MLFlowResultTracker

class MLFlowResultTracker(tracking_uri: str | None = None, experiment_id: int | None = None, experiment_name: str | None = None, tags: dict[str, Any] | None = None)[source]

Bases: ResultTracker

A tracker for MLflow.

Initialize result tracking via MLFlow.

Parameters:

tracking_uri (str | None) – The tracking uri.
experiment_id (int | None) – The experiment ID. If given, this has to be the ID of an existing experiment in MFLow. Has priority over experiment_name.
experiment_name (str | None) – The experiment name. If this experiment name exists, add the current run to this experiment. Otherwise create an experiment of the given name.
tags (dict[str, Any] | None) – The additional run details which are presented as tags to be logged

Methods Summary

`end_run`([success])	End a run.
`log_metrics`(metrics[, step, prefix])	Log metrics to result store.
`log_params`(params[, prefix])	Log parameters to result store.
`start_run`([run_name])	Start a run with an optional name.

Methods Documentation

end_run(success: bool = True) → None[source]

End a run.

HAS to be called after the experiment is finished.

Parameters:: success (bool) – Can be used to signal failed runs. May be ignored.
Return type:: None

log_metrics(metrics: Mapping[str, float], step: int | None = None, prefix: str | None = None) → None[source]

Log metrics to result store.

Parameters:

metrics (Mapping[str, float]) – The metrics to log.
step (int | None) – An optional step to attach the metrics to (e.g. the epoch).
prefix (str | None) – An optional prefix to prepend to every key in metrics.

Return type:

None

log_params(params: Mapping[str, Any], prefix: str | None = None) → None[source]

Log parameters to result store.

Parameters:

params (Mapping[str, Any])
prefix (str | None)

Return type:

None

start_run(run_name: str | None = None) → None[source]

Start a run with an optional name.

Parameters:: run_name (str | None)
Return type:: None