---
id: optuna-integration
version: "4.9.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# optuna-integration — Integration libraries of Optuna.

License: permissive · Maintenance: active · Downloads: 961.3K/mo

## 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 above — verify before relying.

Provides integration modules connecting Optuna hyperparameter optimization with third-party ML frameworks like PyTorch, scikit-learn, TensorFlow, XGBoost, LightGBM, and others.

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

pip install optuna-integration
uv add optuna-integration
poetry add optuna-integration

## Installing optuna-integration

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.

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-learn

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].

Verify 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: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 961.3K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

optuna integration modules, hyperparameter optimization pytorch, optuna sklearn integration, optuna xgboost lightgbm, optuna keras tensorflow, optuna pruning callbacks, optuna framework connectors, hyperparameter-optimization, ml-framework-integration, automl

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