Optuna
Optuna is an open-source Python library designed for automatic hyperparameter optimization of machine learning models. It was introduced in 2018 by Preferred Networks and supports dynamic construction of search spaces during code execution through its define-by-run API. The framework efficiently searches large hyperparameter spaces, discards unpromising trials early, and supports parallelization across multiple threads or processes. Optuna integrates with popular machine learning libraries such as PyTorch, TensorFlow, XGBoost, LightGBM, Keras, and Catboost. It also provides a dashboard for real-time monitoring of optimization progress and hyperparameter importance.
Open-source Python library for automatic hyperparameter optimization with dynamic search space definition and parallelization support.
Hyperparameter Tuning for ML Models
Machine learning practitioners can use Optuna to automatically optimize hyperparameters of models built with frameworks like PyTorch or TensorFlow.
Multi-objective Optimization
Users can perform optimization involving multiple objectives using algorithms like NSGA-II supported by Optuna.
pip install optuna ensuring Python 3.9 or higher is used.study = optuna.create_study().study.optimize(objective, n_trials=100) to start the hyperparameter search.optuna-dashboard to monitor progress.