 +00:00
Andrew FerlitschandGitHub	4338b1d90b	fix: unique str (#1692 ) * fix: unique str * fix: lint * fix cleanup	2023-04-06 12:43:38 +00:00
Andrew FerlitschandGitHub	b7a41637ea	fix: unique str (#1693 ) * fix: unique str * Fix cleanup	2023-04-06 12:43:03 +00:00
Andrew FerlitschandGitHub	c8f7b910f2	fix: add back missing not (#1687 )	2023-04-05 22:48:40 +00:00
Andrew FerlitschandGitHub	1e226a3419	fix: unique str (#1701 )	2023-04-05 20:38:22 +00:00
Andrew FerlitschandGitHub	287f70abc2	fix: unique dtr (#1700 )	2023-04-05 20:37:37 +00:00
Andrew FerlitschandGitHub	8e9d664d88	fix: unique dtr (#1699 )	2023-04-05 20:36:29 +00:00
Andrew FerlitschandGitHub	166a407b8d	fix: unique str (#1698 )	2023-04-05 20:35:29 +00:00
Andrew FerlitschandGitHub	72f9ba7647	fix: unique str (#1697 )	2023-04-05 20:34:41 +00:00
Andrew FerlitschandGitHub	24549bc506	fix: unique str (#1696 )	2023-04-05 20:33:46 +00:00
Andrew FerlitschandGitHub	dc4efb0c63	fix: unique str (#1694 )	2023-04-05 20:33:04 +00:00
Andrew FerlitschandGitHub	b8c6cc29b4	fix: unique str (#1702 )	2023-04-05 20:31:59 +00:00
Andrew FerlitschandGitHub	3b6d2e02d0	fix: unique str (#1703 )	2023-04-05 20:31:18 +00:00
Andrew FerlitschandGitHub	f33adcea80	fix: fine tune unique (#1704 )	2023-04-05 20:30:51 +00:00
Andrew FerlitschandGitHub	5b6063b8a8	fix: unique str (#1695 )	2023-04-05 18:56:37 +00:00
Andrew FerlitschandGitHub	1857b23556	fix: unique str (#1691 )	2023-04-05 18:55:52 +00:00
Andrew FerlitschandGitHub	86d85ca555	fix: unique str (#1690 )	2023-04-05 18:55:18 +00:00
Andrew FerlitschandGitHub	67b50fda2c	fix: update unique str (#1689 )	2023-04-05 18:54:34 +00:00
dstnluong-googleandGitHub	0d5cb22491	Minor nit fixes for model garden tfvision IOD notebook. (#1684 ) * Minor nit fixes for model garden tfvision IOD notebook. * Sync * Lint	2023-04-05 16:52:32 +00:00
Minwoo Park GitHub minwoopark	c030d1d79c	Undeploy endpoint before deleting a model. (#1685 ) Co-authored-by: minwoopark <minwoopark@google.com>	2023-04-04 20:53:53 +00:00
Andrew FerlitschandGitHub	702e6fc262	feat: migrate custom train XGBoost (#1665 ) * feat: migrate custom train XGBoost * fix: review comments	2023-04-04 20:47:49 +00:00