Define-by-run API
Allows dynamic and Pythonic construction of hyperparameter search spaces using conditionals and loops during code execution.
Efficient Optimization Algorithms
Includes algorithms such as TPE, CMA-ES, GP-based Bayesian optimization, NSGA-II for multi-objective optimization, and pruning of unpromising trials.
Parallelization
Supports parallel execution of optimization trials across tens or hundreds of workers with minimal code changes.
Integrations
Compatible with machine learning libraries including PyTorch, TensorFlow, XGBoost, LightGBM, Keras, Catboost, MLflow, and Weights & Biases.
Optuna Dashboard
Provides real-time visualization of optimization history and hyperparameter importance through graphs and tables.