sagemaker-experiments

import os import sagemaker from sagemaker.experiments import Experiment, Trial from sagemaker.experiments.tracker import Tracker # Ensure a SageMaker session is available. In a SageMaker Studio or Job, # a session is usually automatically configured. # For local execution, ensure AWS credentials and region are set up (e.g., via environment vars). try: sess = sagemaker.Session() except Exception: # Fallback for local execution outside a SageMaker context if default fails import boto3 print("Creating sagemaker.Session with boto3.Session for local execution.") sess = sagemaker.Session(boto3.Session(region_name=os.environ.get("AWS_REGION", "us-east-1"))) experiment_name = f"my-quickstart-experiment-{os.getpid()}" trial_name = f"my-quickstart-trial-{os.getpid()}" # 1. Create an Experiment # Using .create() ensures a new experiment; .load() would retrieve an existing one. my_experiment = Experiment.create( experiment_name=experiment_name, description="A simple quickstart experiment for sagemaker-experiments", sagemaker_session=sess ) print(f"Created Experiment: {my_experiment.experiment_name}") # 2. Create a Trial within the Experiment my_trial = Trial.create( trial_name=trial_name, experiment_name=experiment_name, sagemaker_session=sess ) print(f"Created Trial: {my_trial.trial_name}") # 3. Use a Tracker to log parameters and metrics (e.g., simulating a training run) with Tracker.create(display_name="TrainingJobComponent", sagemaker_session=sess) as tracker: tracker.log_parameters({"learning_rate": 0.01, "epochs": 10, "optimizer": "Adam"}) tracker.log_metrics({"accuracy": 0.85, "loss": 0.15, "f1_score": 0.82}) print(f"Logged data to TrialComponent: {tracker.trial_component.trial_component_name}") # Associate the tracker's automatically created trial component with our trial my_trial.add_trial_component(tracker.trial_component) print("Experiment, Trial, and TrialComponent created and data logged.") print("You can view these in SageMaker Studio under the Experiments tab.") # Optional: Clean up created resources (uncomment to enable) # print("Cleaning up resources...") # my_trial.delete_all_trial_components() # my_trial.delete() # my_experiment.delete() # print("Cleanup complete.")