optuna-integration
Integration libraries of Optuna.
Decision gist · record as of 2026-08-14
Yes. The package is production-stable, actively maintained, permissively licensed, and has low install friction. It is worth installing if you use Optuna with any of the supported ML frameworks (PyTorch, scikit-learn, TensorFlow, XGBoost, LightGBM, etc.), as it eliminates boilerplate integration code. Install only the base package if you need a specific integration; optional dependencies keep the footprint minimal.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Optional dependencies for specific integrations (e.g., botorch, lightgbm) must be installed separately via pip install optuna-integration[module_name].
- Low install friction with a single runtime dependency on optuna.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive) allows commercial and private use with minimal restrictions.
last release 2026-06-01 (74 days) · last repo commit 2026-08-05 · 76 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 961,291 downloads/mo, #4,633 on PyPI
Alternatives
Verify before relying
pip install optuna-integration
from optuna_integration.sklearn import OptunaSearchCV
# Use OptunaSearchCV for hyperparameter tuning with scikit-learn- Whether all integration modules are equally maintained or if some deprecated modules (marked with *) are actively supported.
- Performance characteristics and optimization quality compared to direct Optuna usage.
- Compatibility guarantees between optuna-integration version and specific third-party library versions.
What it is and what it does
optuna-integration extends Optuna's hyperparameter optimization capabilities by providing pre-built connectors and callbacks for popular ML frameworks. Instead of manually integrating Optuna with PyTorch, scikit-learn, TensorFlow, XGBoost, or other libraries, this package offers ready-to-use modules like pruning callbacks, tuners, and storage backends tailored to each framework's conventions.
The package is designed as a modular layer on top of Optuna. Core dependencies are minimal (just optuna itself), but each integration module has its own optional dependencies that you install on demand. For example, to use the LightGBM integration, you install optuna-integration[lightgbm]. This approach keeps the base installation lightweight while letting you pull in only what you need. It supports Python 3.9 through 3.13 and is actively maintained.
Use it for
- Tune scikit-learn model hyperparameters using OptunaSearchCV without writing custom Optuna trial code.
- Prune unpromising training runs in PyTorch, TensorFlow, or Keras models via framework-specific callbacks.
- Integrate Optuna with XGBoost or LightGBM for gradient boosting hyperparameter optimization.
- Distribute hyperparameter search across Dask clusters using DaskStorage.
- Track optimization experiments with MLflow or Weights & Biases callbacks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is production-stable, actively maintained, permissively licensed, and has low install friction. It is worth installing if you use Optuna with any of the supported ML frameworks (PyTorch, scikit-learn, TensorFlow, XGBoost, LightGBM, etc.), as it eliminates boilerplate integration code. Install only the base package if you need a specific integration; optional dependencies keep the footprint minimal.
Install
optuna-integration on PyPI
Before you install
Low install friction with a single runtime dependency on optuna. Active maintenance with a recent release 74 days ago and ongoing commits. Production-stable status.
Requires Python 3.9 or later. Optional dependencies for specific integrations (e.g., botorch, lightgbm) must be installed separately via pip install optuna-integration[module_name].
License in practice
MIT license (permissive) allows commercial and private use with minimal restrictions.
Quickstart
pip install optuna-integration
from optuna_integration.sklearn import OptunaSearchCV
# Use OptunaSearchCV for hyperparameter tuning with scikit-learnVerify before relying
- Whether all integration modules are equally maintained or if some deprecated modules (marked with *) are actively supported.
- Performance characteristics and optimization quality compared to direct Optuna usage.
- Compatibility guarantees between optuna-integration version and specific third-party library versions.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageoptuna |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 961,291 / month, #4,633 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 :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: optuna_integration-4.9.0-py3-none-any.whl
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See also optuna · hydra-optuna-sweeper · optuna-dashboard · botorch · pyannote-pipeline · openvino · keras-nightly · onnxmltools · spark-sklearn · keras-tuner