Supported tracking backends

The tracking module logs experiment metadata, metrics, and artifacts to external backends.

Tracking workflow

RAITAP writes outputs to the local directory first, then forwards them to the tracking backend. The local output directory remains intact regardless of the tracking configuration.

MLflow

Docs

MLflow docs

Configuration

tracking:
  _target_: MLFlowTracker
  output_forwarding_url: http://127.0.0.1:5000
  log_model: true
  open_when_done: false

If output_forwarding_url is not set, MLflow stores runs locally in ./mlruns.

Logged artifacts

For each run, MLflow receives:

  • Configuration as JSON

  • Dataset metadata

  • Scalar performance metrics

  • Transparency outputs (attributions and visualisations)

  • Model artifacts (when log_model: true)

Model logging

When log_model: true, RAITAP logs the assessed model to MLflow. This can take significant time and resources for large models.