{"data":{"slug":"optuna-optuna","name":"optuna","tagline":"A hyperparameter optimization framework","github_url":"https://github.com/optuna/optuna","owner":"optuna","repo":"optuna","owner_avatar_url":"https://avatars.githubusercontent.com/u/57251745?v=4","primary_language":"Python","stars":14870,"forks":1406,"topics":["distributed","hyperparameter-optimization","machine-learning","parallel","python"],"archived":false,"github_pushed_at":"2026-09-30T07:26:24+00:00","maintenance_label":"Very active","stars_delta_30d":267,"url":"https://www.graphcanon.com/tools/optuna-optuna","markdown_url":"https://www.graphcanon.com/tools/optuna-optuna.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/optuna-optuna","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=optuna-optuna","description":"A hyperparameter optimization framework","homepage_url":"https://optuna.org","license":"MIT","open_issues":19,"watchers":124,"ai_summary":"Optuna is an open-source Python library used for automating the hyperparameter optimization process in machine learning tasks.","readme_excerpt":"## Installation\n\nOptuna is available at [the Python Package Index](https://pypi.org/project/optuna/) and on [Anaconda Cloud](https://anaconda.org/conda-forge/optuna).\n\n```bash\n\n---\n\n# Install AutoSampler dependencies (CPU only is sufficient for PyTorch)\n$ pip install cmaes scipy torch --extra-index-url https://download.pytorch.org/whl/cpu\n```\n\nYou can load registered module with `optunahub.load_module`.\n\n```python\nimport optuna\nimport optunahub\n\n\ndef objective(trial: optuna.Trial) -> float:\n    x = trial.suggest_float(\"x\", -5, 5)\n    y = trial.suggest_float(\"y\", -5, 5)\n    return x**2 + y**2\n\n\nmodule = optunahub.load_module(package=\"samplers/auto_sampler\")\n\n---\n\n## License\n\nMIT License (see [LICENSE](./LICENSE)).\n\nOptuna uses the codes from SciPy and fdlibm projects (see [LICENSE_THIRD_PARTY](./LICENSE_THIRD_PARTY)).","github_created_at":"2018-02-21T06:12:56+00:00","created_at":"2026-07-11T23:36:17.028343+00:00","updated_at":"2026-10-02T06:00:51.170296+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"distributed","name":"distributed"},{"slug":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"parallel","name":"parallel"},{"slug":"python","name":"python"}],"trust":{"provenance":{"is_fork":false,"github_id":122299416,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-10-02T06:00:50.395Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":1,"last_release_at":"2026-09-07T05:15:45Z","stars_delta_30d":267,"open_issues_delta_30d":3},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:36:20.447Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-10-02T06:00:50.881Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-10-02T06:00:50.881Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-10-02T06:00:50.881Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-10-02T06:00:50.881Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to streamline the hyperparameter tuning process for machine learning models built in Python.","For optimizing model performance where automation and a streamlined workflow are crucial."],"when_not_to_use":["If your project is not compatible with Python, as Optuna does not support other languages directly out of box.","Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs."],"source":"enrich:decision_facts","observed_at":"2026-07-16T20:54:37.936Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks."}]}}