Command-Line Interface




Create a new study.


Delete a specified study.


Launch web dashboard (beta).

storage upgrade

Upgrade the schema of a storage.


Show a list of studies.

study optimize

Start optimization of a study.

study set-user-attr

Set a user attribute to a study.

Optuna provides command-line interface as shown in the above table.

Let us assume you are not in IPython shell and writing Python script files instead. It is totally fine to write scripts like the following:

import optuna

def objective(trial):
    x = trial.suggest_float("x", -10, 10)
    return (x - 2) ** 2

if __name__ == "__main__":
    study = optuna.create_study()
    study.optimize(objective, n_trials=100)
    print("Best value: {} (params: {})\n".format(study.best_value, study.best_params))


Best value: 0.0015047239806386503 (params: {'x': 2.0387907718489675})

However, we can reduce boilerplate codes by using our optuna command. Let us assume that contains only the following code.

def objective(trial):
    x = trial.suggest_float("x", -10, 10)
    return (x - 2) ** 2

Even so, we can invoke the optimization as follows. (Don’t care about --storage sqlite:///example.db for now, which is described in Saving/Resuming Study with RDB Backend.)

$ cat
def objective(trial):
    x = trial.suggest_float('x', -10, 10)
    return (x - 2) ** 2

$ STUDY_NAME=`optuna create-study --storage sqlite:///example.db`
$ optuna study optimize objective --n-trials=100 --storage sqlite:///example.db --study-name $STUDY_NAME
[I 2018-05-09 10:40:25,196] Finished a trial resulted in value: 54.353767789264026. Current best value is 54.353767789264026 with parameters: {'x': -5.372500782588228}.
[I 2018-05-09 10:40:25,197] Finished a trial resulted in value: 15.784266965526376. Current best value is 15.784266965526376 with parameters: {'x': 5.972941852774387}.
[I 2018-05-09 10:40:26,204] Finished a trial resulted in value: 14.704254135013741. Current best value is 2.280758099793617e-06 with parameters: {'x': 1.9984897821018828}.

Please note that only contains the definition of the objective function. By giving the script file name and the method name of objective function to optuna study optimize command, we can invoke the optimization.

Total running time of the script: ( 0 minutes 0.339 seconds)

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