notebooks/01pipelines_intro_kfp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "copyright"
   },
   "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": "title:generic"
   },
   "source": [
    "# Vertex AI Pipelines: Pipelines introduction for KFP\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/pipelines/pipelines_intro_kfp.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/master/notebooks/official/pipelines/pipelines_intro_kfp.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/pipelines/pipelines_intro_kfp.ipynb\">\n",
    "      Open in Vertex AI Workbench\n",
    "    </a>\n",
    "  </td>\n",
    "</table>\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "overview:pipelines,intro"
   },
   "source": [
    "## Overview\n",
    "\n",
    "This notebook provides an introduction to using [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) with [the Kubeflow Pipelines (KFP) SDK](https://www.kubeflow.org/docs/components/pipelines/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "objective:pipelines,intro"
   },
   "source": [
    "### Objective\n",
    "\n",
    "The steps performed include:\n",
    "\n",
    "- Define and compile a pipeline.\n",
    "- Schedule a recurring pipeline run.\n",
    "- Specify which service account to use for a pipeline run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "costs:functions,scheduler"
   },
   "source": [
    "### Costs\n",
    "\n",
    "This tutorial uses billable components of Google Cloud:\n",
    "\n",
    "* Vertex AI\n",
    "* Cloud Storage\n",
    "* Cloud Functions\n",
    "* Cloud Scheduler\n",
    "\n",
    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
    "[Cloud Storage pricing](https://cloud.google.com/storage/pricing),\n",
    "[Cloud Functions pricing](ttps://cloud.google.com/functions/pricing), and\n",
    "[Clould Scheduler pricing]((https://cloud.google.com/scheduler/pricing)),\n",
    "and use the [Pricing\n",
    "Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "setup_local"
   },
   "source": [
    "### Set up your local development environment\n",
    "\n",
    "If you are using Colab or Google Cloud Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
    "\n",
    "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
    "\n",
    "- The Cloud Storage SDK\n",
    "- Git\n",
    "- Python 3\n",
    "- virtualenv\n",
    "- Jupyter notebook running in a virtual environment with Python 3\n",
    "\n",
    "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
    "\n",
    "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
    "\n",
    "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
    "\n",
    "3. [Install virtualenv](Ihttps://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3.\n",
    "\n",
    "4. Activate that environment and run `pip3 install Jupyter` in a terminal shell to install Jupyter.\n",
    "\n",
    "5. Run `jupyter notebook` on the command line in a terminal shell to launch Jupyter.\n",
    "\n",
    "6. Open this notebook in the Jupyter Notebook Dashboard.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "install_aip:mbsdk"
   },
   "source": [
    "## Installation\n",
    "\n",
    "Install the latest version of Vertex AI SDK for Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "install_aip:mbsdk"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Google Cloud Notebook\n",
    "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
    "    USER_FLAG = \"--user\"\n",
    "else:\n",
    "    USER_FLAG = \"\"\n",
    "\n",
    "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "install_storage"
   },
   "source": [
    "Install the latest GA version of *google-cloud-storage* library as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "install_storage"
   },
   "outputs": [],
   "source": [
    "! pip3 install -U google-cloud-storage $USER_FLAG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "install_kfp"
   },
   "source": [
    "Install the latest GA version of *KFP SDK* library as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "install_kfp"
   },
   "outputs": [],
   "source": [
    "! pip3 install $USER kfp --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "restart"
   },
   "source": [
    "### Restart the kernel\n",
    "\n",
    "Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "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": "check_versions"
   },
   "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": "check_versions:kfp"
   },
   "outputs": [],
   "source": [
    "! python3 -c \"import kfp; print('KFP SDK version: {}'.format(kfp.__version__))\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "before_you_begin:nogpu"
   },
   "source": [
    "## Before you begin\n",
    "\n",
    "### GPU runtime\n",
    "\n",
    "This tutorial does not require a GPU runtime.\n",
    "\n",
    "### Set up your Google Cloud project\n",
    "\n",
    "**The following steps are required, regardless of your notebook environment.**\n",
    "\n",
    "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
    "\n",
    "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
    "\n",
    "3. [Enable the Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
    "\n",
    "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n",
    "\n",
    "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
    "Cloud SDK uses the right project for all the commands in this notebook.\n",
    "\n",
    "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "set_project_id"
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "autoset_project_id"
   },
   "outputs": [],
   "source": [
    "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
    "    # Get your GCP project id from gcloud\n",
    "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
    "    PROJECT_ID = shell_output[0]\n",
    "    print(\"Project ID:\", PROJECT_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "set_gcloud_project_id"
   },
   "outputs": [],
   "source": [
    "! gcloud config set project $PROJECT_ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "region"
   },
   "source": [
    "#### Region\n",
    "\n",
    "You can also change the `REGION` variable, which is used for operations\n",
    "throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
    "\n",
    "- Americas: `us-central1`\n",
    "- Europe: `europe-west4`\n",
    "- Asia Pacific: `asia-east1`\n",
    "\n",
    "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
    "\n",
    "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "region"
   },
   "outputs": [],
   "source": [
    "REGION = \"us-central1\"  # @param {type: \"string\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "timestamp"
   },
   "source": [
    "#### Timestamp\n",
    "\n",
    "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "timestamp"
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gcp_authenticate"
   },
   "source": [
    "### Authenticate your Google Cloud account\n",
    "\n",
    "**If you are using Google Cloud Notebook**, 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gcp_authenticate"
   },
   "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 Google Cloud Notebook, then don't execute this code\n",
    "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
    "    if \"google.colab\" in sys.modules:\n",
    "        from google.colab import auth as google_auth\n",
    "\n",
    "        google_auth.authenticate_user()\n",
    "\n",
    "    # If you are running this notebook locally, replace the string below with the\n",
    "    # path to your service account key and run this cell to authenticate your GCP\n",
    "    # account.\n",
    "    elif not os.getenv(\"IS_TESTING\"):\n",
    "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bucket:mbsdk"
   },
   "source": [
    "### Create a Cloud Storage bucket\n",
    "\n",
    "**The following steps are required, regardless of your notebook environment.**\n",
    "\n",
    "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
    "\n",
    "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bucket"
   },
   "outputs": [],
   "source": [
    "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "autoset_bucket"
   },
   "outputs": [],
   "source": [
    "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
    "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "create_bucket"
   },
   "source": [
    "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "create_bucket"
   },
   "outputs": [],
   "source": [
    "! gsutil mb -l $REGION $BUCKET_NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "validate_bucket"
   },
   "source": [
    "Finally, validate access to your Cloud Storage bucket by examining its contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "validate_bucket"
   },
   "outputs": [],
   "source": [
    "! gsutil ls -al $BUCKET_NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "set_service_account"
   },
   "source": [
    "#### Service Account\n",
    "\n",
    "**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "set_service_account"
   },
   "outputs": [],
   "source": [
    "SERVICE_ACCOUNT = \"[your-service-account]\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "autoset_service_account"
   },
   "outputs": [],
   "source": [
    "if (\n",
    "    SERVICE_ACCOUNT == \"\"\n",
    "    or SERVICE_ACCOUNT is None\n",
    "    or SERVICE_ACCOUNT == \"[your-service-account]\"\n",
    "):\n",
    "    # Get your GCP project id from gcloud\n",
    "    shell_output = !gcloud auth list 2>/dev/null\n",
    "    SERVICE_ACCOUNT = shell_output[2].strip()\n",
    "    print(\"Service Account:\", SERVICE_ACCOUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "set_service_account:pipelines"
   },
   "source": [
    "#### Set service account access for Vertex AI Pipelines\n",
    "\n",
    "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "set_service_account:pipelines"
   },
   "outputs": [],
   "source": [
    "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_NAME\n",
    "\n",
    "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_NAME"
   ]
  },
  {
   "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 google.cloud.aiplatform as aip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aip_constants:endpoint"
   },
   "source": [
    "#### Vertex AI constants\n",
    "\n",
    "Setup up the following constants for Vertex AI:\n",
    "\n",
    "- `API_ENDPOINT`: The Vertex AI API service endpoint for `Dataset`, `Model`, `Job`, `Pipeline` and `Endpoint` services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aip_constants:endpoint"
   },
   "outputs": [],
   "source": [
    "# API service endpoint\n",
    "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipeline_constants"
   },
   "source": [
    "#### Vertex AI Pipelines constants\n",
    "\n",
    "Setup up the following constants for Vertex AI Pipelines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipeline_constants"
   },
   "outputs": [],
   "source": [
    "PIPELINE_ROOT = \"{}/pipeline_root/intro\".format(BUCKET_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "additional_imports"
   },
   "source": [
    "Additional imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "import_kfp:namedtuple"
   },
   "outputs": [],
   "source": [
    "from typing import NamedTuple\n",
    "\n",
    "from kfp import dsl\n",
    "from kfp.v2 import compiler\n",
    "from kfp.v2.dsl import component"
   ]
  },
  {
   "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": "init_aip:mbsdk"
   },
   "outputs": [],
   "source": [
    "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "define_component:hello_world"
   },
   "source": [
    "### Define Python function-based pipeline components\n",
    "\n",
    "In this tutorial, you define a simple pipeline that has three steps, where each step is defined as a component.\n",
    "\n",
    "#### Define hello_world component\n",
    "\n",
    "First, define a component based on a very simple Python function. It takes a string input parameter and returns that value as output.\n",
    "\n",
    "Note the use of the `@component` decorator, which compiles the function to a KFP component when evaluated.  For example purposes, this example specifies a base image to use for the component (`python:3.9`), and a component YAML file, `hw.yaml`. The compiled component specification is written to this file.  (The default base image is `python:3.7`, which would of course work just fine too)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "define_component:hello_world"
   },
   "outputs": [],
   "source": [
    "@component(output_component_file=\"hw.yaml\", base_image=\"python:3.9\")\n",
    "def hello_world(text: str) -> str:\n",
    "    print(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "define_component:hello_world"
   },
   "source": [
    "As you'll see below, compilation of this component creates a [task factory function](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/)—called `hello_world`— that you can use in defining a pipeline step.\n",
    "\n",
    "While not shown here, if you want to share this component definition, or use it in another context, you could also load it from its yaml file like this:\n",
    "`hello_world_op = components.load_component_from_file('./hw.yaml')`.\n",
    "You can also use the `load_component_from_url` method, if your component yaml file is stored online. (For GitHub URLs, load the 'raw' file.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "define_component:two_outputs"
   },
   "source": [
    "#### Define two_outputs component\n",
    "\n",
    "The first component below, `two_outputs`, demonstrates installing a package -- in this case the `google-cloud-storage` package. Alternatively, you can specify a base image that includes the necessary installations.\n",
    "\n",
    "*Note:* The component function won't actually use the package.\n",
    "\n",
    "Alternatively, you can specify a base image that includes the necessary installations.\n",
    "\n",
    "The `two_outputs` component returns two named outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "define_component:two_outputs"
   },
   "outputs": [],
   "source": [
    "@component(packages_to_install=[\"google-cloud-storage\"])\n",
    "def two_outputs(\n",
    "    text: str,\n",
    ") -> NamedTuple(\n",
    "    \"Outputs\",\n",
    "    [\n",
    "        (\"output_one\", str),  # Return parameters\n",
    "        (\"output_two\", str),\n",
    "    ],\n",
    "):\n",
    "    # the import is not actually used for this simple example, but the import\n",
    "    # is successful, as it was included in the `packages_to_install` list.\n",
    "    from google.cloud import storage  # noqa: F401\n",
    "\n",
    "    o1 = f\"output one from text: {text}\"\n",
    "    o2 = f\"output two from text: {text}\"\n",
    "    print(\"output one: {}; output_two: {}\".format(o1, o2))\n",
    "    return (o1, o2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "define_component:consumer"
   },
   "source": [
    "#### Define the consumer component\n",
    "\n",
    "The third component, `consumer`, takes three string inputs and prints them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "define_component:consumer"
   },
   "outputs": [],
   "source": [
    "@component\n",
    "def consumer(text1: str, text2: str, text3: str):\n",
    "    print(f\"text1: {text1}; text2: {text2}; text3: {text3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "define_pipeline:intro"
   },
   "source": [
    "### Define a pipeline that uses the components\n",
    "\n",
    "Next, define a pipeline that uses these three components.\n",
    "\n",
    "By evaluating the component definitions above, you've created task factory functions that are used in the pipeline definition to create the pipeline steps.\n",
    "\n",
    "The pipeline takes an input parameter, and passes that parameter as an argument to the first two pipeline steps (`hw_task` and `two_outputs_task`).\n",
    "\n",
    "Then, the third pipeline step (`consumer_task`) consumes the outputs of the first and second steps.  Because the `hello_world` component definition just returns one unnamed output, you refer to it as `hw_task.output`.  The `two_outputs` task returns two named outputs, which you access as `two_outputs_task.outputs[\"<output_name>\"]`.\n",
    "\n",
    "*Note:* In the `@dsl.pipeline` decorator, you're defining the `PIPELINE_ROOT` Cloud Storage path to use.  If you had not included that info here, it would be required to specify it when creating the pipeline run, as you'll see below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "define_pipeline:intro"
   },
   "outputs": [],
   "source": [
    "@dsl.pipeline(\n",
    "    name=\"hello-world-v2\",\n",
    "    description=\"A simple intro pipeline\",\n",
    "    pipeline_root=PIPELINE_ROOT,\n",
    ")\n",
    "def pipeline(text: str = \"hi there\"):\n",
    "    hw_task = hello_world(text)\n",
    "    two_outputs_task = two_outputs(text)\n",
    "    consumer_task = consumer(  # noqa: F841\n",
    "        hw_task.output,\n",
    "        two_outputs_task.outputs[\"output_one\"],\n",
    "        two_outputs_task.outputs[\"output_two\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "compile_pipeline"
   },
   "source": [
    "## Compile the pipeline\n",
    "\n",
    "Next, compile the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "compile_pipeline"
   },
   "outputs": [],
   "source": [
    "from kfp.v2 import compiler  # noqa: F811\n",
    "\n",
    "compiler.Compiler().compile(\n",
    "    pipeline_func=pipeline, package_path=\"intro_pipeline.json\".replace(\" \", \"_\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "run_pipeline:intro"
   },
   "source": [
    "## Run the pipeline\n",
    "\n",
    "Next, run the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "run_pipeline:intro"
   },
   "outputs": [],
   "source": [
    "DISPLAY_NAME = \"intro_\" + TIMESTAMP\n",
    "\n",
    "job = aip.PipelineJob(\n",
    "    display_name=DISPLAY_NAME,\n",
    "    template_path=\"intro_pipeline.json\".replace(\" \", \"_\"),\n",
    "    pipeline_root=PIPELINE_ROOT,\n",
    ")\n",
    "\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view_pipeline_run:intro"
   },
   "source": [
    "Click on the generated link to see your run in the Cloud Console.\n",
    "\n",
    "<!-- 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> -->\n",
    "\n",
    "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n",
    "\n",
    "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/intro_pipeline.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/intro_pipeline.png\" width=\"60%\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "schedule_pipeline_run"
   },
   "source": [
    "## Recurring pipeline runs: create a scheduled pipeline job\n",
    "\n",
    "This section shows how to create a **scheduled pipeline job**.  You do this using the pipeline you already defined.\n",
    "\n",
    "Under the hood, the scheduled jobs are supported by the Cloud Scheduler and a Cloud Functions function.  Check first that the APIs for both of these services are enabled.\n",
    "You will need to first enable the [enable the Cloud Scheduler API](http://console.cloud.google.com/apis/library/cloudscheduler.googleapis.com) and the [Cloud Functions and Cloud Build APIs](https://console.cloud.google.com/flows/enableapi?apiid=cloudfunctions,cloudbuild.googleapis.com) if you have not already done so.\n",
    "Note:you need to [create an App Engine app for your project](https://cloud.google.com/scheduler/docs/quickstart) if one does not already exist.\n",
    "\n",
    "\n",
    "See the [Cloud Scheduler](https://cloud.google.com/scheduler/docs/configuring/cron-job-schedules) documentation for more on the cron syntax.\n",
    "\n",
    "Create a scheduled pipeline job, passing as an argument the job specification file that you compiled above.\n",
    "\n",
    "*Note:* You can pass a `parameter_values` dict that specifies the pipeline input parameters you want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "schedule_pipeline_run"
   },
   "outputs": [],
   "source": [
    "if not os.getenv(\"IS_TESTING\"):\n",
    "    from kfp.v2.google.client import AIPlatformClient  # noqa: F811\n",
    "\n",
    "    api_client = AIPlatformClient(project_id=PROJECT_ID, region=REGION)\n",
    "\n",
    "    # adjust time zone and cron schedule as necessary\n",
    "    response = api_client.create_schedule_from_job_spec(\n",
    "        job_spec_path=\"intro_pipeline.json\",\n",
    "        schedule=\"2 * * * *\",\n",
    "        time_zone=\"America/Los_Angeles\",  # change this as necessary\n",
    "        parameter_values={\"text\": \"Hello world!\"},\n",
    "        # pipeline_root=PIPELINE_ROOT  # this argument is necessary if you did not specify PIPELINE_ROOT as part of the pipeline definition.\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "schedule_pipeline_run"
   },
   "source": [
    "Once the scheduled job is created, you can see it listed in the [Cloud Scheduler](https://console.cloud.google.com/cloudscheduler/) panel in the Console.\n",
    "\n",
    "<a href=\"https://storage.googleapis.com/amy-jo/images/kf-pls/pipelines_scheduler.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/kf-pls/pipelines_scheduler.png\" width=\"95%\"/></a>\n",
    "\n",
    "You can test the setup from the Cloud Scheduler panel by clicking 'RUN NOW'.\n",
    "\n",
    "> **Note**: The implementation is using a Cloud Functions function, which you can see listed in the [Cloud Functions](https://console.cloud.google.com/functions/list) panel in the console as `templated_http_request-v1`.\n",
    "Don't delete this function, as it will prevent the Cloud Scheduler jobs from actually kicking off the pipeline run.  If you do delete it, create a new scheduled job in order to recreate the function.\n",
    "\n",
    "When you're done experimenting, you probably want to **PAUSE** your scheduled job from the Cloud Scheduler panel, so that the recurrent jobs do not keep running."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "run_pipeline_service_account"
   },
   "source": [
    "## Specifying a service account to use for a pipeline run\n",
    "\n",
    "By default, the [service account](https://cloud.google.com/iam/docs/service-accounts) used for your pipeline run is your [default compute engine service account](https://cloud.google.com/compute/docs/access/service-accounts#default_service_account).\n",
    "However, you might want to run pipelines with permissions to access different roles than those configured for your default SA (e.g. perhaps using a more restricted set of permissions).\n",
    "\n",
    "If you want to execute your pipeline using a different service account, this is straightforward to do.  You just need to give the new service account the correct permissions.\n",
    "\n",
    "### Create a service account\n",
    "\n",
    "Once your service account is configured, you pass it as an argument to the `create_run_from_job_spec` method. The pipeline job runs with the permissions of the given service account.\n",
    "\n",
    "Learn about [creating and configuring a service account to work with Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/configure-project#service-account)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "run_pipeline_service_account"
   },
   "outputs": [],
   "source": [
    "if not os.getenv(\"IS_TESTING\"):\n",
    "    response = api_client.create_run_from_job_spec(\n",
    "        job_spec_path=\"intro_pipeline.json\",\n",
    "        pipeline_root=PIPELINE_ROOT,\n",
    "        service_account=SERVICE_ACCOUNT,  # <-- CHANGE to use non-default service account\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "run_pipeline_caching"
   },
   "source": [
    "## Pipeline step caching\n",
    "\n",
    "By default, pipeline step caching is enabled. This means that the results of previous step executions are reused when possible.\n",
    "\n",
    "if you want to disable caching for a pipeline run, you can pass the `enable_caching=False` argument to the `PipelineJob` constructor when you submit the pipeline job, as shown below.  Try submitting the example pipeline job again, first with and then without caching enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "run_pipeline_caching"
   },
   "outputs": [],
   "source": [
    "job = aip.PipelineJob(\n",
    "    display_name=\"intro_\" + TIMESTAMP,\n",
    "    template_path=\"intro_pipeline.json\",\n",
    "    enable_caching=False,\n",
    ")\n",
    "\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest"
   },
   "source": [
    "## Using the Pipelines REST API\n",
    "\n",
    "At times you may want to use the REST API instead of the Python KFP SDK.  Below are examples of how to do that.\n",
    "\n",
    "Where a command requires a pipeline ID, you can get that info from the \"Run\" column in the pipelines list— as shown below— as well as from the 'details' page for a given pipeline.  You can also see that info when you list pipeline job(s) via the API.\n",
    "\n",
    "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/pipeline_id.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/pipeline_id.png\" width=\"80%\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest_list"
   },
   "source": [
    "### List pipeline jobs\n",
    "\n",
    "*Note:* This request may generate a large response if you have many pipeline runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipelines_rest_list"
   },
   "outputs": [],
   "source": [
    "! curl -X GET -H \"Authorization: Bearer $(gcloud auth print-access-token)\"   -H \"Content-Type: application/json\"   https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/pipelineJobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest_create"
   },
   "source": [
    "### Create a pipeline job\n",
    "\n",
    "For this REST request, you submit a compiled pipeline job specification.  We're using the one you generated previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipelines_rest_create"
   },
   "outputs": [],
   "source": [
    "output = ! curl -X POST  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"Content-Type: application/json; charset=utf-8\"   https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/pipelineJobs  --data \"@intro_pipeline.json\"\n",
    "\n",
    "PIPELINE_RUN_ID = output[5].split(\"/\")[-1].split('\"')[0]\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest_get"
   },
   "source": [
    "### Get a pipeline job from its ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipelines_rest_get"
   },
   "outputs": [],
   "source": [
    "! curl -X GET -H \"Authorization: Bearer $(gcloud auth print-access-token)\"   -H \"Content-Type: application/json\"   https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/pipelineJobs/{PIPELINE_RUN_ID}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest_cancel"
   },
   "source": [
    "### Cancel a pipeline job given its ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipelines_rest_cancel"
   },
   "outputs": [],
   "source": [
    "! curl -X POST -H \"Authorization: Bearer $(gcloud auth print-access-token)\"   -H \"Content-Type: application/json\"   https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/pipelineJobs/{PIPELINE_RUN_ID}:cancel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pipelines_rest_delete"
   },
   "source": [
    "### Delete a pipeline job given its ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pipelines_rest_delete"
   },
   "outputs": [],
   "source": [
    "! curl -X DELETE -H \"Authorization: Bearer $(gcloud auth print-access-token)\"   -H \"Content-Type: application/json\"   https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{REGION}/pipelineJobs/{PIPELINE_RUN_ID}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cleanup:pipelines"
   },
   "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 -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
    "\n",
    "- Dataset\n",
    "- Pipeline\n",
    "- Model\n",
    "- Endpoint\n",
    "- Batch Job\n",
    "- Custom Job\n",
    "- Hyperparameter Tuning Job\n",
    "- Cloud Storage Bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cleanup:pipelines"
   },
   "outputs": [],
   "source": [
    "delete_dataset = True\n",
    "delete_pipeline = True\n",
    "delete_model = True\n",
    "delete_endpoint = True\n",
    "delete_batchjob = True\n",
    "delete_customjob = True\n",
    "delete_hptjob = True\n",
    "delete_bucket = True\n",
    "\n",
    "try:\n",
    "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
    "        models = aip.Model.list(\n",
    "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "        )\n",
    "        model = models[0]\n",
    "        aip.Model.delete(model)\n",
    "        print(\"Deleted model:\", model)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "try:\n",
    "    if delete_endpoint and \"DISPLAY_NAME\" in globals():\n",
    "        endpoints = aip.Endpoint.list(\n",
    "            filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
    "        )\n",
    "        endpoint = endpoints[0]\n",
    "        endpoint.undeploy_all()\n",
    "        aip.Endpoint.delete(endpoint.resource_name)\n",
    "        print(\"Deleted endpoint:\", endpoint)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
    "    if \"none\" == \"tabular\":\n",
    "        try:\n",
    "            datasets = aip.TabularDataset.list(\n",
    "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "            )\n",
    "            dataset = datasets[0]\n",
    "            aip.TabularDataset.delete(dataset.resource_name)\n",
    "            print(\"Deleted dataset:\", dataset)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "\n",
    "    if \"none\" == \"image\":\n",
    "        try:\n",
    "            datasets = aip.ImageDataset.list(\n",
    "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "            )\n",
    "            dataset = datasets[0]\n",
    "            aip.ImageDataset.delete(dataset.resource_name)\n",
    "            print(\"Deleted dataset:\", dataset)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "\n",
    "    if \"none\" == \"text\":\n",
    "        try:\n",
    "            datasets = aip.TextDataset.list(\n",
    "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "            )\n",
    "            dataset = datasets[0]\n",
    "            aip.TextDataset.delete(dataset.resource_name)\n",
    "            print(\"Deleted dataset:\", dataset)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "\n",
    "    if \"none\" == \"video\":\n",
    "        try:\n",
    "            datasets = aip.VideoDataset.list(\n",
    "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "            )\n",
    "            dataset = datasets[0]\n",
    "            aip.VideoDataset.delete(dataset.resource_name)\n",
    "            print(\"Deleted dataset:\", dataset)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "\n",
    "try:\n",
    "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
    "        pipelines = aip.PipelineJob.list(\n",
    "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
    "        )\n",
    "        pipeline = pipelines[0]\n",
    "        aip.PipelineJob.delete(pipeline.resource_name)\n",
    "        print(\"Deleted pipeline:\", pipeline)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
    "    ! gsutil rm -r $BUCKET_NAME"
   ]
  }
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