#4545th of 56 in ML frameworks and libraries
optuna
Python hyperparameter optimization library with define-by-run search spaces and pruning
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- Stars
- 14.9k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
Optuna runs studies made of trials: you write an objective function in Python, declare hyperparameters inside it with suggest_* calls, and the library samples values and prunes unpromising trials. Search spaces can use conditionals and loops, and studies can be stored in a database such as SQLite and run across many workers. Companion projects cover a web dashboard (optuna-dashboard), a plugin registry (OptunaHub) and an experimental Rust port (Rustuna).
Who it is for: ML engineers tuning model hyperparameters in Python
Strengths
- Search spaces are plain Python, so conditionals and loops work inside the objective
- Pruning of unpromising trials, multi-objective and constrained optimization are supported
- Installs from pip or conda with few requirements; MIT license
- Studies persist to a database like SQLite and can run across many workers
Weaknesses
- Web dashboard is a separate package (optuna-dashboard), not part of core
- Requires Python 3.10 or newer
- Rust port Rustuna is experimental and lacks some Optuna features and APIs
- Some OptunaHub samplers need extra dependencies such as torch, scipy and cmaes
What it needs
- no GPU
- Needs Python 3.10+, SQLite or other database (optional storage)
- Models: scikit-learn
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