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}")See the Dask-Optuna documentation for more information.
