azureml-featurestore

import os from azure.ai.ml import MLClient from azure.ai.ml.entities import FeatureStore, FeatureStoreEntity from azure.identity import DefaultAzureCredential from azureml.featurestore import FeatureStoreClient # Replace with your actual subscription, resource group, and feature store name subscription_id = os.environ.get('AZURE_SUBSCRIPTION_ID', 'your-subscription-id') resource_group_name = os.environ.get('AZURE_RESOURCE_GROUP', 'your-resource-group') feature_store_name = os.environ.get('AZURE_FEATURE_STORE_NAME', 'my-feature-store') feature_store_location = os.environ.get('AZURE_LOCATION', 'eastus') # Authenticate and create MLClient for managing Azure ML resources try: credential = DefaultAzureCredential() ml_client = MLClient( credential=credential, subscription_id=subscription_id, resource_group_name=resource_group_name ) except Exception as e: print(f"Could not authenticate or create MLClient: {e}") print("Please ensure AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, and AZURE_LOCATION environment variables are set or replaced.") exit(1) # Create a Feature Store if it doesn't exist (this step typically requires Azure CLI or a separate script to run once) # For a runnable quickstart, we assume the feature store might already be created or we just define it. # In a real scenario, you'd check for existence and create if necessary. print(f"Attempting to get or define Feature Store '{feature_store_name}'...") feature_store = FeatureStore( name=feature_store_name, location=feature_store_location, description="My first Azure ML Feature Store" ) # This line would typically be ml_client.feature_stores.begin_create_or_update(feature_store).result() # However, for a quickstart that focuses on the azureml-featurestore SDK, we will mock the client initialization # assuming the feature store exists. print("Assuming feature store is created. Initializing FeatureStoreClient...") # Initialize FeatureStoreClient # This client is used for developing and consuming features. fs_client = FeatureStoreClient( credential=credential, subscription_id=subscription_id, resource_group_name=resource_group_name, name=feature_store_name ) print(f"Successfully initialized FeatureStoreClient for '{feature_store_name}'.") # Example: List existing feature sets (requires a feature store to be set up and contain feature sets) # In a real scenario, you would have feature sets defined and registered. # This is a placeholder to demonstrate client usage. print("Attempting to list feature sets (this might return empty if no feature sets exist)...") feature_sets = fs_client.feature_sets.list() for fs in feature_sets: print(f" - Found Feature Set: {fs.name} (Version: {fs.version})") print("Quickstart finished. For advanced usage, refer to Azure ML Feature Store documentation.")