{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/6"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/8"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Provides integration modules connecting Optuna hyperparameter optimization with third-party ML frameworks like PyTorch, scikit-learn, TensorFlow, XGBoost, LightGBM, and others.","skillfed_tags":["hyperparameter-optimization","ml-framework-integration","automl"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"optuna-integration","links":{"html":"https://skillfed.io/packages/optuna-integration","md":"https://skillfed.io/packages/optuna-integration.md","pypi":"https://pypi.org/project/optuna-integration/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":null,"license_treatment":"permissive","name":"optuna-integration","python_support":"supports_current","summary":"Integration libraries of Optuna."},"popularity":{"monthly_downloads":961291,"position":4633,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.9.0"}