Optuna-Integration

Docs

Optuna-Integration is an integration module of Optuna. This package allows us to use Optuna, an automatic Hyperparameter optimization software framework, integrated with many useful tools like PyTorch, sklearn, TensorFlow, etc.

Integrations

Optuna-Integration API reference is here.

• AllenNLP (example)
• BoTorch
• Catalyst (example)
• CatBoost (example)
• Chainer (example)
• ChainerMN (example)
• Dask (example)
• FastAI ( V1 (example), V2 (example))
• Keras (example)
• LightGBM (example)
• MLflow (example)
• MXNet (example)
• PyTorch (example)
• pycma
• scikit-optimize
• SHAP
• sklearn (example)
• skorch (example)
• TensorBoard (example)
• tf.keras (example)
• Weights & Biases (example)
• XGBoost (example)

Installation

Optuna-Integration is available at the Python Package Index and on Anaconda Cloud.

# PyPI
$ pip install optuna-integration

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

Optuna-Integration supports from Python 3.7 to Python 3.10.

Also, we also provide Optuna docker images on DockerHub.

Communication

• GitHub Discussions for questions.
• GitHub Issues for bug reports and feature requests.

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

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD (arXiv).