Saving/Resuming Study with RDB Backend

An RDB backend enables persistent experiments (i.e., to save and resume a study) as well as access to history of studies. In addition, we can run multi-node optimization tasks with this feature, which is described in Distributed Optimization.

In this section, let’s try simple examples running on a local environment with SQLite DB.


You can also utilize other RDB backends, e.g., PostgreSQL or MySQL, by setting the storage argument to the DB’s URL. Please refer to SQLAlchemy’s document for how to set up the URL.

New Study

We can create a persistent study by calling create_study() function as follows. An SQLite file example.db is automatically initialized with a new study record.

import optuna
study_name = 'example-study'  # Unique identifier of the study.
study = optuna.create_study(study_name=study_name, storage='sqlite:///example.db')

To run a study, call optimize() method passing an objective function.

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

study.optimize(objective, n_trials=3)

Resume Study

To resume a study, instantiate a Study object passing the study name example-study and the DB URL sqlite:///example.db.

study = optuna.create_study(study_name='example-study', storage='sqlite:///example.db', load_if_exists=True)
study.optimize(objective, n_trials=3)

Experimental History

We can access histories of studies and trials via the Study class. For example, we can get all trials of example-study as:

import optuna
study = optuna.create_study(study_name='example-study', storage='sqlite:///example.db', load_if_exists=True)
df = study.trials_dataframe()

The method trials_dataframe() returns a pandas dataframe like:

number                state       value             datetime_start          datetime_complete    params system_attrs
                                                                                                      x      _number
     0  TrialState.COMPLETE   25.301959 2019-03-14 10:57:27.716141 2019-03-14 10:57:27.746354 -3.030105            0
     1  TrialState.COMPLETE    1.406223 2019-03-14 10:57:27.774461 2019-03-14 10:57:27.835520  0.814157            1
     2  TrialState.COMPLETE   44.010366 2019-03-14 10:57:27.871365 2019-03-14 10:57:27.926247 -4.634031            2
     3  TrialState.COMPLETE   55.872181 2019-03-14 10:59:00.845565 2019-03-14 10:59:00.899305  9.474770            3
     4  TrialState.COMPLETE  113.039223 2019-03-14 10:59:00.921534 2019-03-14 10:59:00.947233 -8.631991            4
     5  TrialState.COMPLETE   57.319570 2019-03-14 10:59:00.985909 2019-03-14 10:59:01.028819  9.570969            5

A Study object also provides properties such as trials, best_value, best_params (see also First Optimization).

study.best_params  # Get best parameters for the objective function.
study.best_value  # Get best objective value.
study.best_trial  # Get best trial's information.
study.trials  # Get all trials' information.