Dask-Optuna helps improve integration between Optuna and Dask.

What Dask-Optuna does

Dask-Optuna leverages 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 dask.distributed
import dask_optuna

client = dask.distributed.Client()
# Wraps Optuna's in-memory storage
storage_1 = dask_optuna.DaskStorage()
# Wraps Optuna's SQLite DB storage
storage_2 = dask_optuna.DaskStorage("sqlite:///example.db")

The underlying Optuna storage object lives on the cluster’s scheduler and any method calls on the DaskStorage instance results in the same method being called on the underlying Optuna storage object.

This offers two primary benefits:

  1. Helps extend Optuna’s InMemoryStorage class to run across multiple processes. This is important when using remote workers in a Dask cluster or situations where Python’s GIL leads to less-than-ideal parallelization.

  2. Reduces setup when using persistent storage (e.g. creating a SQLite DB that’s globally available) as the underlying Optuna storage class on the scheduler is accessible all workers in a Dask cluster.


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}")

Community discussion

Discussions on improving integration between Dask and Optuna are taking place in both the Dask issue tracker and Optuna issue tracker. Please feel free to join these conversations if you’d like to get involved.

If you have feedback or thoughts on how Dask-Optuna may be improved, please feel free to open an issue in Dask-Optuna’s issue tracker.


When would I use this?

Dask-Optuna is useful if you want to use Optuna’s InMemoryStorage when running trials in parallel across multiple processes or if the workers in your Dask cluster don’t use the same filesystem that your Dask Client uses. If, for example, you’re using a dask.distributed.LocalCluster you may be better served by using Optuna’s built in storage classes.