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Mlflow

Concepts

a Run records: - Code Version - Start & End time - Source (the file used to launch, or project) - Parameters - Metrics - Artifacts

A set of Runs could be organized into an Experiment

Where Runs Are Recorded

  • Local file path specified as file:/my/local/dir
  • Database encoded as <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>
  • HTTP server specified as https://my-server:5000
  • Databricks workspace

Logging Data to Runs

  • mlflow.set_tracking_uri() connects to a tracking URI. You can also set the MLFLOW_TRACKING_URI (See above for "Where Runs Are Recorded")
  • mlflow.tracking.get_tracking_uri() returns the current tracking URI.
  • mlflow.create_experiment() creates a new experiment and returns its ID. Runs can be launched under the experiment by passing the experiment ID to mlflow.start_run.
  • mlflow.set_experiment() sets an experiment as active. If the experiment does not exist, creates a new experiment. If you do not specify an experiment in mlflow.start_run(), new runs are launched under this experiment.

  • mlflow.start_run() returns the currently active run (if one exists), or starts a new run and returns a mlflow.ActiveRun object usable as a context manager for the current run. You do not need to call start_run explicitly: calling one of the logging functions with no active run automatically starts a new one.

  • mlflow.end_run() ends the currently active run, if any, taking an optional run status.

  • mlflow.active_run() returns a mlflow.entities.Run object corresponding to the currently active run, if any.

  • mlflow.log_param() logs a single key-value param in the currently active run. The key and value are both strings. Use mlflow.log_params() to log multiple params at once.

  • mlflow.log_metric() logs a single key-value metric. The value must always be a number. MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log multiple metrics at once.

  • mlflow.set_tag() sets a single key-value tag in the currently active run. The key and value are both strings. Use mlflow.set_tags() to set multiple tags at once.

  • mlflow.log_artifact() logs a local file or directory as an artifact, optionally taking an artifact_path to place it in within the run’s artifact URI. Run artifacts can be organized into directories, so you can place the artifact in a directory this way.

  • mlflow.log_artifacts() logs all the files in a given directory as artifacts, again taking an optional artifact_path.

  • mlflow.get_artifact_uri() returns the URI that artifacts from the current run should be logged to.