sagemaker-mlflow

import sagemaker_mlflow import mlflow import os # Ensure MLflow is installed alongside sagemaker-mlflow # Option 1: Explicitly set the tracking URI # This assumes a SageMaker MLflow Tracking Server is already provisioned. # You might need to set AWS_REGION environment variable if not in SageMaker Studio. # os.environ['AWS_REGION'] = 'us-east-1' try: tracking_uri = sagemaker_mlflow.get_sagemaker_tracking_uri() mlflow.set_tracking_uri(tracking_uri) print(f"MLflow Tracking URI set to: {tracking_uri}") with mlflow.start_run() as run: mlflow.log_param("alpha", 0.5) mlflow.log_metric("rmse", 0.75) print(f"Logged run with ID: {run.info.run_id}") except Exception as e: print(f"Failed to set MLflow Tracking URI or start run: {e}") print("Ensure a SageMaker MLflow Tracking Server is deployed and your IAM role has permissions.") # Option 2 (recommended for SageMaker Studio environments): # sagemaker_mlflow.enable_global_sagemaker_mlflow_tracking() # print("Global SageMaker MLflow tracking enabled.") # with mlflow.start_run() as run: # mlflow.log_param("beta", 0.1) # mlflow.log_metric("accuracy", 0.99) # print(f"Logged another run with ID: {run.info.run_id}")