comet-ml

import os from comet_ml import Experiment # Ensure COMET_API_KEY and COMET_WORKSPACE are set as environment variables # or use comet_ml.login() if you prefer interactive login. # For example: os.environ['COMET_API_KEY'] = 'YOUR_API_KEY' # os.environ['COMET_WORKSPACE'] = 'YOUR_WORKSPACE' # Or, for local testing without an explicit API key (results only stored locally): # experiment = Experiment(project_name="my-test-project", log_code=False, display_summary_to_terminal=False) # Initialize an experiment # It's best practice to use a context manager to ensure the experiment terminates correctly with Experiment(project_name="my-quickstart-project", api_key=os.environ.get('COMET_API_KEY', None), workspace=os.environ.get('COMET_WORKSPACE', None), auto_output_logging='simple', # capture print statements auto_metric_logging=True, # capture common metrics log_code=True # logs your script code ) as experiment: # Log hyperparameters hyper_params = {"learning_rate": 0.001, "epochs": 10, "batch_size": 32} experiment.log_parameters(hyper_params) # Simulate a training loop for epoch in range(hyper_params["epochs"]): # Simulate metric calculation accuracy = 0.5 + (epoch * 0.05) + (hyper_params["learning_rate"] * 100) loss = 1.0 - (epoch * 0.08) - (hyper_params["learning_rate"] * 50) # Log metrics for each epoch experiment.log_metric("accuracy", accuracy, step=epoch) experiment.log_metric("loss", loss, step=epoch) # Log a final metric or result final_accuracy = accuracy # from the last epoch experiment.log_metric("final_accuracy", final_accuracy) print(f"Experiment URL: {experiment.url}") print("Experiment finished.")