mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
Sdk feature store ver1 (#790)
* Added condition to create Featurestore if it doesn't exist * Ran Linter Test * Made changes mentioned in review * Ran Linter Test * Attached uuid to featurestore_id to avoid error while creating featurestore with existing name * Ran linter test Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>
This commit is contained in:
SamyuktaDR
co-authored by
Andrew Ferlitsch
Andrew Ferlitsch
parent
55e37f795c
commit
6cac60f74a
@@ -29,6 +29,8 @@
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"id": "JAPoU8Sm5E6e"
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},
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"source": [
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"# Online and Batch predictions using Vertex AI Feature Store\n",
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"\n",
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"<table align=\"left\">\n",
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" <td>\n",
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" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\">\n",
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@@ -51,19 +53,22 @@
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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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"id": "c4aaea3bab5e"
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},
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"source": [
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"## Overview\n",
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"\n",
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"This notebook introduces Vertex AI Feature Store, a managed cloud service for machine learning engineers and data scientists to store, serve, manage and share machine learning features at a large scale.\n",
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"\n",
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"This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n",
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"\n",
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"### Dataset\n",
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"\n",
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"This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online. \n",
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"\n",
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"This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\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": "71779c8088bf"
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},
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"source": [
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"### Objective\n",
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"\n",
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"In this notebook, you will learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.\n",
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@@ -79,8 +84,26 @@
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"- Create featurestore, entity type, and feature resources.\n",
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"- Import feature data into `Vertex AI Feature Store` resource.\n",
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"- Serve online prediction requests using the imported features.\n",
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"- Access imported features in offline jobs, such as training jobs.\n",
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"- Access imported features in offline jobs, such as training jobs."
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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": "55e01a856f57"
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},
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"source": [
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"### Dataset\n",
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"\n",
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"This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online."
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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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"### Costs \n",
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"\n",
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"This tutorial uses billable components of Google Cloud:\n",
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@@ -262,7 +285,15 @@
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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\"}"
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"import os\n",
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"\n",
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"PROJECT_ID = \"\"\n",
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"\n",
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"# Get your Google Cloud project ID from gcloud\n",
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"if not os.getenv(\"IS_TESTING\"):\n",
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" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
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" PROJECT_ID = shell_output[0]\n",
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" print(\"Project ID: \", PROJECT_ID)"
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]
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},
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{
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@@ -275,10 +306,7 @@
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"source": [
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"if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
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" PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n",
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" # Get your GCP project id from gcloud\n",
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" shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
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" PROJECT_ID = shell_output[0]\n",
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" print(\"Project ID:\", PROJECT_ID)"
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"print(\"Project ID: \", PROJECT_ID)"
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]
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},
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{
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@@ -320,7 +348,9 @@
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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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"REGION = \"[your-region]\" # @param {type:\"string\"}\n",
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"if REGION == \"[your-region]\":\n",
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" REGION = \"us-central1\""
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]
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},
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{
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@@ -329,9 +359,9 @@
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"id": "timestamp"
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},
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"source": [
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"#### Timestamp\n",
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"#### UUID\n",
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"\n",
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"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
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"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
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]
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},
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{
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@@ -342,9 +372,16 @@
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},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"import random\n",
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"import string\n",
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"\n",
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"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
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"\n",
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"# Generate a uuid of a specifed length(default=8)\n",
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"def generate_uuid(length: int = 8) -> str:\n",
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" return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
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"\n",
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"\n",
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"UUID = generate_uuid()"
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]
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},
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{
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@@ -441,7 +478,7 @@
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"source": [
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"from google.cloud.aiplatform import Feature, Featurestore\n",
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"\n",
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"FEATURESTORE_ID = \"movie_prediction\"\n",
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"FEATURESTORE_ID = \"movie_prediction\" + UUID\n",
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"INPUT_CSV_FILE = \"gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv\"\n",
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"ONLINE_STORE_FIXED_NODE_COUNT = 1"
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]
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