mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
Migrate Controlnet notebook to use Model Garden SDK
PiperOrigin-RevId: 797349160
This commit is contained in:
Vertex MG Team
committed by
Copybara-Service
Copybara-Service
parent
f8c93de1c4
commit
d39bed012a
@@ -4,11 +4,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "7d9bbf86da5e"
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"cellView": "form",
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"id": "ebvioouiVV_9"
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},
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"outputs": [],
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"source": [
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"# Copyright 2024 Google LLC\n",
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"# Copyright 2025 Google LLC\n",
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"#\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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@@ -31,19 +32,18 @@
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"source": [
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"# Vertex AI Model Garden - ControlNet\n",
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"\n",
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"<table align=\"left\">\n",
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" <td>\n",
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"<table><tbody><tr>\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_controlnet.ipynb\">\n",
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" <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
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" </a>\n",
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" </td>\n",
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" <td>\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_controlnet.ipynb\">\n",
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" <img src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" alt=\"GitHub logo\"><br>\n",
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" View on GitHub\n",
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" <img alt=\"GitHub logo\" src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" width=\"32px\"><br> View on GitHub\n",
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" </a>\n",
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" </td>\n",
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"</table>"
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"</tr></tbody></table>"
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]
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},
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{
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@@ -54,31 +54,43 @@
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"source": [
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"## Overview\n",
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"\n",
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"This notebook demonstrates deploying the [ControlNet](https://huggingface.co/lllyasviel/ControlNet) model on Vertex AI for online prediction.\n",
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"This notebook demonstrates how to deploy a **Control-Net** open model on Google Cloud Vertex AI.\n",
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"\n",
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"### Objective\n",
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"### Objectives\n",
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"\n",
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"- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
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"- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
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"- Run online predictions for text-guided-image-to-image.\n",
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"- Deploy Control-Net using containerized backends like [vLLM](https://github.com/vllm-project/vllm) on GPU.\n",
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"- Use the deployed model to serve chat completion requests for both text and multimodal inputs.\n",
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"\n",
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"### File a Bug\n",
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"\n",
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"If you encounter issues with this notebook, report them on [GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/issues/new).\n",
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"\n",
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"### Costs\n",
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"\n",
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"This tutorial uses billable components of Google Cloud:\n",
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"\n",
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"* Vertex AI\n",
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"* Cloud Storage\n",
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"- Vertex AI\n",
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"- Cloud Storage\n",
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"\n",
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"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."
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"Refer to the [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing) pages for more information. Use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to estimate your projected costs."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "264c07757582"
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"id": "jeYw-Czg-DFy"
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},
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"source": [
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"## Run the notebook"
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"## Get Started"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KgyhGvEzBDkj"
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},
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"source": [
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"### Install Vertex AI SDK and other required packages"
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]
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},
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{
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@@ -86,147 +98,52 @@
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "2707b02ef5df"
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"id": "iCacdLqG-IsH"
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},
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"outputs": [],
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"source": [
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"# @title Setup Google Cloud project\n",
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"%pip install --upgrade --force-reinstall --quiet 'google-cloud-aiplatform>=1.106.0' 'openai' 'google-auth' 'requests'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HUKCrpBy-3yf"
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},
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"source": [
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"### Authenticate the Notebook Environment (Colab only)\n",
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"\n",
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"# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
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"\n",
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"# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
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"\n",
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"import base64\n",
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"import os\n",
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"If you're running this notebook in Google Colab, run the following cell to authenticate."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "JXwCT1kn-3Gu"
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"import uuid\n",
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"from datetime import datetime\n",
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"from io import BytesIO\n",
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"\n",
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"import cv2\n",
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"import numpy as np\n",
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"import requests\n",
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"from google.cloud import aiplatform\n",
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"from PIL import Image\n",
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"\n",
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"# Get the default cloud project id.\n",
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"PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
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"\n",
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"# Get the default region for launching jobs.\n",
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"REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
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"\n",
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"# Enable the Vertex AI API and Compute Engine API, if not already.\n",
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"! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
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"\n",
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"# Cloud Storage bucket for storing the experiment artifacts.\n",
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"# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
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"# prefer using your own GCS bucket, please change the value yourself below.\n",
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"now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
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"BUCKET_URI = \"gs://\" # @param {type: \"string\"}\n",
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"BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
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"assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
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"\n",
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"# Create a unique GCS bucket for this notebook, if not specified by the user.\n",
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"if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
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" BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
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" BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
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" ! gsutil mb -l {REGION} {BUCKET_URI}\n",
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"else:\n",
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" shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
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" bucket_region = shell_output[0].strip().lower()\n",
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" if bucket_region != REGION:\n",
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" raise ValueError(\n",
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" \"Bucket region %s is different from notebook region %s.\"\n",
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" % (bucket_region, REGION)\n",
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" )\n",
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"\n",
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"print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
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"\n",
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"# Set up the default SERVICE_ACCOUNT.\n",
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"SERVICE_ACCOUNT = None\n",
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"shell_output = ! gcloud projects describe $PROJECT_ID\n",
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"project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
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"SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
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"\n",
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"print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
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"\n",
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"# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
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"! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
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"\n",
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"if \"google.colab\" in sys.modules:\n",
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" from google.colab import auth\n",
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"\n",
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" auth.authenticate_user(project_id=PROJECT_ID)\n",
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" auth.authenticate_user()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AcW2nwB8-7yC"
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},
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"source": [
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"### Set Google Cloud Project Information\n",
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"\n",
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"To get started with Vertex AI, ensure you have an existing Google Cloud project and that the [Vertex AI API is enabled](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
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"\n",
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"# The pre-built serving docker image. It contains serving scripts and models.\n",
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"SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-diffusers-serve-opt:20240605_1400_RC00\"\n",
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"\n",
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"\n",
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"# Define common functions.\n",
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"def download_image(url):\n",
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" response = requests.get(url)\n",
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" return Image.open(BytesIO(response.content))\n",
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"\n",
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"\n",
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"def image_to_base64(image, format=\"JPEG\"):\n",
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" buffer = BytesIO()\n",
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" image.save(buffer, format=format)\n",
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" image_str = base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n",
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" return image_str\n",
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"\n",
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"\n",
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"def base64_to_image(image_str):\n",
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" image = Image.open(BytesIO(base64.b64decode(image_str)))\n",
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" return image\n",
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"\n",
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"\n",
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"def image_grid(imgs, rows=2, cols=2):\n",
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" w, h = imgs[0].size\n",
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" grid = Image.new(\"RGB\", size=(cols * w, rows * h), color=(255, 255, 255))\n",
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" for i, img in enumerate(imgs):\n",
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" grid.paste(img, box=(i % cols * w + 10 * i, i // cols * h))\n",
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" return grid\n",
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"\n",
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"\n",
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"def canny(image):\n",
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" image = np.array(image)\n",
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" image = cv2.Canny(image, 100, 200)\n",
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" image = image[:, :, None]\n",
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" image = np.concatenate([image, image, image], axis=2)\n",
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" image = Image.fromarray(image)\n",
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" return image\n",
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"\n",
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"\n",
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"def deploy_model(model_id, task):\n",
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" model_name = \"controlnet\"\n",
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" endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
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" serving_env = {\n",
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" \"MODEL_ID\": model_id,\n",
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" \"TASK\": task,\n",
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" \"DEPLOY_SOURCE\": \"notebook\",\n",
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" }\n",
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" model = aiplatform.Model.upload(\n",
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" display_name=model_name,\n",
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" serving_container_image_uri=SERVE_DOCKER_URI,\n",
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" serving_container_ports=[7080],\n",
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" serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
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" serving_container_health_route=\"/ping\",\n",
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" serving_container_environment_variables=serving_env,\n",
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" model_garden_source_model_name=\"publishers/lllyasviel/models/control-net\"\n",
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" )\n",
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" model.deploy(\n",
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" endpoint=endpoint,\n",
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" machine_type=\"g2-standard-8\",\n",
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" accelerator_type=\"NVIDIA_L4\",\n",
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" accelerator_count=1,\n",
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" deploy_request_timeout=1800,\n",
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" service_account=SERVICE_ACCOUNT,\n",
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" system_labels={\n",
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" \"NOTEBOOK_NAME\": \"model_garden_pytorch_controlnet.ipynb\"\n",
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" },\n",
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" )\n",
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" return model, endpoint"
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"See the guide on [setting up your project and development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment). Also confirm that [billing is enabled](https://cloud.google.com/billing/docs/how-to/modify-project).\n"
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]
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},
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{
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@@ -234,21 +151,249 @@
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "a9bbe0f7c237"
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"id": "eIVLp0oE--k-"
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},
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"outputs": [],
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"source": [
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"# @title Upload and deploy model\n",
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"# Use the environment variable if the user doesn't provide Project ID.\n",
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"import os\n",
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"\n",
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"# @markdown This step deploys the pre-trained [lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny) model for the text-guided image-to-image task.\n",
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"import vertexai\n",
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"\n",
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"# @markdown The model deployment step will take ~15 minutes to complete.\n",
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"PROJECT_ID = \"[your-project-id]\" # @param {type: \"string\", placeholder: \"[your-project-id]\", isTemplate: true}\n",
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"\n",
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"model, endpoint = deploy_model(\n",
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" model_id=\"lllyasviel/sd-controlnet-canny\", task=\"controlnet\"\n",
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"if not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n",
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" PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n",
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"\n",
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"REGION = os.environ.get(\"GOOGLE_CLOUD_REGION\", \"us-central1\")\n",
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"\n",
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"vertexai.init(project=PROJECT_ID, location=REGION)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Q0CXrvcZH_aw"
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},
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"source": [
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"### Import libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "3G2UXB82ICs6"
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},
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"outputs": [],
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"source": [
|
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"from vertexai import model_garden"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "upYRiGtP_-iN"
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},
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"source": [
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"## Deploy model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "H2WC_0hXDVXc"
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},
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"source": [
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"### Choose model variant"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "u41zbNa2EoFq"
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},
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"source": [
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"You can proceed with the default model variant or select a different one."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "-fgC4NLSDkF7"
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},
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"outputs": [],
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"source": [
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"model_version = \"sd-controlnet-canny\" # @param [\"sd-controlnet-canny\"] {isTemplate:true}\n",
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"MODEL_NAME = f\"lllyasviel/control-net@{model_version}\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "VRnUgU8LF3_i"
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},
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"source": [
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"To see all deployable model variants available in Model Garden, use:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "-QLd-wshF6sB"
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},
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"outputs": [],
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"source": [
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"all_model_versions = model_garden.list_deployable_models(\n",
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" model_filter=\"control-net\", list_hf_models=False\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "N0UeFHa2GO63"
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},
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"source": [
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||||
"Once you've selected a model variant, initialize it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "GZiV3trBBcA3"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = model_garden.OpenModel(MODEL_NAME)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "-0cL378wFlvf"
|
||||
},
|
||||
"source": [
|
||||
"### Check the Deployment Configuration\n",
|
||||
"\n",
|
||||
"Use the `list_deploy_options()` method to view the verified deployment configurations for your selected model. This helps ensure you have sufficient resources (e.g., GPU quota) available to deploy it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zm73g7vFFm9N"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"deploy_options = model.list_deploy_options(concise=True)\n",
|
||||
"print(deploy_options)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "WjV499VsGwrD"
|
||||
},
|
||||
"source": [
|
||||
"### Deploy the Model\n",
|
||||
"\n",
|
||||
"Now that you’ve reviewed the deployment options, use the `deploy()` method to serve the selected open model to a Vertex AI endpoint. Deployment time may vary depending on the model size and infrastructure requirements.\n",
|
||||
"\n",
|
||||
"> **Note**: If the model requires accepting a license agreement (EULA), set the `accept_eula=True` flag in the deploy call. Set `use_dedicated_endpoint` to False if you don't want to use [dedicated endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#create-dedicated-endpoint)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "wX1itVTvXdEP"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"use_dedicated_endpoint = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "MRmPFEPoGzsB"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint = model.deploy(\n",
|
||||
" accept_eula=True,\n",
|
||||
" use_dedicated_endpoint=use_dedicated_endpoint,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "PHBtn8DQp-ID"
|
||||
},
|
||||
"source": [
|
||||
"Alternatively, you can select one of the verified deployment configurations listed above."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ADsJG8JYqI6c"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint = model.deploy(\n",
|
||||
" accept_eula=True,\n",
|
||||
" use_dedicated_endpoint=use_dedicated_endpoint,\n",
|
||||
" serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-diffusers-serve-opt:20240605_1400_RC00\",\n",
|
||||
" machine_type=\"g2-standard-8\",\n",
|
||||
" accelerator_type=\"NVIDIA_L4\",\n",
|
||||
" accelerator_count=1,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "kqSUK2CwsImi"
|
||||
},
|
||||
"source": [
|
||||
"To further customize your deployment, you can configure:\n",
|
||||
"\n",
|
||||
"- **Compute Resources**: Machine type, replica count (min/max), accelerator type and quantity.\n",
|
||||
"- **Infrastructure**: Use Spot VMs, reservation affinity, or dedicated endpoints.\n",
|
||||
"- **Serving Container**: Customize container image, ports, health checks, and environment variables.\n",
|
||||
"\n",
|
||||
"See the [Model Garden SDK README](https://github.com/googleapis/python-aiplatform/blob/main/vertexai/model_garden/README.md) for advanced configuration options."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "Pt1O-tITu5xL"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"! pip install --upgrade 'opencv-python'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -266,25 +411,49 @@
|
||||
"\n",
|
||||
"# @markdown You may adjust the parameters below to achieve best image quality.\n",
|
||||
"\n",
|
||||
"import importlib\n",
|
||||
"\n",
|
||||
"import cv2\n",
|
||||
"import numpy as np\n",
|
||||
"from PIL import Image\n",
|
||||
"\n",
|
||||
"# Import the necessary packages.\n",
|
||||
"! rm -rf vertex-ai-samples && git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
|
||||
"! cd vertex-ai-samples\n",
|
||||
"\n",
|
||||
"common_util = importlib.import_module(\n",
|
||||
" \"vertex-ai-samples.notebooks.community.model_garden.docker_source_codes.notebook_util.common_util\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def canny(image):\n",
|
||||
" image = np.array(image)\n",
|
||||
" image = cv2.Canny(image, 100, 200)\n",
|
||||
" image = image[:, :, None]\n",
|
||||
" image = np.concatenate([image, image, image], axis=2)\n",
|
||||
" image = Image.fromarray(image)\n",
|
||||
" return image\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"prompt = \"bird\" # @param {type: \"string\"}\n",
|
||||
"image = \"https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png\" # @param {type: \"string\"}\n",
|
||||
"num_inference_steps = 25 # @param {type:\"number\"}\n",
|
||||
"\n",
|
||||
"init_image = download_image(image)\n",
|
||||
"init_image = common_util.download_image(image)\n",
|
||||
"canny_image = canny(init_image)\n",
|
||||
"\n",
|
||||
"instances = [\n",
|
||||
" {\n",
|
||||
" \"prompt\": prompt,\n",
|
||||
" \"image\": image_to_base64(canny_image),\n",
|
||||
" \"image\": common_util.image_to_base64(canny_image),\n",
|
||||
" \"num_inference_steps\": num_inference_steps,\n",
|
||||
" },\n",
|
||||
"]\n",
|
||||
"response = endpoint.predict(instances=instances)\n",
|
||||
"images = [base64_to_image(image) for image in response.predictions]\n",
|
||||
"images = [common_util.base64_to_image(image) for image in response.predictions]\n",
|
||||
"new_image = images[0]\n",
|
||||
"\n",
|
||||
"image_grid([init_image, canny_image, new_image], rows=1, cols=3)"
|
||||
"common_util.image_grid([init_image, canny_image, new_image], rows=1, cols=3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -298,19 +467,10 @@
|
||||
"source": [
|
||||
"# @title Clean up resources\n",
|
||||
"\n",
|
||||
"# @markdown Delete the experiment models and endpoints to recycle the resources\n",
|
||||
"# @markdown and avoid unnecessary continouous charges that may incur.\n",
|
||||
"# @markdown Delete the endpoint.\n",
|
||||
"\n",
|
||||
"# Undeploy model and delete endpoint.\n",
|
||||
"endpoint.delete(force=True)\n",
|
||||
"\n",
|
||||
"# Delete models.\n",
|
||||
"model.delete()\n",
|
||||
"\n",
|
||||
"# Delete bucket.\n",
|
||||
"delete_bucket = False # @param {type:\"boolean\"}\n",
|
||||
"if delete_bucket:\n",
|
||||
" ! gsutil -m rm -r $BUCKET_NAME"
|
||||
"if endpoint:\n",
|
||||
" endpoint.delete(force=True)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in New Issue
Block a user