Metadata-Version: 2.1
Name: dask-optuna
Version: 0.0.1
Summary: Scaling Optuna with Dask
Home-page: https://github.com/jrbourbeau/dask-optuna
Author: James Bourbeau
License: MIT
Project-URL: Documentation, https://jrbourbeau.github.io/dask-optuna
Project-URL: Source Code, https://github.com/jrbourbeau/dask-optuna
Project-URL: Issue Tracker, https://github.com/jrbourbeau/dask-optuna/issues
Description: # Dask-Optuna
        
        [![Tests](https://github.com/jrbourbeau/dask-optuna/workflows/Tests/badge.svg)](https://github.com/jrbourbeau/dask-optuna/actions?query=workflow%3ATests+branch%3Amaster)
        [![Documentation](https://github.com/jrbourbeau/dask-optuna/workflows/Documentation/badge.svg)](https://github.com/jrbourbeau/dask-optuna/actions?query=workflow%3ADocumentation+branch%3Amaster)
        [![Pre-commit](https://github.com/jrbourbeau/dask-optuna/workflows/Pre-commit/badge.svg)](https://github.com/jrbourbeau/dask-optuna/actions?query=workflow%3APre-commit+branch%3Amaster)
        
        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)
        
Keywords: optuna,dask,distributed
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
