tracking

Note

See the Glossary for the meaning of the acronyms used in this guide.

mlflow.py

A task plugin module for using the MLFlow Tracking service.

log_metrics(metrics: Dict[str, float]) → None

Logs metrics to the MLFlow Tracking service for the current run.

Parameters

metrics – A dictionary with the metrics to be logged. The keys are the metric names and the values are the metric values.

See also

• mlflow.log_metric()

log_parameters(parameters: Dict[str, float]) → None

Logs parameters to the MLFlow Tracking service for the current run.

Parameters can only be set once per run.

Parameters

parameters – A dictionary with the parameters to be logged. The keys are the parameter names and the values are the parameter values.

See also

• mlflow.log_param()

log_tensorflow_keras_estimator(estimator: tensorflow.keras.models.Sequential, model_dir: str) → None

Logs a Keras estimator trained during the current run to the MLFlow registry.

Parameters

• estimator – A trained Keras estimator.

• model_dir – The relative artifact directory where MLFlow should save the model.