Distributed Optimization

There is no complicated setup but just sharing the same study name among nodes/processes.

First, create a shared study using optuna create-study command (or using optuna.create_study() in a Python script).

$ optuna create-study --study-name "distributed-example" --storage "sqlite:///example.db"
[I 2020-07-21 13:43:39,642] A new study created with name: distributed-example

Then, write an optimization script. Let’s assume that foo.py contains the following code.

import optuna

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

if __name__ == '__main__':
    study = optuna.load_study(study_name='distributed-example', storage='sqlite:///example.db')
    study.optimize(objective, n_trials=100)

Finally, run the shared study from multiple processes. For example, run Process 1 in a terminal, and do Process 2 in another one. They get parameter suggestions based on shared trials’ history.

Process 1:

$ python foo.py
[I 2020-07-21 13:45:02,973] Trial 0 finished with value: 45.35553104173011 and parameters: {'x': 8.73465151598285}. Best is trial 0 with value: 45.35553104173011.
[I 2020-07-21 13:45:04,013] Trial 2 finished with value: 4.6002397305938905 and parameters: {'x': 4.144816945707463}. Best is trial 1 with value: 0.028194513284051464.

Process 2 (the same command as process 1):

$ python foo.py
[I 2020-07-21 13:45:03,748] Trial 1 finished with value: 0.028194513284051464 and parameters: {'x': 1.8320877810162361}. Best is trial 1 with value: 0.028194513284051464.
[I 2020-07-21 13:45:05,783] Trial 3 finished with value: 24.45966755098074 and parameters: {'x': 6.945671597566982}. Best is trial 1 with value: 0.028194513284051464.


We do not recommend SQLite for large scale distributed optimizations because it may cause serious performance issues. Please consider to use another database engine like PostgreSQL or MySQL.


Please avoid putting the SQLite database on NFS when running distributed optimizations. See also: https://www.sqlite.org/faq.html#q5

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