aimz.mlflow.autolog#

aimz.mlflow.autolog(*, log_input_examples=False, log_model_signatures=True, log_models=True, disable=False, silent=False, registered_model_name=None, extra_tags=None)[source]#

Enable and configure autologging for aimz with MLflow.

Autologging is performed when you call:
Logs the following:

  • Selected arguments to the methods, together with param_input, param_output, inference_method, and optimizer as parameters.

  • The final evidence lower bound (ELBO) loss as a metric.

  • The source code of the kernel function used in the model as a text artifact.

  • An MLflow Model containing the fitted estimator as an artifact.

Parameters:

  • log_input_examples (bool) – If True, input examples from training datasets are collected and logged along with model artifacts during training. If False, input examples are not logged. Note: Input examples are MLflow model attributes and are only collected if log_models is also True.

  • log_model_signatures (bool) – If True, ModelSignatures describing model inputs and outputs are collected and logged along with model artifacts during training. If False, signatures are not logged. Note: Model signatures are MLflow model attributes and are only collected if log_models is also True.

  • log_models (bool) – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged. Input examples and model signatures, which are attributes of MLflow models, are also omitted when log_models is False.

  • disable (bool) – If True, disables all supported autologging integrations. If False, enables all supported autologging integrations.

  • silent (bool) – If True, suppress all event logs and warnings from MLflow during autologging setup and training execution. If False, show all events and warnings during autologging setup and training execution.

  • registered_model_name (str | None) – If given, each time a model is trained, it is registered as a new model version of the registered model with this name. The registered model is created if it does not already exist.

  • extra_tags (dict[str, str] | None) – A dictionary of extra tags to set on each managed run created by autologging.

Return type:

None