mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
feat: add offline feature serving notebook (#3184)
This commit is contained in:
Manu Kumar
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"cells": [
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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": "ur8xi4C7S06n"
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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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"#\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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||||||
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# https://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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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": "JAPoU8Sm5E6e"
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},
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"source": [
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"## Fetch historical feature values\n",
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"\n",
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"<table align=\"left\">\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/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\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 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%2Ffeature_store%2Foffline_feature_serving_from_bigquery_with_feature_registry.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/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.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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||||||
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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/feature_store/offline_feature_serving_from_bigquery_with_feature_registry.ipynb\">\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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]
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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": "tvgnzT1CKxrO"
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},
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"source": [
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"## Overview\n",
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"\n",
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"In this tutorial, you will learn how to use the Vertex AI SDK for Python to retrieve historical values from the feature data source in BigQuery.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services and resources:\n",
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"\n",
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"* Vertex AI Feature Store\n",
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"* BigQuery\n",
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"\n",
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"The steps performed include the following:\n",
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"\n",
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"* Setup BigQuery data\n",
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"* Setup Feature Registry\n",
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"* Fetch historical feature values from feature data source in BigQuery\n",
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"* Clean up"
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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": "19cf444ebb99"
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},
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"source": [
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"### Objective"
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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": "61RBz8LLbxCR"
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},
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"source": [
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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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "tFy3H3aPgx12"
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},
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"outputs": [],
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"source": [
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"! pip3 install --upgrade --quiet google-cloud-aiplatform bigframes"
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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": "R5Xep4W9lq-Z"
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},
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"source": [
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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": "XRvKdaPDTznN"
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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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" 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": "SbmM4z7FOBpM"
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},
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"source": [
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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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"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": "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 and initialize Vertex AI SDK for Python\n",
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"\n",
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"To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). 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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"\n",
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"import vertexai\n",
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"\n",
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"vertexai.init(project=PROJECT_ID, location=LOCATION)"
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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": "33067053f38b"
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},
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"source": [
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"### Imports and IDs\n",
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"\n",
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"Import the packages required to use the`fetch_historical_feature_values()`\n",
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"function in the Vertex AI SDK for Python."
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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": "c8abe818393b"
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},
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"outputs": [],
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"source": [
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"import bigframes\n",
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"import bigframes.pandas\n",
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"import pandas as pd\n",
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"from google.cloud import bigquery\n",
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"from vertexai.resources.preview.feature_store import (Feature, FeatureGroup,\n",
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" offline_store)\n",
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"from vertexai.resources.preview.feature_store import utils as fs_utils"
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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": "4d0295f5d524"
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},
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"source": [
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"The following variables set BigQuery and Feature Group resources that will be\n",
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"used or created. If you'd like to use your own data source (CSV), please adjust\n",
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"`DATA_SOURCE`."
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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": "ac036ecfbc32"
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},
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"outputs": [],
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"source": [
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"BQ_DATASET_ID = \"fhfv_dataset_unique\" # @param {type:\"string\"}\n",
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"BQ_TABLE_ID = \"fhfv_table_unique\" # @param {type:\"string\"}\n",
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"BQ_TABLE_URI = f\"{PROJECT_ID}.{BQ_DATASET_ID}.{BQ_TABLE_ID}\"\n",
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"\n",
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"FEATURE_GROUP_ID = \"fhfv_fg_unique\" # @param {type:\"string\"}\n",
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"\n",
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"DATA_SOURCE = \"gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv\""
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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": "cd580a0679ce"
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},
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"source": [
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"## Create BigQuery table containing feature data"
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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": "1e2c688b844b"
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},
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"source": [
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"First we'll use BigQuery DataFrames to load in our CSV data source. Then we'll\n",
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"rename the `timestamp` column to `feature_timestamp` to support usage as a\n",
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"BigQuery source in Feature Registry."
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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": "1ff4481243a8"
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},
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"outputs": [],
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"source": [
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"session = bigframes.connect(\n",
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" bigframes.BigQueryOptions(\n",
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" project=PROJECT_ID,\n",
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" location=LOCATION,\n",
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" )\n",
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")\n",
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"df = session.read_csv(DATA_SOURCE)\n",
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"df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"], utc=True)\n",
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"df = df.rename(columns={\"timestamp\": \"feature_timestamp\"})"
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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": "de5008f71567"
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},
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"source": [
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"Let's preview the data we'll write to the table."
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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": "38b448c47657"
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},
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"outputs": [],
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"source": [
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"df.head()"
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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": "967ec2a0193f"
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},
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"source": [
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"And finally we'll write the DataFrame to the target BigQuery table."
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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": "4c11b88ab55d"
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},
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"outputs": [],
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"source": [
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"df.to_gbq(BQ_TABLE_URI, if_exists=\"replace\")"
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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": "818a36b9da86"
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},
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"source": [
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"## Create feature registry resources"
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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": "2fd96d0d8628"
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},
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"source": [
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"Create a feature group backed by the BigQuery table created above."
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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": "1f915ddd4669"
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},
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"outputs": [],
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"source": [
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"fg: FeatureGroup = FeatureGroup.create(\n",
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" f\"{FEATURE_GROUP_ID}\",\n",
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" fs_utils.FeatureGroupBigQuerySource(\n",
|
||||||
|
" uri=f\"bq://{BQ_TABLE_URI}\", entity_id_columns=[\"users\"]\n",
|
||||||
|
" ),\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "ebb9179572f7"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"Create the `movies` feature which corresponds to the `movies` column in the\n",
|
||||||
|
"recently created BigQuery table."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "0dba1c02883c"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"movies_feature: Feature = fg.create_feature(\"movies\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "c71964219962"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"## Fetch historical feature values"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "8eb9b5257006"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"### Fetch historical feature values for an entity"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "31edf36830b7"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"The following will fetch historical feature values for the same entity (`alice`)\n",
|
||||||
|
"at two different timestamps. We expect the values of the `movies` feature at\n",
|
||||||
|
"each of those timestamps."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "8fb9774db748"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"entity_df = pd.DataFrame(\n",
|
||||||
|
" data={\n",
|
||||||
|
" \"users\": [\"alice\", \"alice\"],\n",
|
||||||
|
" \"timestamp\": [\n",
|
||||||
|
" pd.Timestamp(\"2021-09-14T09:36\"),\n",
|
||||||
|
" pd.Timestamp(\"2023-12-12T13:13\"),\n",
|
||||||
|
" ],\n",
|
||||||
|
" },\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"offline_store.fetch_historical_feature_values(\n",
|
||||||
|
" entity_df=entity_df,\n",
|
||||||
|
" features=[movies_feature],\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "f9ecae3005df"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"### Fetch with multiple entities"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "f463b68f2f4f"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"The following will fetch historical feature values for two different entities\n",
|
||||||
|
"at different timestamps. We expect the values of the `movies` feature for each\n",
|
||||||
|
"entity at it's corresponding timestamp."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "80ab288afd22"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"entity_df = pd.DataFrame(\n",
|
||||||
|
" data={\n",
|
||||||
|
" \"users\": [\"alice\", \"bob\"],\n",
|
||||||
|
" \"timestamp\": [\n",
|
||||||
|
" pd.Timestamp(\"2021-09-14T09:36\"),\n",
|
||||||
|
" pd.Timestamp(\"2023-12-12T13:13\"),\n",
|
||||||
|
" ],\n",
|
||||||
|
" },\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"offline_store.fetch_historical_feature_values(\n",
|
||||||
|
" entity_df=entity_df,\n",
|
||||||
|
" features=[movies_feature],\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "2a4e033321ad"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"## Cleaning up"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "ffd3dc65a25f"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"### Delete feature and feature group"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "7517048d8510"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"movies_feature.delete()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "8488287340ca"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"fg.delete()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "753e85f60d06"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"### Delete BigQuery dataset and table"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "99e984fe9c53"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"client = bigquery.Client()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "6cc5fdf51c9e"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"client.delete_table(f\"{BQ_TABLE_URI}\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"id": "4bac93a9ffcb"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"client.delete_dataset(f\"{PROJECT_ID}.{BQ_DATASET_ID}\")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"name": "offline_feature_serving_from_bigquery_with_feature_registry.ipynb",
|
||||||
|
"toc_visible": true
|
||||||
|
},
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"name": "python3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 0
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user