dask-optuna 0.0.2

Scaling Optuna with Dask

Dask-Optuna

Dask-Optuna helps improve integration between Optuna and Dask by leveraging Optuna's existing distributed optimization capabilities to run optimization trials in parallel on a Dask cluster. It does this by providing a Dask-compatible dask_optuna.DaskStorage storage class which wraps an Optuna storage class (e.g. Optuna's in-memory or sqlite storage) and can be used directly by Optuna. For example:

import optuna
import joblib
import dask.distributed
import dask_optuna

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

with dask.distributed.Client() as client:
    # Create a study using Dask-compatible storage
    storage = dask_optuna.DaskStorage()
    study = optuna.create_study(storage=storage)
    # Optimize in parallel on your Dask cluster
    with joblib.parallel_backend("dask"):
        study.optimize(objective, n_trials=100, n_jobs=-1)
    print(f"best_params = {study.best_params}")

Documentation

See the Dask-Optuna documentation for more information.

License

MIT License