optuna/optuna

Health v2 · maintenance only

Adoption not part of the score

14713 stars · 1381 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Optuna is an open-source Python hyperparameter optimization framework with a define-by-run API for dynamically constructing search spaces. It supports state-of-the-art search algorithms, trial pruning, and easy parallel/distributed optimization across any ML framework.

Use cases

• tune hyperparameters of a machine learning model
• optimize neural network architecture search
• find best hyperparameters for LightGBM or XGBoost
• run distributed hyperparameter search in parallel
• prune unpromising training trials early
• multi-objective hyperparameter optimization

When to choose

• you need framework-agnostic hyperparameter tuning in Python
• you want dynamic, code-defined search spaces
• you need parallel or distributed optimization with minimal code changes
• you want pruning and modern samplers like TPE out of the box

When to avoid

• you need a non-Python environment
• you want a fully automated AutoML pipeline including feature engineering and model selection
• your project requires a GUI-first tuning tool

Facets

library · maturity active

machine-learning benchmarking data-science machine-learning deep-learning artificial-intelligence data-science python cross-platform hyperparameter-optimization automl bayesian-optimization define-by-run distributed-optimization pruning

Member repositories

For agents

markdown · JSON · MCP: product_card(name="optuna/optuna")