opto.features.mlflow.autolog¶
MLflow autologging functionality for Trace library.
This module provides MLflow autologging functionality similar to mlflow.dspy.autolog(). Users can call trace.mlflow.autolog() at the beginning of their scripts to enable automatic logging of Trace operations to MLflow.
autolog ¶
autolog(
log_models: bool = True,
log_datasets: bool = True,
disable: bool = False,
disable_default_op_logging: bool = True,
exclusive: bool = False,
disable_for_unsupported_versions: bool = False,
silent: bool = False,
registered_model_name: Optional[str] = None,
extra_tags: Optional[Dict[str, Any]] = None,
) -> None
Enable automatic logging of Trace operations to MLflow.
This function enables MLflow autologging for Trace library operations, similar to how mlflow.dspy.autolog() works for DSPy. When enabled, Trace operations will automatically log relevant information to MLflow.
Args: log_models (bool): If True, trained models are logged as MLflow model artifacts. Defaults to True. log_datasets (bool): If True, dataset information used in training is logged to MLflow Tracking. Defaults to True. disable (bool): If True, disables MLflow autologging. Defaults to False. disable_default_op_logging (bool): If True, disables logging of default Trace operations in trace.operators exclusive (bool): If True, autologged content is not logged to additional MLflow Tracking fluent APIs. Defaults to False. disable_for_unsupported_versions (bool): If True, disable MLflow autologging for versions of Trace that have not been tested against this version of the MLflow client or are not supported. Defaults to False. silent (bool): If True, suppress all event logs and warnings from MLflow during Trace training. Defaults to False. registered_model_name (str): 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. Defaults to None. extra_tags (Dict[str, Any]): A dictionary of extra tags to set on each managed MLflow Run. Defaults to None.
Example: >>> import opto.trace as trace >>> trace.mlflow.autolog() # Enable MLflow autologging >>> # Your Trace code here - operations will be automatically logged to MLflow
is_autolog_enabled ¶
Check if MLflow autologging is currently enabled.
Returns: bool: True if MLflow autologging is enabled, False otherwise.
get_autolog_config ¶
Get the current MLflow autolog configuration.
Returns: Dict[str, Any]: The current autolog configuration, or empty dict if not configured.
disable_autolog ¶
Disable MLflow autologging for Trace operations.
This is equivalent to calling autolog(disable=True).