aimz.mlflow.autolog#

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

Enable (or disable) and configure autologging from aimz to MLflow.

Logs the following:

  • parameters specified in fit() and fit_on_batch(), together with param_input, param_output, inference_method, and, for SVI inference, optimizer.

  • the evidence lower bound (ELBO) loss on each optimization step (SVI inference only).

  • the source code of the kernel function used by the model.

  • the training dataset as a run input, if applicable.

  • trained model, including:

    • an example of valid input.

    • inferred signature of the inputs and outputs of the model.

Autologging is performed when you call fit() or fit_on_batch().

Parameters:

  • log_input_examples (bool) – If True, input examples from training datasets are collected and logged along with aimz 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 aimz 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.

  • log_datasets (bool) – If True, train dataset information is logged to MLflow Tracking if applicable. If False, dataset information is not logged.

  • disable (bool) – If True, disables the aimz autologging integration. If False, enables the aimz autologging integration.

  • exclusive (bool) – If True, autologged content is not logged to user-created fluent runs. If False, autologged content is logged to the active fluent run, which may be user-created.

  • disable_for_unsupported_versions (bool) – Accepted for parity with MLflow’s built-in autologging integrations. MLflow’s version-compatibility gate only applies to flavors shipped with MLflow, so this flag currently has no effect for aimz.

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

  • 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