| Name | optuna-integration JSON |
| Version |
4.5.0
JSON |
| download |
| home_page | None |
| Summary | Integration libraries of Optuna. |
| upload_time | 2025-08-18 06:36:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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# Optuna-Integration
[](https://www.python.org)
[](https://pypi.python.org/pypi/optuna-integration)
[](https://anaconda.org/conda-forge/optuna-integration)
[](https://github.com/optuna/optuna-integration)
[](https://codecov.io/gh/optuna/optuna-integration/branch/main)
[](https://optuna-integration.readthedocs.io/en/stable/)
This package is an integration module of [Optuna](https://github.com/optuna/optuna), an automatic Hyperparameter optimization software framework.
The modules in this package provide users with extended functionalities for Optuna in combination with third-party libraries such as PyTorch, sklearn, and TensorFlow.
> [!NOTE]
> You can find more information in [**our official documentations**](https://optuna-integration.readthedocs.io/en/stable/) and [**API reference**](https://optuna-integration.readthedocs.io/en/stable/reference/index.html).
## Installation
Optuna-Integration is available via [pip](https://pypi.org/project/optuna-integration/) and
on [conda](https://anaconda.org/conda-forge/optuna-integration).
```bash
# PyPI
$ pip install optuna-integration
# Anaconda Cloud
$ conda install -c conda-forge optuna-integration
```
> [!IMPORTANT]
> As dependencies of all the modules are large and complicated, the commands above install only the common dependencies.
> Dependencies for each module can be installed via pip.
> For example, if you would like to install the dependencies of `optuna_integration.botorch` and `optuna_integration.lightgbm`, you can install them via:
> ```shell
> $ pip install optuna-integration[botorch,lightgbm]
> ```
> [!NOTE]
> Optuna-Integration supports from Python 3.8 to Python 3.12.
> Optuna Docker image is also provided at [DockerHub](https://hub.docker.com/r/optuna/optuna).
## Integration Modules
Here is the table of `optuna-integration` modules:
|Third Party Library| Example |
|:--|:--|
|[BoTorch](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#botorch)| Unavailable |
|[CatBoost](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#catboost)|[CatBoostPruningCallback](https://github.com/optuna/optuna-examples/blob/main/catboost/catboost_pruning.py)|
|[Dask](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#dask)|[DaskStorage](https://github.com/optuna/optuna-examples/tree/main/dask/dask_simple.py)|
|[FastAI](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#fast-ai)|[FastAIPruningCallback](https://github.com/optuna/optuna-examples/tree/main/fastai/fastai_simple.py)|
|[Keras](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#keras)|[KerasPruningCallback](https://github.com/optuna/optuna-examples/blob/main/keras/keras_integration.py)|
|[LightGBM](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#lightgbm)|[LightGBMPruningCallback](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_integration.py) / [LightGBMTuner](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)|
|[MLflow](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#mlflow)|[MLflowCallback](https://github.com/optuna/optuna-examples/blob/main/mlflow/keras_mlflow.py)|
|[PyTorch Distributed](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#pytorch)|[TorchDistributedTrial](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_distributed_simple.py)|
|[PyTorch Ignite](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#pytorch)|[PyTorchIgnitePruningHandler](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py)|
|[PyTorch Lightning](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#pytorch)|[PyTorchLightningPruningCallback](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_lightning_simple.py)|
|[pycma](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#pycma)|[PyCma Sampler](https://github.com/optuna/optuna-integration/blob/v4.4.0/optuna_integration/cma/cma.py#L44-L64)|
|[SHAP](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#shap)|[SHAP Importance Evaluator](https://github.com/optuna/optuna-integration/blob/v4.4.0/optuna_integration/shap/shap.py#L29-L45)|
|[scikit-learn](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#sklearn)|[OptunaSearchCV](https://github.com/optuna/optuna-examples/tree/main/sklearn/sklearn_optuna_search_cv_simple.py)|
|[skorch](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#skorch)|[SkorchPruningCallback](https://github.com/optuna/optuna-examples/tree/main/pytorch/skorch_simple.py)|
|[TensorBoard](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#tensorboard)|[TensorBoardCallback](https://github.com/optuna/optuna-examples/tree/main/tensorboard/tensorboard_simple.py)|
|[tf.keras](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#tensorflow)|[TFKerasPruningCallback](https://github.com/optuna/optuna-examples/tree/main/tfkeras/tfkeras_integration.py)|
|[Weights & Biases](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#wandb)|[WeightsAndBiasesCallback](https://github.com/optuna/optuna-examples/blob/main/wandb/wandb_integration.py)|
|[XGBoost](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#xgboost)|[XGBoostPruningCallback](https://github.com/optuna/optuna-examples/tree/main/xgboost/xgboost_integration.py)|
|[AllenNLP](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#allennlp)*|[AllenNLPPruningCallback](https://github.com/optuna/optuna-examples/blob/main/allennlp/allennlp_simple.py)|
|[Chainer](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#chainer)*|[ChainerPruningExtension](https://github.com/optuna/optuna-examples/tree/main/chainer/chainer_integration.py)|
|[ChainerMN](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#chainermn)* | [ChainerMNStudy](https://github.com/optuna/optuna-examples/tree/main/chainer/chainermn_simple.py) |
|[MXNet](https://optuna-integration.readthedocs.io/en/stable/reference/index.html#mxnet)*|Unavailable|
> [!WARNING]
> `*` shows deprecated modules and they might be removed in the future.
## Communication
* [GitHub Discussions] for questions.
* [GitHub Issues] for bug reports and feature requests.
[GitHub Discussions]: https://github.com/optuna/optuna-integration/discussions
[GitHub issues]: https://github.com/optuna/optuna-integration/issues
## Contribution
Any contributions to Optuna-Integration are more than welcome!
For general guidelines how to contribute to the project, take a look at [CONTRIBUTING.md](./CONTRIBUTING.md).
## Reference
If you use Optuna in one of your research projects, please cite [our KDD paper](https://doi.org/10.1145/3292500.3330701) "Optuna: A Next-generation Hyperparameter Optimization Framework":
<details open>
<summary>BibTeX</summary>
```bibtex
@inproceedings{akiba2019optuna,
title={{O}ptuna: A Next-Generation Hyperparameter Optimization Framework},
author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
booktitle={The 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={2623--2631},
year={2019}
}
```
</details>
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"description": "# Optuna-Integration\n\n[](https://www.python.org)\n[](https://pypi.python.org/pypi/optuna-integration)\n[](https://anaconda.org/conda-forge/optuna-integration)\n[](https://github.com/optuna/optuna-integration)\n[](https://codecov.io/gh/optuna/optuna-integration/branch/main)\n[](https://optuna-integration.readthedocs.io/en/stable/)\n\nThis package is an integration module of [Optuna](https://github.com/optuna/optuna), an automatic Hyperparameter optimization software framework.\nThe modules in this package provide users with extended functionalities for Optuna in combination with third-party libraries such as PyTorch, sklearn, and TensorFlow.\n\n> [!NOTE]\n> You can find more information in [**our official documentations**](https://optuna-integration.readthedocs.io/en/stable/) and [**API reference**](https://optuna-integration.readthedocs.io/en/stable/reference/index.html).\n\n## Installation\n\nOptuna-Integration is available via [pip](https://pypi.org/project/optuna-integration/) and\non [conda](https://anaconda.org/conda-forge/optuna-integration).\n\n```bash\n# PyPI\n$ pip install optuna-integration\n\n# Anaconda Cloud\n$ conda install -c conda-forge optuna-integration\n```\n\n> [!IMPORTANT]\n> As dependencies of all the modules are large and complicated, the commands above install only the common dependencies.\n> Dependencies for each module can be installed via pip.\n> For example, if you would like to install the dependencies of `optuna_integration.botorch` and `optuna_integration.lightgbm`, you can install them via:\n> ```shell\n> $ pip install optuna-integration[botorch,lightgbm]\n> ```\n\n> [!NOTE]\n> Optuna-Integration supports from Python 3.8 to Python 3.12.\n> Optuna Docker image is also provided at [DockerHub](https://hub.docker.com/r/optuna/optuna).\n\n## Integration Modules\n\nHere is the table of `optuna-integration` modules:\n\n|Third Party Library| Example 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