Optuna is an open-source hyperparameter optimization framework designed to automate the search for optimal hyperparameters in machine learning and AI models. It provides an efficient and flexible way to improve model performance using state-of-the-art optimization algorithms such as TPE, Bayesian optimization, and multi-objective strategies. Optuna integrates seamlessly with popular ML libraries like PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM, making it a versatile tool for both research and production environments.

Features of Optuna:

• Automates hyperparameter optimization with cutting-edge algorithms.
• Supports pruning of unpromising trials to save compute resources.
• Seamless integration with leading ML/DL frameworks.
• Enables multi-objective optimization for complex use cases.
• Scales to parallel and distributed environments with ease.
• Provides visualization tools and a dashboard for experiment tracking.
• Lightweight, flexible API designed for researchers and practitioners.
• Actively maintained open-source project under the MIT License.

To check the installed version of Optuna, run these commands in your environment:

$ sudo su
$ sudo apt update
$ cd /opt/optuna
$ source venv/bin/activate
$ python -c "import optuna; print(optuna.__version__)"

Disclaimer: Optuna is released under the MIT License and maintained by the Optuna community. Users are responsible for ensuring correct usage in their specific applications. The developers do not take responsibility for any consequences arising from its use. Always refer to the official documentation for the most accurate and up-to-date information.