 = client.get_pipeline("<PIPELINE_NAME>").last_runtrainer_step = last_run.get_step("<STEP_NAME>")

tracking_url = trainer_step.run_metadata["experiment_tracker_url"].value

This will link to your deployed MLflow instance UI, or the local MLflow experiment file.

Additional Configuration

You can further configure the experiment tracker using MLFlowExperimentTrackerSettings:

from zenml.integrations.mlflow.flavors.mlflow_experiment_tracker_flavor import MLFlowExperimentTrackerSettings

mlflow_settings = MLFlowExperimentTrackerSettings(

nested=True,

tags={"key": "value"}

@step(

experiment_tracker="<MLFLOW_TRACKER_STACK_COMPONENT_NAME>",

settings={

"experiment_tracker.mlflow": mlflow_settings

For more details and advanced options, see the full MLflow Experiment Tracker documentation.

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Last updated 19 days ago