optuna_integration.MLflowCallback
- class optuna_integration.MLflowCallback(tracking_uri=None, metric_name='value', create_experiment=True, mlflow_kwargs=None, tag_study_user_attrs=False, tag_trial_user_attrs=True)[source]
Callback to track Optuna trials with MLflow.
This callback adds relevant information that is tracked by Optuna to MLflow.
Example
Add MLflow callback to Optuna optimization.
- Parameters:
tracking_uri (str | None) –
The URI of the MLflow tracking server.
Please refer to mlflow.set_tracking_uri for more details.
metric_name (str | Sequence[str]) – Name assigned to optimized metric. In case of multi-objective optimization, list of names can be passed. Those names will be assigned to metrics in the order returned by objective function. If single name is provided, or this argument is left to default value, it will be broadcasted to each objective with a number suffix in order returned by objective function e.g. two objectives and default metric name will be logged as
value_0
andvalue_1
. The number of metrics must be the same as the number of values an objective function returns.create_experiment (bool) – When
True
, new MLflow experiment will be created for each optimization run, named after the Optuna study. Setting this argument toFalse
lets user run optimization under existing experiment, set via mlflow.set_experiment, by passingexperiment_id
as one ofmlflow_kwargs
or under default MLflow experiment, when no additional arguments are passed. Note that this argument must be set toFalse
when using Optuna with this callback within Databricks Notebook.mlflow_kwargs (dict[str, Any] | None) –
Set of arguments passed when initializing MLflow run. Please refer to MLflow API documentation for more details.
Note
nest_trials
argument added in v2.3.0 is a part ofmlflow_kwargs
since v3.0.0. Anyone usingnest_trials=True
should migrate tomlflow_kwargs={"nested": True}
to avoid raisingTypeError
.tag_study_user_attrs (bool) – Flag indicating whether or not to add the study’s user attrs to the mlflow trial as tags. Please note that when this flag is set, key value pairs in
user_attrs
will supersede existing tags.tag_trial_user_attrs (bool) – Flag indicating whether or not to add the trial’s user attrs to the mlflow trial as tags. Please note that when both trial and study user attributes are logged, the latter will supersede the former in case of a collision.
Note
Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.
Methods
Decorator for using MLflow logging in the objective function.
- track_in_mlflow()[source]
Decorator for using MLflow logging in the objective function.
This decorator enables the extension of MLflow logging provided by the callback.
All information logged in the decorated objective function will be added to the MLflow run for the trial created by the callback.
Example
Add additional logging to MLflow.
- Returns:
Objective function with tracking to MLflow enabled.
- Return type:
Note
Added in v2.9.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.9.0.