Integrate Comet with ZenML - Experiment Tracker Integrations

Effortlessly track and visualize Comet experiments with ZenML pipelines

Seamlessly integrate Comet's powerful experiment tracking capabilities with your ZenML pipelines. Visualize metrics, models, and datasets from your automated MLOps workflows in Comet's intuitive UI, making it easy to monitor and share pipeline results across your team.

Features with ZenML

Automatically log metrics, parameters, models, and more from ZenML steps to Comet experiments
Easily enable Comet tracking in steps using the @step decorator
Retrieve Comet experiment URLs for each pipeline run via ZenML metadata
Organize experiments with automatic pipeline_name and pipeline_run_name tags
Configure additional experiment settings using CometExperimentTrackerSettings

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Main Features

Interactive web-based UI to visualize and compare experiments
Supports logging metrics, hyperparameters, datasets, models, and more
Workspaces and projects to organize experiments across teams
Extensive visualization and charting of tracked data
Easy sharing of experiment results and insights

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How to use ZenML with Comet


from zenml import step

@step(experiment_tracker="comet_tracker")
def my_step():
    ...
    # go through some experiment tracker methods
    experiment_tracker.log_metrics({"my_metric": 42})
    experiment_tracker.log_params({"my_param": "hello"})

    # or use the Experiment object directly
    experiment_tracker.experiment.log_model(...)

    # or pass the Comet Experiment object into helper methods
    from comet_ml.integration.sklearn import log_model
    log_model(
        experiment=experiment_tracker.experiment,
        model_name="SVC",
        model=model,
    )
		...

Additional Resources

Code Example of using ZenML and Comet together Comet Experiment Tracking Overview