optuna.integration.ChainerMNStudy¶

class optuna.integration.ChainerMNStudy(study: Study, comm: CommunicatorBase)[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 – A Study object.
comm – A ChainerMN communicator.

__init__(study: Study, comm: CommunicatorBase) → None [source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(study, comm)	Initialize self.
`optimize`(func[, n_trials, timeout, catch])	Optimize an objective function.

optimize(func: Callable[[ChainerMNTrial, CommunicatorBase], float], n_trials: Optional[int] = None, timeout: Optional[float] = None, catch: Tuple[Type[Exception], …] = ()) → None [source]¶

Optimize an objective function.

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