mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
Compare commits
3
Commits
uj10
...
automl_filenames
| Author | SHA1 | Date | |
|---|---|---|---|
 Andrew FerlitschandGitHub
|
8be3a063e1 | ||
|
Andrew Ferlitsch
|
c6f760455a | ||
|
Andrew Ferlitsch
|
b10d47e972 |
@@ -1,879 +0,0 @@
|
||||
{
|
||||
"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": [
|
||||
"# E2E ML on GCP: MLOps stage 6 : serving: get started with re-importing AutoML tabular models\n",
|
||||
"\n",
|
||||
"<table align=\"left\">\n",
|
||||
" <td>\n",
|
||||
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.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://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.ipynb\">\n",
|
||||
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
|
||||
" </a>\n",
|
||||
" </td>\n",
|
||||
" <td>\n",
|
||||
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.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",
|
||||
"\n",
|
||||
"This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with AutoML Training."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "objective:mlops,stage2,get_started_automl_training"
|
||||
},
|
||||
"source": [
|
||||
"### Objective\n",
|
||||
"\n",
|
||||
"In this tutorial, you learn how to use `AutoML Tabular` for re-importing exported model artifacts as a `Model` resource. This is useful for example, if one wants to move the exported model across projects.\n",
|
||||
"\n",
|
||||
"This tutorial uses the following Google Cloud ML services:\n",
|
||||
"\n",
|
||||
"- `AutoML Tabular`\n",
|
||||
"- `Vertex AI Model` resource\n",
|
||||
"- `Vertex AI Prediction`\n",
|
||||
"\n",
|
||||
"The steps performed include:\n",
|
||||
"\n",
|
||||
"- Importing a pretrained AutoML tabular exported model artifacts, as a `Model` resource.\n",
|
||||
"- Create an `Endpoint` resource.\n",
|
||||
"- Deploy the `Model` resource to the `Endpoint` resource.\n",
|
||||
"- Make a prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "dataset:flowers,icn"
|
||||
},
|
||||
"source": [
|
||||
"### Dataset\n",
|
||||
"\n",
|
||||
"This tutorial uses a pretrained AutoML tabular model with exported model artifacts.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The tabular dataset used for the pretrained model is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp).\n",
|
||||
"\n",
|
||||
"*Note:* This version of the exported model contains the custom op and requires the model server: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "fb3451ce8e47"
|
||||
},
|
||||
"source": [
|
||||
"### Costs\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": "install_mlops"
|
||||
},
|
||||
"source": [
|
||||
"## Installations\n",
|
||||
"\n",
|
||||
"Install the packages required for executing this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "install_mlops"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# The Vertex AI Workbench Notebook product has specific requirements\n",
|
||||
"IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
|
||||
"IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
|
||||
" \"/opt/deeplearning/metadata/env_version\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
|
||||
"USER_FLAG = \"\"\n",
|
||||
"if IS_WORKBENCH_NOTEBOOK:\n",
|
||||
" USER_FLAG = \"--user\"\n",
|
||||
"\n",
|
||||
"# Install the packages\n",
|
||||
"\n",
|
||||
"! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
|
||||
"! pip3 install --upgrade google-cloud-storage {USER_FLAG} -q"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "project_id"
|
||||
},
|
||||
"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 $300 free credit towards your compute/storage costs.\n",
|
||||
"\n",
|
||||
"1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
|
||||
"\n",
|
||||
"1. [Enable the Vertex AI, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage_component).\n",
|
||||
"\n",
|
||||
"1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
|
||||
"\n",
|
||||
"1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
|
||||
"Cloud SDK uses the right project for all the commands in this notebook.\n",
|
||||
"\n",
|
||||
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "56d591439df1"
|
||||
},
|
||||
"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": "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 = \"[your-region]\" # @param {type: \"string\"}\n",
|
||||
"\n",
|
||||
"if REGION == \"[your-region]\":\n",
|
||||
" REGION = \"us-central1\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "timestamp"
|
||||
},
|
||||
"source": [
|
||||
"#### Timestamp\n",
|
||||
"\n",
|
||||
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "timestamp"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "3ffa6b6c7cdb"
|
||||
},
|
||||
"source": [
|
||||
"### Authenticate your Google Cloud account\n",
|
||||
"\n",
|
||||
"**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
|
||||
"\n",
|
||||
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
|
||||
"\n",
|
||||
"**Otherwise**, follow these steps:\n",
|
||||
"\n",
|
||||
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
|
||||
"\n",
|
||||
"1. **Click Create service account**.\n",
|
||||
"\n",
|
||||
"2. In the **Service account name** field, enter a name, and click **Create**.\n",
|
||||
"\n",
|
||||
"3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
|
||||
"\n",
|
||||
"4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
|
||||
"\n",
|
||||
"5. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "2b72272258fc"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If you are running this notebook in Colab, run this cell and follow the\n",
|
||||
"# instructions to authenticate your GCP account. This provides access to your\n",
|
||||
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
|
||||
"# requests.\n",
|
||||
"\n",
|
||||
"import os\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"# If on Vertex AI Workbench, then don't execute this code\n",
|
||||
"IS_COLAB = \"google.colab\" in sys.modules\n",
|
||||
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
|
||||
" \"DL_ANACONDA_HOME\"\n",
|
||||
"):\n",
|
||||
" if \"google.colab\" in sys.modules:\n",
|
||||
" from google.colab import auth as google_auth\n",
|
||||
"\n",
|
||||
" google_auth.authenticate_user()\n",
|
||||
"\n",
|
||||
" # If you are running this notebook locally, replace the string below with the\n",
|
||||
" # path to your service account key and run this cell to authenticate your GCP\n",
|
||||
" # account.\n",
|
||||
" elif not os.getenv(\"IS_TESTING\"):\n",
|
||||
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "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 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 = \"[your-bucket-name]\" # @param {type:\"string\"}\n",
|
||||
"BUCKET_URI = f\"gs://{BUCKET_NAME}\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "autoset_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
|
||||
" BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
|
||||
" BUCKET_URI = \"gs://\" + BUCKET_NAME"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "create_bucket"
|
||||
},
|
||||
"source": [
|
||||
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "create_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil mb -l $REGION $BUCKET_URI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "validate_bucket"
|
||||
},
|
||||
"source": [
|
||||
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "validate_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil ls -al $BUCKET_URI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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": "init_aip:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 `DEPLOY_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": "machine:training"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
|
||||
" MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
|
||||
"else:\n",
|
||||
" MACHINE_TYPE = \"n1-standard\"\n",
|
||||
"\n",
|
||||
"VCPU = \"4\"\n",
|
||||
"DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
|
||||
"print(\"Train machine type\", DEPLOY_COMPUTE)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "import_file:u_dataset,csv"
|
||||
},
|
||||
"source": [
|
||||
"### Location of pretrained `AutoML Tabular` exported model\n",
|
||||
"\n",
|
||||
"Now set the variable `MODEL_PACKAGE` to the location of the exported model artifacts in Cloud Storage."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "import_file:flowers,csv,icn"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"MODEL_PACKAGE = \"gs://cloud-samples-data/vertex-ai/tabular-workflows/models/custom_op\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "quick_peek:csv"
|
||||
},
|
||||
"source": [
|
||||
"#### Quick peek at your model package.\n",
|
||||
"\n",
|
||||
"Next, take a look at the contents of the model package for the exported AutoML tabular model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "quick_peek:csv"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil ls {MODEL_PACKAGE}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "automl_tabular_intro"
|
||||
},
|
||||
"source": [
|
||||
"## AutoML tabular models\n",
|
||||
"\n",
|
||||
"AutoML can train the following types of tabular models:\n",
|
||||
"\n",
|
||||
"- classification\n",
|
||||
"- regression\n",
|
||||
"- forecasting\n",
|
||||
"\n",
|
||||
"A model can be trained for either automatic deployment to the cloud or exported for manual deployment to the cloud. In this tutorial, you use a pretrained exported AutoML tabular model.\n",
|
||||
"\n",
|
||||
"Learn more about [AutoML Model Types](https://cloud.google.com/vertex-ai/docs/start/automl-model-types)\n",
|
||||
"\n",
|
||||
"Learn more about [Exporting AutoML Tabular models](https://cloud.google.com/vertex-ai/docs/export/export-model-tabular)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "c10efb34321b"
|
||||
},
|
||||
"source": [
|
||||
"### Set the model server\n",
|
||||
"\n",
|
||||
"Next, you set the pre-built container for the model server. The container will be a version of `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server`. If the model package contains an `environment.json` file, use the container version specified by the key `container_uri`; otherwise, use `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:latest` "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "a5f271de9040"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"output = !gsutil cat {MODEL_PACKAGE}/environment.json\n",
|
||||
"\n",
|
||||
"MODEL_SERVER = json.loads(output[0])[\"container_uri\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "e8ce91147c93"
|
||||
},
|
||||
"source": [
|
||||
"### Upload the pretrained exported `AutoML Tabular` model package to a `Vertex AI Model` resource\n",
|
||||
"\n",
|
||||
"Next, you upload the model artifacts for the pretrained exported `AutoML Tabular` model into a `Vertex AI Model` resource, using the `Model.upload()` method with the following parameters:\n",
|
||||
"\n",
|
||||
"- `display_name`: A human readable name for the `Model` resource.\n",
|
||||
"- `artifact_uri`: The Cloud Storage location of the model package.\n",
|
||||
"- `serving_container_image_uri`: The serving container image.\n",
|
||||
"- `serving_container_ports`: The serving port.\n",
|
||||
"\n",
|
||||
"*Note:* When you upload the model artifacts to a `Vertex Model` resource, you specify the corresponding deployment container image."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "7988eae27f80"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = aiplatform.Model.upload(\n",
|
||||
" display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" artifact_uri=MODEL_PACKAGE,\n",
|
||||
" serving_container_image_uri=MODEL_SERVER,\n",
|
||||
" serving_container_ports=[8080],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "628de0914ba1"
|
||||
},
|
||||
"source": [
|
||||
"## Creating an `Endpoint` resource\n",
|
||||
"\n",
|
||||
"You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
|
||||
"\n",
|
||||
"In this example, the following parameters are specified:\n",
|
||||
"\n",
|
||||
"- `display_name`: A human readable name for the `Endpoint` resource.\n",
|
||||
"- `project`: Your project ID.\n",
|
||||
"- `location`: Your region.\n",
|
||||
"- `labels`: (optional) User defined metadata for the `Endpoint` in the form of key/value pairs.\n",
|
||||
"\n",
|
||||
"This method returns an `Endpoint` object.\n",
|
||||
"\n",
|
||||
"Learn more about [Vertex AI Endpoints](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "0ea443f9593b"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint = aiplatform.Endpoint.create(\n",
|
||||
" display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" project=PROJECT_ID,\n",
|
||||
" location=REGION,\n",
|
||||
" labels={\"your_key\": \"your_value\"},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(endpoint)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ca3fa3f6a894"
|
||||
},
|
||||
"source": [
|
||||
"## Deploying `Model` resources to an `Endpoint` resource.\n",
|
||||
"\n",
|
||||
"You can deploy one of more `Vertex AI Model` resource instances to the same endpoint. Each `Vertex AI Model` resource that is deployed will have its own deployment container for the serving binary. \n",
|
||||
"\n",
|
||||
"*Note:* For this example, you specified the deployment container for the exported AutoML Tabular model in the previous step of uploading the model artifacts to a `Vertex AI Model` resource.\n",
|
||||
"\n",
|
||||
"To deploy, you specify the following additional configuration settings:\n",
|
||||
"\n",
|
||||
"- The machine type.\n",
|
||||
"- The (if any) type and number of GPUs.\n",
|
||||
"- Static, manual or auto-scaling of VM instances.\n",
|
||||
"\n",
|
||||
"In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
|
||||
"\n",
|
||||
"- `model`: The `Model` resource.\n",
|
||||
"- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
|
||||
"- `machine_type`: The machine type for each VM instance.\n",
|
||||
"\n",
|
||||
"Do to the requirements to provision the resource, this may take upto a few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "4e93b034a72f"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = endpoint.deploy(\n",
|
||||
" model=model,\n",
|
||||
" deployed_model_display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" machine_type=DEPLOY_COMPUTE,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(response)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "f75331c946d5"
|
||||
},
|
||||
"source": [
|
||||
"## Make a prediction\n",
|
||||
"\n",
|
||||
"Finally, you make an online prediction using the `endpoint()` method, with the following parameters:\n",
|
||||
"\n",
|
||||
"- `instances`: The instances to predict.\n",
|
||||
"\n",
|
||||
"The following is the for a prediction request:\n",
|
||||
"\n",
|
||||
" [ INSTANCE_1, INSTANCE_2, ... ]\n",
|
||||
" \n",
|
||||
" INSTANCE : { \"column_1\": value, \"column_2\": value, ... }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "72683bd9d777"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"INSTANCES = [{\"year\": \"2020\", \"month\": \"1\", \"day\": \"23\"}]\n",
|
||||
"\n",
|
||||
"prediction = endpoint.predict(instances=INSTANCES)\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "endpoint_delete:mbsdk"
|
||||
},
|
||||
"source": [
|
||||
"#### Delete the endpoint\n",
|
||||
"\n",
|
||||
"The method 'delete()' will delete the endpoint."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "endpoint_delete:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint.undeploy_all()\n",
|
||||
"endpoint.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "model_delete:mbsdk"
|
||||
},
|
||||
"source": [
|
||||
"#### Delete the model\n",
|
||||
"\n",
|
||||
"The method 'delete()' will delete the model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "model_delete:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "cleanup"
|
||||
},
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "cleanup"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set this to true only if you'd like to delete your bucket\n",
|
||||
"delete_bucket = False\n",
|
||||
"\n",
|
||||
"if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
|
||||
" ! gsutil rm -r $BUCKET_URI"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "get_started_automl_tabular_exported_deploy.ipynb",
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
+1141
-1122
File diff suppressed because it is too large
Load Diff
+898
@@ -0,0 +1,898 @@
|
||||
{
|
||||
"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": [
|
||||
"# E2E ML on GCP: MLOps stage 6 : serving: get started with re-importing AutoML tabular models\n",
|
||||
"\n",
|
||||
"<table align=\"left\">\n",
|
||||
" <td>\n",
|
||||
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_exported_deploy.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://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_with_tabular_exported_deploy.ipynb\">\n",
|
||||
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
|
||||
" </a>\n",
|
||||
" </td>\n",
|
||||
" <td>\n",
|
||||
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_automl_with_tabular_exported_deploy.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",
|
||||
"\n",
|
||||
"This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with AutoML Training."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "objective:mlops,stage2,get_started_automl_training"
|
||||
},
|
||||
"source": [
|
||||
"### Objective\n",
|
||||
"\n",
|
||||
"In this tutorial, you learn how to use `AutoML Tabular` for re-importing exported model artifacts as a `Model` resource. This is useful for example, if one wants to move the exported model across projects.\n",
|
||||
"\n",
|
||||
"This tutorial uses the following Google Cloud ML services:\n",
|
||||
"\n",
|
||||
"- `AutoML Tabular`\n",
|
||||
"- `Vertex AI Model` resource\n",
|
||||
"- `Vertex AI Prediction`\n",
|
||||
"\n",
|
||||
"The steps performed include:\n",
|
||||
"\n",
|
||||
"- Importing a pretrained AutoML tabular exported model artifacts, as a `Model` resource.\n",
|
||||
"- Create an `Endpoint` resource.\n",
|
||||
"- Deploy the `Model` resource to the `Endpoint` resource.\n",
|
||||
"- Make a prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "dataset:flowers,icn"
|
||||
},
|
||||
"source": [
|
||||
"### Dataset\n",
|
||||
"\n",
|
||||
"This tutorial uses a pretrained AutoML tabular model with exported model artifacts.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The tabular dataset used for the pretrained model is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp).\n",
|
||||
"\n",
|
||||
"*Note:* This version of the exported model contains the custom op and requires the model server: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "fb3451ce8e47"
|
||||
},
|
||||
"source": [
|
||||
"### Costs\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": "install_mlops"
|
||||
},
|
||||
"source": [
|
||||
"## Installations\n",
|
||||
"\n",
|
||||
"Install the packages required for executing this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "install_mlops"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# The Vertex AI Workbench Notebook product has specific requirements\n",
|
||||
"IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
|
||||
"IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
|
||||
" \"/opt/deeplearning/metadata/env_version\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
|
||||
"USER_FLAG = \"\"\n",
|
||||
"if IS_WORKBENCH_NOTEBOOK:\n",
|
||||
" USER_FLAG = \"--user\"\n",
|
||||
"\n",
|
||||
"# Install the packages\n",
|
||||
"\n",
|
||||
"! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
|
||||
"! pip3 install --upgrade google-cloud-storage {USER_FLAG} -q"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "project_id"
|
||||
},
|
||||
"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 $300 free credit towards your compute/storage costs.\n",
|
||||
"\n",
|
||||
"1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
|
||||
"\n",
|
||||
"1. [Enable the Vertex AI, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage_component).\n",
|
||||
"\n",
|
||||
"1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
|
||||
"\n",
|
||||
"1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
|
||||
"Cloud SDK uses the right project for all the commands in this notebook.\n",
|
||||
"\n",
|
||||
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "56d591439df1"
|
||||
},
|
||||
"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": "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 = \"[your-region]\" # @param {type: \"string\"}\n",
|
||||
"\n",
|
||||
"if REGION == \"[your-region]\":\n",
|
||||
" REGION = \"us-central1\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "timestamp"
|
||||
},
|
||||
"source": [
|
||||
"#### Timestamp\n",
|
||||
"\n",
|
||||
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "timestamp"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "3ffa6b6c7cdb"
|
||||
},
|
||||
"source": [
|
||||
"### Authenticate your Google Cloud account\n",
|
||||
"\n",
|
||||
"**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
|
||||
"\n",
|
||||
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
|
||||
"\n",
|
||||
"**Otherwise**, follow these steps:\n",
|
||||
"\n",
|
||||
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
|
||||
"\n",
|
||||
"1. **Click Create service account**.\n",
|
||||
"\n",
|
||||
"2. In the **Service account name** field, enter a name, and click **Create**.\n",
|
||||
"\n",
|
||||
"3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
|
||||
"\n",
|
||||
"4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
|
||||
"\n",
|
||||
"5. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "2b72272258fc"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If you are running this notebook in Colab, run this cell and follow the\n",
|
||||
"# instructions to authenticate your GCP account. This provides access to your\n",
|
||||
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
|
||||
"# requests.\n",
|
||||
"\n",
|
||||
"import os\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"# If on Vertex AI Workbench, then don't execute this code\n",
|
||||
"IS_COLAB = \"google.colab\" in sys.modules\n",
|
||||
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
|
||||
" \"DL_ANACONDA_HOME\"\n",
|
||||
"):\n",
|
||||
" if \"google.colab\" in sys.modules:\n",
|
||||
" from google.colab import auth as google_auth\n",
|
||||
"\n",
|
||||
" google_auth.authenticate_user()\n",
|
||||
"\n",
|
||||
" # If you are running this notebook locally, replace the string below with the\n",
|
||||
" # path to your service account key and run this cell to authenticate your GCP\n",
|
||||
" # account.\n",
|
||||
" elif not os.getenv(\"IS_TESTING\"):\n",
|
||||
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "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 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 = \"[your-bucket-name]\" # @param {type:\"string\"}\n",
|
||||
"BUCKET_URI = f\"gs://{BUCKET_NAME}\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "autoset_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
|
||||
" BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
|
||||
" BUCKET_URI = \"gs://\" + BUCKET_NAME"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "create_bucket"
|
||||
},
|
||||
"source": [
|
||||
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "create_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil mb -l $REGION $BUCKET_URI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "validate_bucket"
|
||||
},
|
||||
"source": [
|
||||
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "validate_bucket"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil ls -al $BUCKET_URI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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": "init_aip:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 `DEPLOY_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": "machine:training"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
|
||||
" MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
|
||||
"else:\n",
|
||||
" MACHINE_TYPE = \"n1-standard\"\n",
|
||||
"\n",
|
||||
"VCPU = \"4\"\n",
|
||||
"DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
|
||||
"print(\"Train machine type\", DEPLOY_COMPUTE)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "import_file:u_dataset,csv"
|
||||
},
|
||||
"source": [
|
||||
"### Location of pretrained `AutoML Tabular` exported model\n",
|
||||
"\n",
|
||||
"Now set the variable `MODEL_PACKAGE` to the location of the exported model artifacts in Cloud Storage."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "import_file:flowers,csv,icn"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"MODEL_PACKAGE = \"gs://cloud-samples-data/vertex-ai/tabular-workflows/models/custom_op\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "quick_peek:csv"
|
||||
},
|
||||
"source": [
|
||||
"#### Quick peek at your model package.\n",
|
||||
"\n",
|
||||
"Next, take a look at the contents of the model package for the exported AutoML tabular model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "quick_peek:csv"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! gsutil ls {MODEL_PACKAGE}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "automl_tabular_intro"
|
||||
},
|
||||
"source": [
|
||||
"## AutoML tabular models\n",
|
||||
"\n",
|
||||
"AutoML can train the following types of tabular models:\n",
|
||||
"\n",
|
||||
"- classification\n",
|
||||
"- regression\n",
|
||||
"- forecasting\n",
|
||||
"\n",
|
||||
"A model can be trained for either automatic deployment to the cloud or exported for manual deployment to the cloud. In this tutorial, you use a pretrained exported AutoML tabular model.\n",
|
||||
"\n",
|
||||
"Learn more about [AutoML Model Types](https://cloud.google.com/vertex-ai/docs/start/automl-model-types)\n",
|
||||
"\n",
|
||||
"Learn more about [Exporting AutoML Tabular models](https://cloud.google.com/vertex-ai/docs/export/export-model-tabular)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "c10efb34321b"
|
||||
},
|
||||
"source": [
|
||||
"### Set the model server\n",
|
||||
"\n",
|
||||
"Next, you set the pre-built container for the model server. The container will be a version of `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server`. If the model package contains an `environment.json` file, use the container version specified by the key `container_uri`; otherwise, use `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:latest` "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "a5f271de9040"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"output = !gsutil cat {MODEL_PACKAGE}/environment.json\n",
|
||||
"\n",
|
||||
"MODEL_SERVER = json.loads(output[0])[\"container_uri\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "e8ce91147c93"
|
||||
},
|
||||
"source": [
|
||||
"### Upload the pretrained exported `AutoML Tabular` model package to a `Vertex AI Model` resource\n",
|
||||
"\n",
|
||||
"Next, you upload the model artifacts for the pretrained exported `AutoML Tabular` model into a `Vertex AI Model` resource, using the `Model.upload()` method with the following parameters:\n",
|
||||
"\n",
|
||||
"- `display_name`: A human readable name for the `Model` resource.\n",
|
||||
"- `artifact_uri`: The Cloud Storage location of the model package.\n",
|
||||
"- `serving_container_image_uri`: The serving container image.\n",
|
||||
"- `serving_container_ports`: The serving port.\n",
|
||||
"\n",
|
||||
"*Note:* When you upload the model artifacts to a `Vertex Model` resource, you specify the corresponding deployment container image."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "7988eae27f80"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = aiplatform.Model.upload(\n",
|
||||
" display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" artifact_uri=MODEL_PACKAGE,\n",
|
||||
" serving_container_image_uri=MODEL_SERVER,\n",
|
||||
" serving_container_ports=[8080],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "628de0914ba1"
|
||||
},
|
||||
"source": [
|
||||
"## Creating an `Endpoint` resource\n",
|
||||
"\n",
|
||||
"You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
|
||||
"\n",
|
||||
"In this example, the following parameters are specified:\n",
|
||||
"\n",
|
||||
"- `display_name`: A human readable name for the `Endpoint` resource.\n",
|
||||
"- `project`: Your project ID.\n",
|
||||
"- `location`: Your region.\n",
|
||||
"- `labels`: (optional) User defined metadata for the `Endpoint` in the form of key/value pairs.\n",
|
||||
"\n",
|
||||
"This method returns an `Endpoint` object.\n",
|
||||
"\n",
|
||||
"Learn more about [Vertex AI Endpoints](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "0ea443f9593b"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint = aiplatform.Endpoint.create(\n",
|
||||
" display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" project=PROJECT_ID,\n",
|
||||
" location=REGION,\n",
|
||||
" labels={\"your_key\": \"your_value\"},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(endpoint)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ca3fa3f6a894"
|
||||
},
|
||||
"source": [
|
||||
"## Deploying `Model` resources to an `Endpoint` resource.\n",
|
||||
"\n",
|
||||
"You can deploy one of more `Vertex AI Model` resource instances to the same endpoint. Each `Vertex AI Model` resource that is deployed will have its own deployment container for the serving binary. \n",
|
||||
"\n",
|
||||
"*Note:* For this example, you specified the deployment container for the exported AutoML Tabular model in the previous step of uploading the model artifacts to a `Vertex AI Model` resource.\n",
|
||||
"\n",
|
||||
"To deploy, you specify the following additional configuration settings:\n",
|
||||
"\n",
|
||||
"- The machine type.\n",
|
||||
"- The (if any) type and number of GPUs.\n",
|
||||
"- Static, manual or auto-scaling of VM instances.\n",
|
||||
"\n",
|
||||
"In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
|
||||
"\n",
|
||||
"- `model`: The `Model` resource.\n",
|
||||
"- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
|
||||
"- `machine_type`: The machine type for each VM instance.\n",
|
||||
"\n",
|
||||
"Do to the requirements to provision the resource, this may take upto a few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "4e93b034a72f"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = endpoint.deploy(\n",
|
||||
" model=model,\n",
|
||||
" deployed_model_display_name=\"gsod_\" + TIMESTAMP,\n",
|
||||
" machine_type=DEPLOY_COMPUTE,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(response)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "f75331c946d5"
|
||||
},
|
||||
"source": [
|
||||
"## Make a prediction\n",
|
||||
"\n",
|
||||
"Finally, you make an online prediction using the `endpoint()` method, with the following parameters:\n",
|
||||
"\n",
|
||||
"- `instances`: The instances to predict.\n",
|
||||
"\n",
|
||||
"The following is the for a prediction request:\n",
|
||||
"\n",
|
||||
" [ INSTANCE_1, INSTANCE_2, ... ]\n",
|
||||
" \n",
|
||||
" INSTANCE : { \"column_1\": value, \"column_2\": value, ... }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "72683bd9d777"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"INSTANCES = [{\"year\": \"2020\", \"month\": \"1\", \"day\": \"23\"}]\n",
|
||||
"\n",
|
||||
"prediction = endpoint.predict(instances=INSTANCES)\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "endpoint_delete:mbsdk"
|
||||
},
|
||||
"source": [
|
||||
"#### Delete the endpoint\n",
|
||||
"\n",
|
||||
"The method 'delete()' will delete the endpoint."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "endpoint_delete:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint.undeploy_all()\n",
|
||||
"endpoint.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "model_delete:mbsdk"
|
||||
},
|
||||
"source": [
|
||||
"#### Delete the model\n",
|
||||
"\n",
|
||||
"The method 'delete()' will delete the model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "model_delete:mbsdk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "cleanup"
|
||||
},
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "cleanup"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set this to true only if you'd like to delete your bucket\n",
|
||||
"delete_bucket = False\n",
|
||||
"\n",
|
||||
"if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
|
||||
" ! gsutil rm -r $BUCKET_URI"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "get_started_automl_tabular_exported_deploy.ipynb",
|
||||
"toc_visible": true
|
||||
},
|
||||
"environment": {
|
||||
"kernel": "python3",
|
||||
"name": "common-cpu.m95",
|
||||
"type": "gcloud",
|
||||
"uri": "gcr.io/deeplearning-platform-release/base-cpu:m95"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in New Issue
Block a user