Optuna is an open-source hyperparameter optimization framework designed to automate the search for optimal parameters in machine learning and deep learning models. It provides an efficient and flexible interface for defining optimization objectives, supports cutting-edge algorithms like TPE and CMA-ES, and offers pruning mechanisms to accelerate training by discarding unpromising trials early. Optuna integrates seamlessly with popular ML/DL libraries and can scale from single-machine setups to distributed clusters.

Features of Optuna:

• Automates hyperparameter optimization for ML/DL models.
• Supports state-of-the-art optimization algorithms such as TPE and CMA-ES.
• Includes pruning strategies for efficient resource usage.
• Integrates with libraries like PyTorch, TensorFlow, XGBoost, LightGBM, and Scikit-learn.
• Provides built-in visualization tools for tracking and analyzing trials.
• Scales from local execution to distributed, parallel optimization.
• Open-source and actively maintained by a large research and engineering community.

To check the installed version of Optuna on Ubuntu 22.04, run these commands in your environment:

$ sudo su
$ sudo apt update
$ cd /opt/optuna
$ source venv/bin/activate
$ python3 -c "import optuna; print(optuna.__version__)"

Disclaimer: Optuna is released under the MIT License and is 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.