semvec/pypi-packages huggingface dataset:
dask-optuna 0.0.2
Scaling Optuna with Dask
License: MIT
Requires Python: >=3.6
Keywords: optuna,dask,distributed

# Dask-Optuna

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Dask-Optuna helps improve integration between [Optuna](https://optuna.org/) and [Dask](https://dask.org/)
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:

```python
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](https://jrbourbeau.github.io/dask-optuna) for more information.


## License

[MIT License](LICENSE)