optuna
A hyperparameter optimization framework
Decision gist · record as of 2026-08-14
Yes. Optuna is a mature, actively maintained framework (Production/Stable, 14662 stars, recent releases) with low install friction, no known vulnerabilities, and permissive MIT licensing. It is well-suited for anyone doing systematic hyperparameter tuning in machine learning and offers a more flexible, Pythonic alternative to grid or random search.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or newer.
- Low install friction with a pure-Python wheel and seven common runtime dependencies (alembic, colorlog, numpy, packaging, sqlalchemy, tqdm, PyYAML).
- Actively maintained with a recent release 74 days ago and 14662 repository stars.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), allowing unrestricted use, modification, and distribution in both open-source and commercial projects.
last release 2026-06-01 (74 days) · last repo commit 2026-08-13 · 14,662 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 17,394,263 downloads/mo, #1,121 on PyPI
Alternatives
Verify before relying
pip install optuna
import optuna
def objective(trial):
x = trial.suggest_float('x', -10, 10)
return x**2
study = optuna.create_study()
study.optimize(objective, n_trials=100)- Whether the package's distributed optimization scales to 'tens or hundreds of workers' as claimed without additional infrastructure setup.
- Performance characteristics and convergence speed compared to other hyperparameter optimization frameworks in typical ML workflows.
What it is and what it does
Optuna is a framework for automating hyperparameter optimization in machine learning. It uses a define-by-run API where you write an objective function that suggests hyperparameter values and returns a metric to minimize or maximize; Optuna then orchestrates multiple trials to find the best configuration. The framework supports dynamic search spaces (conditionals and loops), efficient sampling algorithms including pruning of unpromising trials, and easy parallelization across workers.
The package is designed for practitioners who need to tune models systematically without manually scripting grid or random search. It integrates with popular ML libraries (scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, and others) and provides visualization tools to inspect optimization history. Storage is pluggable (SQLite, PostgreSQL, etc. via SQLAlchemy), making it suitable for both single-machine experiments and distributed optimization studies.
Use it for
- Tune scikit-learn model hyperparameters (e.g., SVM regularization, random forest depth) by defining an objective function that trains and evaluates the model.
- Optimize neural network architectures and training hyperparameters in PyTorch or TensorFlow with pruning to stop unpromising trials early.
- Run multi-objective optimization to balance competing goals (e.g., model accuracy vs. inference latency) across multiple workers.
- Store and visualize optimization histories in a persistent database for reproducibility and post-hoc analysis via the Optuna Dashboard.
- Implement constrained optimization where hyperparameter choices depend on earlier trial results using conditional suggest calls.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Optuna is a mature, actively maintained framework (Production/Stable, 14662 stars, recent releases) with low install friction, no known vulnerabilities, and permissive MIT licensing. It is well-suited for anyone doing systematic hyperparameter tuning in machine learning and offers a more flexible, Pythonic alternative to grid or random search.
Install
optuna on PyPI
Before you install
Low install friction with a pure-Python wheel and seven common runtime dependencies (alembic, colorlog, numpy, packaging, sqlalchemy, tqdm, PyYAML). Actively maintained with a recent release 74 days ago and 14662 repository stars.
Requires Python 3.9 or newer.
License in practice
Licensed under MIT (permissive), allowing unrestricted use, modification, and distribution in both open-source and commercial projects.
Quickstart
pip install optuna
import optuna
def objective(trial):
x = trial.suggest_float('x', -10, 10)
return x**2
study = optuna.create_study()
study.optimize(objective, n_trials=100)Verify before relying
- Whether the package's distributed optimization scales to 'tens or hundreds of workers' as claimed without additional infrastructure setup.
- Performance characteristics and convergence speed compared to other hyperparameter optimization frameworks in typical ML workflows.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesalembiccolorlognumpypackagingsqlalchemytqdmPyYAML |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 17,394,263 / month, #1,121 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: optuna-4.9.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “parameter tuning automation”
- optunaOptuna is a hyperparameter optimization framework that automates the…
- ax-platformAx is a platform for adaptive experimentation that uses…
- pyannote-pipelineDefines and optimizes tunable hyperparameter pipelines using…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also hydra-optuna-sweeper · hyperopt · keras-tuner · optuna-dashboard · optuna-integration · cmaes · ConfigSpace · moocore · pyannote-pipeline · FLAML