mirror of
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fix, chore, refactor(egen): hardcodes scikit-learn version to 1.2, changes python version from 3.9 to 3.10, adds numpy==1.26.4 installation, adds code to undeploy model from endpoints (#3228)
* fix, chore, refactor(egen): hardcodes scikit-learn version to 1.2, changes python version from 3.9 to 3.10, adds numpy==1.26.4 installation, adds code to undeploy model from endpoints, rusage of future tense * wording changes * markdown wording changes as per PR comments
This commit is contained in:
sumanvita
@@ -32,22 +32,24 @@
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"# Vertex AI: Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata\n",
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"\n",
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"<table align=\"left\">\n",
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"\n",
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" <td>\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
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" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
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" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\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://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fml_metadata%2Fvertex-pipelines-ml-metadata.ipynb\">\n",
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" <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
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" </a>\n",
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" </td> \n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
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" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
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" </a>\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/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\">\n",
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" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
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" View on GitHub\n",
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" </a>\n",
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" </td>\n",
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" <td>\n",
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"<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb\" target='_blank'>\n",
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" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
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" Open in Vertex AI Workbench\n",
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" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><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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@@ -61,7 +63,7 @@
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"source": [
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"## Overview\n",
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"\n",
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"This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipelines runs, and analyze this metadata using the Vertex AI SDK for Pyrhon. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
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"This notebook demonstrates how to track metrics and artifacts across Vertex AI Pipeline runs, and analyze this metadata using the Vertex AI Python SDK. If you'd prefer to follow a step-by-step tutorial, check out the [codelab version](https://codelabs.developers.google.com/vertex-mlmd-pipelines#0) of this notebook.\n",
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"\n",
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"Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
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]
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@@ -86,7 +88,7 @@
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"* Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI.\n",
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"* The pipeline creates a dataset, trains a scikit-learn model, and deploys the model to an endpoint.\n",
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"* Write custom pipeline components that generate artifacts and metadata.\n",
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"* Compare Vertex AI Pipelines runs, both in the Google Cloud console and programmatically.\n",
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"* Compare Vertex AI Pipeline runs, both in the Google Cloud console and programmatically.\n",
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"* Trace the lineage for pipeline-generated artifacts.\n",
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"* Query your pipeline run metadata."
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]
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@@ -99,7 +101,7 @@
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"source": [
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"### Dataset\n",
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"\n",
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"In this notebook, you'll train a model using scikit-learn to classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
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"This notebook uses scikit-learn to train a model and classify bean types using the [Dry Beans Dataset](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset) from UCI Machine Learning. This is a tabular dataset that includes measurements and characteristics of seven different types of beans taken from images."
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]
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},
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{
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@@ -127,12 +129,19 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "i7EUnXsZhAGF"
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"id": "61RBz8LLbxCR"
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},
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"source": [
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"### Install additional packages\n",
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"\n",
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"Run the following commands to install the Vertex AI SDK for Python and packages used in this 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": "No17Cw5hgx12"
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},
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"source": [
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"### Install Vertex AI SDK for Python and other required packages\n"
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]
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},
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{
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@@ -150,137 +159,97 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "restart"
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"id": "R5Xep4W9lq-Z"
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},
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"source": [
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"### Colab only: Uncomment the following cell to restart the kernel"
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"### Restart runtime (Colab only)\n",
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"\n",
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"To use the newly installed packages, you must restart the runtime on Google Colab."
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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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"id": "D-ZBOjErv5mM"
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"id": "XRvKdaPDTznN"
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},
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"outputs": [],
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"source": [
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"# Automatically restart kernel after installs so that your environment can access the new packages\n",
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"# import IPython\n",
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"import sys\n",
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"\n",
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"# app = IPython.Application.instance()\n",
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"# app.kernel.do_shutdown(True)"
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"if \"google.colab\" in sys.modules:\n",
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"\n",
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" import IPython\n",
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"\n",
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" app = IPython.Application.instance()\n",
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" app.kernel.do_shutdown(True)"
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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": "yfEglUHQk9S3"
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"id": "SbmM4z7FOBpM"
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},
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"source": [
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"## Before you begin\n",
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"<div class=\"alert alert-block alert-warning\">\n",
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"<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
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"</div>\n"
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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": "dmWOrTJ3gx13"
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},
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"source": [
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"### Authenticate your notebook environment (Colab only)\n",
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"\n",
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"### Set your project ID\n",
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"\n",
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"**If you don't know your project ID**, try the following:\n",
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"* Run `gcloud config list`.\n",
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"* Run `gcloud projects list`.\n",
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"* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
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"Authenticate your environment on Google Colab.\n"
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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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"id": "set_project_id"
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"id": "NyKGtVQjgx13"
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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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"\n",
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"if \"google.colab\" in sys.modules:\n",
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"\n",
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" from google.colab import auth\n",
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"\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": "DF4l8DTdWgPY"
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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 using Vertex AI, you must have an existing Google Cloud project. Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
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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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"id": "Nqwi-5ufWp_B"
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},
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"outputs": [],
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"source": [
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"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n",
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"LOCATION = \"us-central1\" # @param {type:\"string\"}\n",
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"\n",
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"# Set the project id\n",
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"! gcloud config set project {PROJECT_ID}"
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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": "region"
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},
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"source": [
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"#### Region\n",
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"\n",
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"You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
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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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"id": "region"
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},
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"outputs": [],
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"source": [
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"REGION = \"us-central1\" # @param {type: \"string\"}"
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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": "gcp_authenticate"
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},
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"source": [
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"### Authenticate your Google Cloud account\n",
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"\n",
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"Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
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"\n",
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"**1. Vertex AI Workbench**\n",
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"* Do nothing as you are already authenticated.\n",
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"\n",
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"**2. Local JupyterLab instance, uncomment and run:**"
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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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"id": "ce6043da7b33"
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},
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"outputs": [],
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"source": [
|
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"# ! gcloud auth login"
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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": "0367eac06a10"
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},
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"source": [
|
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"**3. Colab, uncomment and run:**"
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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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"id": "21ad4dbb4a61"
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},
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"outputs": [],
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"source": [
|
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"# from google.colab import auth\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": "c13224697bfb"
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},
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"source": [
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"**4. Service account or other**\n",
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"* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
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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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@@ -344,7 +313,7 @@
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},
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"outputs": [],
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"source": [
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"! gsutil mb -l $REGION $BUCKET_URI"
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"! gsutil mb -l {LOCATION} -p {PROJECT_ID} {BUCKET_URI}"
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]
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},
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{
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@@ -491,7 +460,7 @@
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"id": "2937d462a96a"
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},
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"source": [
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"Initialize the Vertex AI SDK"
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"### Initialize Vertex AI SDK for Python"
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]
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},
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{
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@@ -502,7 +471,7 @@
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},
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"outputs": [],
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"source": [
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"aiplatform.init(project=PROJECT_ID, location=REGION)"
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"aiplatform.init(project=PROJECT_ID, location=LOCATION)"
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]
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},
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{
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@@ -523,7 +492,7 @@
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},
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"source": [
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"### Pipeline Run\n",
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"The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines. Each run generates artifacts, metrics, and associated metadata."
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"The term “run” refers to a single execution of your pipeline in Vertex AI Pipelines, during which artifacts, metrics, and associated metadata are generated."
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]
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},
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{
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@@ -534,7 +503,7 @@
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"source": [
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"### Artifact\n",
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"\n",
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"An artifact is a resource generated by your pipeline. Artifacts could datasets, models, endpoints, or custom resources defined in your pipeline."
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"An artifact is a resource generated by your pipeline. Artifacts can be datasets, models, endpoints, or custom resources defined in your pipeline."
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]
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},
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{
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@@ -545,7 +514,7 @@
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"source": [
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"### Metric\n",
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"\n",
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"A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric could be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
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"A metric is a way to measure the performance of your pipeline runs and artifacts. For example, a metric can be the accuracy of a classification model artifact created in your pipeline, or the size of the dataset used to train your model."
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]
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},
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{
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@@ -556,7 +525,7 @@
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"source": [
|
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"### Metadata\n",
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"\n",
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"Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, could include the URL of the model artifacts, its name, and the time it was created."
|
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"Metadata describes the artifacts and metrics generated by your pipeline runs. Metadata on a model, for example, includes the URL of the model artifacts, its name, and the time it was created."
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]
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},
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{
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@@ -567,7 +536,7 @@
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"source": [
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"## Creating a 3-step pipeline with custom components\n",
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"\n",
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"The focus of this lab is on understanding metadata from pipeline runs. In order to do that, you'll need a pipeline to run on Vertex AI Pipelines, which is where you’ll start. Here you’ll define a 3-step pipeline with the following custom components:\n",
|
||||
"The focus of this lab is on understanding metadata from pipeline runs. To do that, you need a pipeline to run on Vertex AI Pipelines, which is where you start. Here, you define a 3-step pipeline with the following custom components:\n",
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"\n",
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"* `get_dataframe`: Retrieve data from a BigQuery table and convert it into a pandas DataFrame.\n",
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||||
"* `train_sklearn_model`: Use the pandas DataFrame to train and export a scikit-learn model, along with some metrics.\n",
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@@ -604,14 +573,16 @@
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"outputs": [],
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||||
"source": [
|
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"@component(\n",
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||||
" packages_to_install=[\"google-cloud-bigquery\", \"pandas\", \"pyarrow\"],\n",
|
||||
" base_image=\"python:3.9\",\n",
|
||||
" packages_to_install=[\"google-cloud-bigquery[pandas]\", \"pyarrow\"],\n",
|
||||
" base_image=\"python:3.10\",\n",
|
||||
" output_component_file=\"create_dataset.yaml\",\n",
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")\n",
|
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"def get_dataframe(bq_table: str, output_data_path: OutputPath(\"Dataset\")):\n",
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"def get_dataframe(\n",
|
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" project_id: str, bq_table: str, output_data_path: OutputPath(\"Dataset\")\n",
|
||||
"):\n",
|
||||
" from google.cloud import bigquery\n",
|
||||
"\n",
|
||||
" bqclient = bigquery.Client(project=PROJECT_ID)\n",
|
||||
" bqclient = bigquery.Client(project=project_id)\n",
|
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" table = bigquery.TableReference.from_string(bq_table)\n",
|
||||
" rows = bqclient.list_rows(table)\n",
|
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" dataframe = rows.to_dataframe(\n",
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@@ -644,8 +615,8 @@
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"outputs": [],
|
||||
"source": [
|
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"@component(\n",
|
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" packages_to_install=[\"scikit-learn\", \"pandas\", \"joblib\"],\n",
|
||||
" base_image=\"python:3.9\",\n",
|
||||
" packages_to_install=[\"scikit-learn==1.2\", \"pandas\", \"joblib\", \"numpy==1.26.4\"],\n",
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" base_image=\"python:3.10\",\n",
|
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" output_component_file=\"beans_model_component.yaml\",\n",
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")\n",
|
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"def sklearn_train(\n",
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@@ -691,7 +662,7 @@
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"source": [
|
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"@component(\n",
|
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" packages_to_install=[\"google-cloud-aiplatform\"],\n",
|
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" base_image=\"python:3.9\",\n",
|
||||
" base_image=\"python:3.10\",\n",
|
||||
" output_component_file=\"beans_deploy_component.yaml\",\n",
|
||||
")\n",
|
||||
"def deploy_model(\n",
|
||||
@@ -708,7 +679,7 @@
|
||||
" deployed_model = aiplatform.Model.upload(\n",
|
||||
" display_name=\"beans-model-pipeline\",\n",
|
||||
" artifact_uri=model.uri.replace(\"model\", \"\"),\n",
|
||||
" serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest\",\n",
|
||||
" serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-2:latest\",\n",
|
||||
" )\n",
|
||||
" endpoint = deployed_model.deploy(machine_type=\"n1-standard-4\")\n",
|
||||
"\n",
|
||||
@@ -741,12 +712,12 @@
|
||||
" name=\"mlmd-pipeline\",\n",
|
||||
")\n",
|
||||
"def pipeline(\n",
|
||||
" bq_table: str = \"\",\n",
|
||||
" output_data_path: str = \"data.csv\",\n",
|
||||
" project: str = PROJECT_ID,\n",
|
||||
" region: str = REGION,\n",
|
||||
" bq_table: str,\n",
|
||||
" output_data_path: str,\n",
|
||||
" project: str,\n",
|
||||
" region: str,\n",
|
||||
"):\n",
|
||||
" dataset_task = get_dataframe(bq_table)\n",
|
||||
" dataset_task = get_dataframe(project, bq_table)\n",
|
||||
"\n",
|
||||
" model_task = sklearn_train(dataset_task.output)\n",
|
||||
"\n",
|
||||
@@ -759,7 +730,7 @@
|
||||
"id": "910541af051c"
|
||||
},
|
||||
"source": [
|
||||
"The following generates a JSON file that you'll use to run the pipeline:"
|
||||
"The following generates a JSON file that is then used to run the pipeline:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -779,9 +750,22 @@
|
||||
"id": "u-iTnzt3B6Z_"
|
||||
},
|
||||
"source": [
|
||||
"### Start two pipeline runs\n",
|
||||
"### Initiate pipeline runs\n",
|
||||
"\n",
|
||||
"Next you'll kick ofg **two** runs of our pipeline. First, define a timestamp to use for our pipeline job IDs:"
|
||||
"First, define a timestamp to use as your pipeline job IDs.\n",
|
||||
"\n",
|
||||
"Then for each run, create an instance of `PipelineJob` from the `pipeline_jobs` module.\n",
|
||||
" For each instance provide the following details:\n",
|
||||
"\n",
|
||||
"* `display_name` : Human-readable name for the pipeline job.\n",
|
||||
"* `template_path` : This specifies the path to the pipeline template file in JSON format, which contains the pipeline's configuration and structure created in the previous steps.\n",
|
||||
"* `job_id` : This sets a unique identifier for the job.\n",
|
||||
"* `parameter_values` : This dictionary contains key-value pairs for the parameters required by the pipeline which are metioned during pipeline definition.\n",
|
||||
" * `bq_table` : Specifies the BigQuery table to use.\n",
|
||||
" * `output_data_path` : Defines the path for the output data file.\n",
|
||||
" * `project` : Specifies the Google Cloud project ID.\n",
|
||||
" * `region` : Defines the region where the pipeline will run.\n",
|
||||
"* enable_caching : When set to `True`, caching is enabled for the pipeline run. This lets the system reuse previous results, if the same job has been executed before with identical parameters, thereby saving time and resources"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -803,7 +787,7 @@
|
||||
"id": "3d380ed72490"
|
||||
},
|
||||
"source": [
|
||||
"The pipeline takes one parameter when you run it: the `bq_table` we want to use for training data you’ll use for training data. This pipeline run uses a smaller version of the beans dataset:"
|
||||
"Create a pipeline run using the smaller version beans dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -818,7 +802,12 @@
|
||||
" display_name=\"mlmd-pipeline\",\n",
|
||||
" template_path=\"mlmd_pipeline.json\",\n",
|
||||
" job_id=\"mlmd-pipeline-small-{}\".format(TIMESTAMP),\n",
|
||||
" parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\"},\n",
|
||||
" parameter_values={\n",
|
||||
" \"bq_table\": \"sara-vertex-demos.beans_demo.small_dataset\",\n",
|
||||
" \"output_data_path\": \"data.csv\",\n",
|
||||
" \"project\": PROJECT_ID,\n",
|
||||
" \"region\": REGION,\n",
|
||||
" },\n",
|
||||
" enable_caching=True,\n",
|
||||
")"
|
||||
]
|
||||
@@ -844,7 +833,12 @@
|
||||
" display_name=\"mlmd-pipeline\",\n",
|
||||
" template_path=\"mlmd_pipeline.json\",\n",
|
||||
" job_id=\"mlmd-pipeline-large-{}\".format(TIMESTAMP),\n",
|
||||
" parameter_values={\"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\"},\n",
|
||||
" parameter_values={\n",
|
||||
" \"bq_table\": \"sara-vertex-demos.beans_demo.large_dataset\",\n",
|
||||
" \"output_data_path\": \"data.csv\",\n",
|
||||
" \"project\": PROJECT_ID,\n",
|
||||
" \"region\": REGION,\n",
|
||||
" },\n",
|
||||
" enable_caching=True,\n",
|
||||
")"
|
||||
]
|
||||
@@ -895,7 +889,7 @@
|
||||
"id": "cc15017be48e"
|
||||
},
|
||||
"source": [
|
||||
"After running this cell, you'll see a link to view each pipeline in the Google Cloud console. Open that link to see more details on your pipeline.\n",
|
||||
"After running this cell, there is a link to view each pipeline in the Google Cloud console. Open that link to get more details about your pipeline.\n",
|
||||
"\n",
|
||||
"**These pipeline runs will take 10-15 minutes to complete.**"
|
||||
]
|
||||
@@ -915,9 +909,9 @@
|
||||
"id": "A1PqKxlpOZa2"
|
||||
},
|
||||
"source": [
|
||||
"Now that you have two pipeline completed pipeline runs, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
|
||||
"Once both the pipelines run successfully, you're ready to take a closer look at pipeline metrics using the Vertex AI SDK for Python.\n",
|
||||
"\n",
|
||||
"**For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, see [this codelab](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5).**"
|
||||
"For guidance on inspecting pipeline artifacts and metadata in the Google Cloud console, check out this codelab: [Understanding pipeline artifacts and lineage](https://codelabs.developers.google.com/vertex-mlmd-pipelines#5)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -926,7 +920,8 @@
|
||||
"id": "jbRf1WoH_vbY"
|
||||
},
|
||||
"source": [
|
||||
"You can use the `aiplatform.get_pipeline_df()` method to access run metadata. Here, you'll get metadata for the last two runs of the same pipeline and load it into a Pandas DataFrame. The `mlmd-pipeline` parameter here refers to the name you gave your pipeline in the pipeline definition:"
|
||||
"Use `aiplatform.get_pipeline_df()` method to retrieve the metadata for the last two runs of the pipeline. Then, load it into a Pandas DataFrame. \n",
|
||||
"The `pipeline` parameter specifies the name of your pipeline as defined in the pipeline configuration, which in this case is *mlmd-pipeline*."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -947,7 +942,7 @@
|
||||
"id": "d23e2cb66265"
|
||||
},
|
||||
"source": [
|
||||
"You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model's accuracy and the amount of data used for training. Run the following to generate a graph:"
|
||||
"You’ve only executed the pipeline twice here, but you can imagine how many metrics you'd have with more executions. Next, create a custom visualization with matplotlib to see the relationship between the model accuracy and the amount of data used for training. Run the following to generate a graph:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -979,7 +974,7 @@
|
||||
"id": "4431b5d062f3"
|
||||
},
|
||||
"source": [
|
||||
"In addition to getting a DataFrame of all pipeline metrics, you may want to programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
|
||||
"In addition to creating a DataFrame of all pipeline metrics, you can programmatically query artifacts created in your ML system. From there you can create a custom dashboard or let others in your organizaiton get details on specific artifacts."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -990,7 +985,7 @@
|
||||
"source": [
|
||||
"### Getting all Model artifacts\n",
|
||||
"\n",
|
||||
"To query artifacts in this way, you'll create a `MetadataServiceClient`:"
|
||||
"To query artifacts in this way, create a `MetadataServiceClient`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1056,7 +1051,7 @@
|
||||
"id": "F19_5lw0MqXv"
|
||||
},
|
||||
"source": [
|
||||
"Next, get all artifacts created after August 10, 2021 with a `LIVE` state. After you run this request, display the results in a pandas DataFrame. First, execute the request:"
|
||||
"Next, get all artifacts created after August 10, 2021 that are in `LIVE` state. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1122,7 +1117,9 @@
|
||||
"\n",
|
||||
"* The pipeline runs you executed deployed endpoints in Vertex AI. Navigate to the [Google Cloud console](https://console.cloud.google.com/vertex-ai/endpoints) to delete those endpoints.\n",
|
||||
"\n",
|
||||
"* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created."
|
||||
"* Delete the [Cloud Storage bucket](https://console.cloud.google.com/storage/browser/) you created.\n",
|
||||
"\n",
|
||||
"Alternatively, you can execute the below cell to clean up the resources used in this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1133,16 +1130,36 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# delete pipelines\n",
|
||||
"try:\n",
|
||||
" run1.delete()\n",
|
||||
" run2.delete()\n",
|
||||
"except Exception as e:\n",
|
||||
" print(e)\n",
|
||||
"\n",
|
||||
"delete_bucket = False\n",
|
||||
"if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
|
||||
"# undeploy model from endpoints\n",
|
||||
"endpoints = aiplatform.Endpoint.list(\n",
|
||||
" filter='display_name=\"beans-model-pipeline_endpoint\"'\n",
|
||||
")\n",
|
||||
"for endpoint in endpoints:\n",
|
||||
" deployed_models = endpoint.list_models()\n",
|
||||
" for deployed_model in deployed_models:\n",
|
||||
" endpoint.undeploy(deployed_model_id=deployed_model.id)\n",
|
||||
" # delete endpoint\n",
|
||||
" endpoint.delete()\n",
|
||||
"\n",
|
||||
"# delete model\n",
|
||||
"model_ids = aiplatform.Model.list(filter='display_name=\"beans-model-pipeline\"')\n",
|
||||
"for model_id in model_ids:\n",
|
||||
" model = aiplatform.Model(model_name=model_id.resource_name)\n",
|
||||
" model.delete()\n",
|
||||
"\n",
|
||||
"# delete locally generated files\n",
|
||||
"! rm -rf beans_deploy_component.yaml beans_model_component.yaml create_dataset.yaml mlmd_pipeline.json\n",
|
||||
"\n",
|
||||
"# delete cloud storage bucket\n",
|
||||
"delete_bucket = False # set True for deletion\n",
|
||||
"if delete_bucket:\n",
|
||||
" ! gsutil rm -rf {BUCKET_URI}"
|
||||
]
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user