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Optuna-Integration

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This package is an integration module of 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.

Installation

Optuna-Integration is available via pip and on conda.

# PyPI
$ pip install optuna-integration

# Anaconda Cloud
$ conda install -c conda-forge optuna-integration

Integration Modules

Here is the table of optuna-integration modules:

Third Party Library Example
BoTorch Unavailable
CatBoost CatBoostPruningCallback
Dask DaskStorage
FastAI FastAIPruningCallback
Keras KerasPruningCallback
LightGBM LightGBMPruningCallback / LightGBMTuner
MLflow* MLflowCallback
PyTorch Distributed TorchDistributedTrial
PyTorch Ignite PyTorchIgnitePruningHandler
PyTorch Lightning PyTorchLightningPruningCallback
pycma* PyCma Sampler
SHAP SHAP Importance Evaluator
scikit-learn OptunaSearchCV
skorch SkorchPruningCallback
TensorBoard* TensorBoardCallback
tf.keras TFKerasPruningCallback
Weights & Biases* WeightsAndBiasesCallback
XGBoost XGBoostPruningCallback
Trackio* TrackioCallback
AllenNLP* AllenNLPPruningCallback
Chainer* ChainerPruningExtension
ChainerMN* ChainerMNStudy
MXNet* Unavailable

Communication

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.

Reference

If you use Optuna in one of your research projects, please cite our KDD paper "Optuna: A Next-generation Hyperparameter Optimization Framework":

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}
}

Metadata

Release files for optuna-integration 4.9.0

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