azureml-train-automl-client

import os from azureml.core import Workspace, Experiment, Dataset from azureml.train.automl import AutoMLConfig # Placeholder for Azure ML Workspace details subscription_id = os.environ.get('AZURE_SUBSCRIPTION_ID', 'your_subscription_id') resource_group = os.environ.get('AZURE_RESOURCE_GROUP', 'your_resource_group') workspace_name = os.environ.get('AZURE_WORKSPACE_NAME', 'your_workspace_name') # Ensure these are replaced with actual valid values or environment variables if 'your_' in subscription_id or 'your_' in resource_group or 'your_' in workspace_name: print("WARNING: Please set AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, AZURE_WORKSPACE_NAME environment variables ") print("or replace placeholder values in the quickstart code for actual execution.") # Exit or use dummy data for demonstration if not intended to run live exit(1) try: ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group) print(f"Workspace '{ws.name}' loaded successfully.") except Exception as e: print(f"Could not load workspace. Error: {e}") # Handle workspace creation or authentication error exit(1) # Create an experiment experiment = Experiment(workspace=ws, name='automl-quickstart-experiment') # Example: Load a registered dataset (replace with your actual dataset) # For a real run, you'd register a dataset or use a local one. # This is a dummy to make the code runnable for structure. # In a real scenario, you'd load your training data like: # training_data = Dataset.get_by_name(ws, name='my_training_dataset') # Create a dummy AutoMLConfig (requires actual data and compute for a real run) automl_config = AutoMLConfig( task='classification', primary_metric='accuracy', experiment_timeout_minutes=30, training_data=None, # Replace with your actual Dataset object, e.g., training_data label_column_name='target_column', # Replace with your target column name compute_target='cpu-cluster', # Replace with your compute target name enable_early_stopping=True, n_cross_validations=5, max_concurrent_iterations=2, max_cores_per_iteration=-1, # Use all available cores # Additional settings like featurization, blacklisting, etc. can be added ) print("AutoMLConfig created. To run, you would submit it:") print("run = experiment.submit(automl_config, show_output=True)") print("run.wait_for_completion(show_output=True)") # To run the experiment: # run = experiment.submit(automl_config, show_output=True) # run.wait_for_completion(show_output=True)