azureml-train-core

import os from azureml.core import Workspace, ScriptRunConfig, Environment from azureml.core.conda_dependencies import CondaDependencies # NOTE: Replace with your actual workspace details or ensure environment variables are set 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") try: ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group) print(f"Found workspace {ws.name} at {ws.get_details()['location']}") except Exception: print("Could not connect to workspace. Ensure AZURE_SUBSCRIPTION_ID, AZURE_RESOURCE_GROUP, AZURE_WORKSPACE_NAME env vars are set or replace placeholders.") # For a real run, you'd create a workspace if not found # ws = Workspace.create(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group, location='eastus') # Example: Define an environment env = Environment('my-training-env') c_deps = CondaDependencies() c_deps.add_conda_package('scikit-learn') c_deps.add_pip_package('azureml-sdk') env.python.conda_dependencies = c_deps # Create a dummy training script (e.g., train.py) # with open('train.py', 'w') as f: # f.write(""" # import os # print('Hello from Azure ML training!') # print(f'Running on compute: {os.environ.get("AML_RUN_ID", "unknown")}') # """) # Create a ScriptRunConfig (modern way to submit training) # src = ScriptRunConfig(source_directory='./', # script='train.py', # environment=env, # compute_target='cpu-cluster') # Replace with actual compute target # Submit the run (uncomment for actual execution) # if 'ws' in locals(): # run = ws.submit(src) # run.wait_for_completion(show_output=True) # print(f"Run finished with status: {run.get_status()}") else: print("Workspace not initialized, skipping run submission example.")