google-cloud-pipeline-components_automl_tabular-stroke-predictor.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ur8xi4C7S06n"
      },
      "outputs": [],
      "source": [
        "# Copyright 2021 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": [
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/ai-platform-unified/notebooks/unofficial/pipelines/google-cloud-pipeline-components_automl_tabular.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/ai-platform-samples/blob/master/ai-platform-unified/notebooks/unofficial/pipelines/google-cloud-pipeline-components_automl_tabular.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/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/ai-platform-samples/raw/master/ai-platform-unified/notebooks/unofficial/pipelines/google-cloud-pipeline-components_automl_tabular.ipynb\">\n",
        "      Open in Google Cloud Notebooks\n",
        "    </a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVX0RV_s8zZ7"
      },
      "source": [
        "# Vertex Pipelines: AutoML Tabular pipelines using google-cloud-pipeline-components\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
        "\n",
        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build an AutoML Tabular workflow on [Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines).\n",
        "\n",
        "### Objective\n",
        "\n",
        "In this example, you'll learn how to use components from `google_cloud_pipeline_components` to:\n",
        "- create a _Dataset_\n",
        "- train an AutoML Tabular model\n",
        "- deploy the trained model to an _endpoint_ for serving\n",
        "\n",
        "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform).\n",
        "\n",
        "### Costs \n",
        "\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI Training and Serving\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI 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": "ze4-nDLfK4pw"
      },
      "source": [
        "### Set up your local development environment\n",
        "\n",
        "**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n",
        "all the requirements to run this notebook. You can skip this step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCuSR8GkAgzl"
      },
      "source": [
        "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
        "You need the following:\n",
        "\n",
        "* The Google Cloud SDK\n",
        "* Git\n",
        "* Python 3\n",
        "* virtualenv\n",
        "* Jupyter notebook running in a virtual environment with Python 3\n",
        "\n",
        "The Google Cloud guide to [Setting up a Python development\n",
        "environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
        "installation guide](https://jupyter.org/install) provide detailed instructions\n",
        "for meeting these requirements. The following steps provide a condensed set of\n",
        "instructions:\n",
        "\n",
        "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
        "\n",
        "1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n",
        "\n",
        "1. [Install\n",
        "   virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
        "   and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
        "\n",
        "1. To install Jupyter, run `pip install jupyter` on the\n",
        "command-line in a terminal shell.\n",
        "\n",
        "1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
        "\n",
        "1. Open this notebook in the Jupyter Notebook Dashboard."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7EUnXsZhAGF"
      },
      "source": [
        "### Install additional packages\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IaYsrh0Tc17L"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "    USER_FLAG = \"\"\n",
        "else:\n",
        "    USER_FLAG = \"--user\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yxtzwPPNZ-SH"
      },
      "outputs": [],
      "source": [
        "!pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hhq5zEbGg0XX"
      },
      "source": [
        "### Restart the kernel\n",
        "\n",
        "After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EzrelQZ22IZj"
      },
      "outputs": [],
      "source": [
        "# Automatically restart kernel after installs\n",
        "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": "6GPgNN7eeX1l"
      },
      "source": [
        "Check the versions of the packages you installed.  The KFP SDK version should be >=1.6."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NN0mULkEeb84"
      },
      "outputs": [],
      "source": [
        "!python3 -c \"import kfp; print('KFP SDK version: {}'.format(kfp.__version__))\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWEdiXsJg0XY"
      },
      "source": [
        "## Before you begin\n",
        "\n",
        "This notebook does not require a GPU runtime."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3ysRXrDl91t"
      },
      "source": [
        "### Set up your Google Cloud project\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
        "\n",
        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "1. [Enable the Vertex AI, Cloud Storage, and Compute Engine APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage-component.googleapis.com). \n",
        "\n",
        "1. Follow the \"**Configuring your project**\" instructions from the Vertex Pipelines documentation.\n",
        "\n",
        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
        "\n",
        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook.\n",
        "\n",
        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WReHDGG5g0XY"
      },
      "source": [
        "#### Set your project ID\n",
        "\n",
        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oM1iC_MfAts1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "PROJECT_ID = \"\"\n",
        "\n",
        "# Get your Google Cloud project ID from gcloud\n",
        "if not os.getenv(\"IS_TESTING\"):\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": "markdown",
      "metadata": {
        "id": "qJYoRfYng0XZ"
      },
      "source": [
        "Otherwise, set your project ID here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riG_qUokg0XZ"
      },
      "outputs": [],
      "source": [
        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
        "    PROJECT_ID = \"python-docs-samples-tests\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06571eb4063b"
      },
      "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 it onto the name of resources you create in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "697568e92bd6"
      },
      "outputs": [],
      "source": [
        "from datetime import datetime\n",
        "\n",
        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr--iN2kAylZ"
      },
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
        "**If you are using Google Cloud Notebooks**, your environment is already\n",
        "authenticated. Skip this step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBCra4QMA2wR"
      },
      "source": [
        "**If you are using Colab**, run the cell below and follow the instructions\n",
        "when prompted to authenticate your account via oAuth.\n",
        "\n",
        "**Otherwise**, follow these steps:\n",
        "\n",
        "1. In the Cloud Console, go to the [**Create service account key**\n",
        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
        "\n",
        "2. Click **Create service account**.\n",
        "\n",
        "3. In the **Service account name** field, enter a name, and\n",
        "   click **Create**.\n",
        "\n",
        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
        "into the filter box, and select\n",
        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
        "\n",
        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
        "local environment.\n",
        "\n",
        "6. Enter the path to your service account key as the\n",
        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PyQmSRbKA8r-"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "# 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",
        "# If on Google Cloud Notebooks, then don't execute this code\n",
        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
        "        google_auth.authenticate_user()\n",
        "\n",
        "    # If you are running this notebook locally, replace the string below with the\n",
        "    # path to your service account key and run this cell to authenticate your GCP\n",
        "    # account.\n",
        "    elif not os.getenv(\"IS_TESTING\"):\n",
        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxhCPW6e46EF"
      },
      "source": [
        "### Create a Cloud Storage bucket as necessary\n",
        "\n",
        "You will need a Cloud Storage bucket for this example.  If you don't have one that you want to use, you can make one now.\n",
        "\n",
        "\n",
        "Set the name of your Cloud Storage bucket below. It must be unique across all\n",
        "Cloud Storage buckets.\n",
        "\n",
        "You may also change the `REGION` variable, which is used for operations\n",
        "throughout the rest of this notebook. Make sure to [choose a region where Vertex AI services are\n",
        "available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions). You may\n",
        "not use a Multi-Regional Storage bucket for training with Vertex AI."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MzGDU7TWdts_"
      },
      "outputs": [],
      "source": [
        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}\n",
        "REGION = \"us-central1\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cf221059d072"
      },
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EcIXiGsCePi"
      },
      "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": "NIq7R4HZCfIc"
      },
      "outputs": [],
      "source": [
        "! gsutil mb -l $REGION $BUCKET_NAME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucvCsknMCims"
      },
      "source": [
        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vhOb7YnwClBb"
      },
      "outputs": [],
      "source": [
        "! gsutil ls -al $BUCKET_NAME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XoEqT2Y4DJmf"
      },
      "source": [
        "### Import libraries and define constants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYtGjGG45ELJ"
      },
      "source": [
        "Define some constants.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5zmD19ryCre7"
      },
      "outputs": [],
      "source": [
        "PATH=%env PATH\n",
        "%env PATH={PATH}:/home/jupyter/.local/bin\n",
        "\n",
        "USER = \"your-user-name\"  # <---CHANGE THIS\n",
        "PIPELINE_ROOT = \"{}/pipeline_root/{}\".format(BUCKET_NAME, USER)\n",
        "\n",
        "PIPELINE_ROOT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IprQaSI25oSk"
      },
      "source": [
        "Do some imports:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFDUBveR5UfJ"
      },
      "outputs": [],
      "source": [
        "import kfp\n",
        "from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
        "from kfp.v2 import compiler\n",
        "from kfp.v2.google.client import AIPlatformClient"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4MjdglUT3Sw"
      },
      "source": [
        "## Define an AutoML Tabular regression pipeline that uses components from `google_cloud_pipeline_components`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pf0pugbvftD1"
      },
      "source": [
        "Create a managed image dataset from a CSV file and train it using AutoML Tabular Training.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yYGHro4qFhp5"
      },
      "outputs": [],
      "source": [
        "TRAIN_FILE_NAME = \"california_housing_train.csv\"\n",
        "!gsutil cp gs://aju-dev-demos-codelabs/sample_data/california_housing_train.csv {PIPELINE_ROOT}/data/\n",
        "\n",
        "gcs_csv_path = f\"{PIPELINE_ROOT}/data/{TRAIN_FILE_NAME}\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fjGiImBezMo"
      },
      "source": [
        "Define the pipeline:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vEEr62NUftD1"
      },
      "outputs": [],
      "source": [
        "@kfp.dsl.pipeline(name=\"automl-tab-training-v2\")\n",
        "def pipeline(project: str = PROJECT_ID):\n",
        "\n",
        "    dataset_create_op = gcc_aip.TabularDatasetCreateOp(\n",
        "        project=project, display_name=\"housing\", gcs_source=gcs_csv_path\n",
        "    )\n",
        "\n",
        "    training_op = gcc_aip.AutoMLTabularTrainingJobRunOp(\n",
        "        project=project,\n",
        "        display_name=\"train-housing-automl_1\",\n",
        "        optimization_prediction_type=\"regression\",\n",
        "        optimization_objective=\"minimize-rmse\",\n",
        "        column_transformations=[\n",
        "            {\"numeric\": {\"column_name\": \"longitude\"}},\n",
        "            {\"numeric\": {\"column_name\": \"latitude\"}},\n",
        "            {\"numeric\": {\"column_name\": \"housing_median_age\"}},\n",
        "            {\"numeric\": {\"column_name\": \"total_rooms\"}},\n",
        "            {\"numeric\": {\"column_name\": \"total_bedrooms\"}},\n",
        "            {\"numeric\": {\"column_name\": \"population\"}},\n",
        "            {\"numeric\": {\"column_name\": \"households\"}},\n",
        "            {\"numeric\": {\"column_name\": \"median_income\"}},\n",
                     {\"numeric\": {\"column_name\": \"median_house_value\"}},\n",
        "        ],\n",
        "        dataset=dataset_create_op.outputs[\"dataset\"],\n",
        "        target_column=\"median_house_value\",\n",
        "    )\n",
        "\n",
        "    deploy_op = gcc_aip.ModelDeployOp(  # noqa: F841\n",
        "        model=training_op.outputs[\"model\"],\n",
        "        project=project,\n",
        "        machine_type=\"n1-standard-4\",\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Hl1iYEKSzjP"
      },
      "source": [
        "## Compile and run the pipeline\n",
        "\n",
        "Now, you're ready to compile the pipeline:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ycRc83B6bbfO"
      },
      "outputs": [],
      "source": [
        "from kfp.v2 import compiler  # noqa: F811\n",
        "\n",
        "compiler.Compiler().compile(\n",
        "    pipeline_func=pipeline, package_path=\"tab_regression_pipeline.json\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfNuzFswBB4g"
      },
      "source": [
        "The pipeline compilation generates the `tab_regression_pipeline.json` job spec file.\n",
        "\n",
        "Next, instantiate an API client object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hl5Q74_gkW2c"
      },
      "outputs": [],
      "source": [
        "from kfp.v2.google.client import AIPlatformClient  # noqa: F811\n",
        "\n",
        "api_client = AIPlatformClient(project_id=PROJECT_ID, region=REGION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jrn6saiQsPh"
      },
      "source": [
        "Then, you run the defined pipeline like this: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R4Ha4FoDQpkd"
      },
      "outputs": [],
      "source": [
        "response = api_client.create_run_from_job_spec(\n",
        "    \"tab_regression_pipeline.json\",\n",
        "    pipeline_root=PIPELINE_ROOT,\n",
        "    parameter_values={\"project\": PROJECT_ID},\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvBTCP318RKs"
      },
      "source": [
        "Click on the generated link to see your run in the Cloud Console.  It should look something like this as it is running:\n",
        "\n",
        "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" width=\"40%\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNU7IhpB3Hry"
      },
      "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",
        "- Delete Cloud Storage objects that were created.  Uncomment and run the command in the cell below **only if you are not using the `PIPELINE_ROOT` path for any other purpose**.\n",
        "- Delete your deployed model: first, undeploy it from its *endpoint*, then delete the model and endpoint.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6vWg3Kly3Hry"
      },
      "outputs": [],
      "source": [
        "# Warning: this command will delete ALL Cloud Storage objects under the PIPELINE_ROOT path.\n",
        "# ! gsutil -m rm -r $PIPELINE_ROOT"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "google-cloud-pipeline-components_automl_tabular.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}