optuna_integration.ChainerMNStudy

class optuna_integration.ChainerMNStudy(study, comm)[source]

A wrapper of Study to incorporate Optuna with ChainerMN.

See also

ChainerMNStudy provides the same interface as Study. Please refer to optuna.study.Study for further details.

See the example if you want to optimize an objective function that trains neural network written with ChainerMN.

Parameters:

study (Study) – A Study object.
comm (CommunicatorBase) – A ChainerMN communicator.

Warning

Deprecated in v3.5.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v5.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v3.5.0.

Methods

optimize(func[, n_trials, timeout, catch])

Optimize an objective function.

optimize(func, n_trials=None, timeout=None, catch=())[source]

Optimize an objective function.

This method provides the same interface as optuna.study.Study.optimize() except the absence of n_jobs argument.

Parameters:

func (Callable[['ChainerMNTrial', 'CommunicatorBase'], float])
n_trials (int | None)
timeout (float | None)
catch (tuple[type[Exception], ...])

Return type:

None