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align=\"center\"\u003e\n  \u003cdiv\u003e\n    \u003ca href=\"https://gilad-rubin.gitbook.io/hypster\"\u003e\u003cstrong\u003eDocs\u003c/strong\u003e\u003c/a\u003e ·\n    \u003ca href=\"https://github.com/gilad-rubin/hypster/issues/new?template=bug_report.md\"\u003e\u003cstrong\u003eReport Bug\u003c/strong\u003e\u003c/a\u003e ·\n    \u003ca href=\"https://github.com/gilad-rubin/hypster/issues/new?template=feature_request.md\"\u003e\u003cstrong\u003eFeature Request\u003c/strong\u003e\u003c/a\u003e ·\n    \u003ca href=\"https://github.com/gilad-rubin/hypster/blob/hypster-v2/CHANGELOG.md\"\u003e\u003cstrong\u003eChangelog\u003c/strong\u003e\u003c/a\u003e\n  \u003c/div\u003e\n\u003c/div\u003e\n\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://deepwiki.com/gilad-rubin/hypster\" style=\"text-decoration:none;display:inline-block\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/chat%20with%20our%20AI%20docs-%E2%86%92-72A1FF?style=for-the-badge\u0026logo=readthedocs\u0026logoColor=white\"\n         alt=\"chat with our AI docs\" width=\"220\"\u003e\n  \u003c/a\u003e\n\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/gilad-rubin/hypster/actions/workflows/ci.yml\"\u003e\u003cimg src=\"https://github.com/gilad-rubin/hypster/actions/workflows/ci.yml/badge.svg\" alt=\"CI\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://codecov.io/gh/gilad-rubin/hypster\"\u003e\u003cimg src=\"https://codecov.io/gh/gilad-rubin/hypster/graph/badge.svg\" alt=\"codecov\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/hypster/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/hypster.svg\" alt=\"PyPI version\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/hypster/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/pyversions/hypster.svg\" alt=\"Python versions\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://deepwiki.com/gilad-rubin/hypster\"\u003e\u003cimg src=\"https://deepwiki.com/badge.svg\" alt=\"DeepWiki\"/\u003e\u003c/a\u003e\n  \u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-MIT-green.svg\" alt=\"License: MIT\"/\u003e\u003c/a\u003e\n  \u003ca href=\"https://codspeed.io/gilad-rubin/hypster\"\u003e\u003cimg src=\"https://img.shields.io/endpoint?url=https://codspeed.io/badge.json\" alt=\"CodSpeed\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cem\u003e\n    Hypster is a lightweight configuration framework for managing and \u003cb\u003eoptimizing AI \u0026 ML workflows\u003c/b\u003e\n  \u003c/em\u003e\n\u003c/p\u003e\n\n\u003e ⚠️ Hypster is in active development and not yet battle-tested in production.\n\u003e If you’re gaining value and want to promote it to production, please reach out!\n\n## Key Features\n\n- 🐍 **Pythonic API**: Intuitive \u0026 minimal syntax that feels natural to Python developers\n- 🪆 **Hierarchical, Conditional Configurations**: Support for nested and swappable configurations\n- 📐 **Type Safety**: Built-in type hints and validation\n- 🧪 **Hyperparameter Optimization Built-In**: Native, first-class optuna support\n\n## Installation\n\nYou can install Hypster using uv:\n\n```bash\nuv add hypster\n# optional HPO backend\nuv add 'hypster[optuna]'\n```\n\nOr using pip:\n\n```bash\npip install hypster\n```\n\n## Quick Start\n\nDefine a configuration function and instantiate it with overrides:\n\n```python\nfrom hypster import HP, instantiate\nfrom llm import LLM\n\ndef llm_config(hp: HP):\n    model_name = hp.select([\"gpt-4o-mini\", \"gpt-4o\"], name=\"model_name\")\n    temperature = hp.float(0.7, name=\"temperature\", min=0.0, max=1.0)\n    max_tokens = hp.int(256, name=\"max_tokens\", min=1, max=4096)\n    llm = LLM(model_name=model_name, temperature=temperature, max_tokens=max_tokens)\n    return llm\n\nllm = instantiate(llm_config, values={\"model_name\": \"gpt-4o-mini\", \"temperature\": 0.3})\nllm.invoke(\"How's your day going?\")\n```\n\n## HPO with Optuna\n\n```python\nimport optuna\nfrom hypster.hpo.types import HpoInt, HpoFloat, HpoCategorical\nfrom hypster.hpo.optuna import suggest_values\n\n\ndef objective(trial: optuna.Trial) -\u003e float:\n    values = suggest_values(trial, config=model_cfg)\n    model = instantiate(model_cfg, values=values)\n    X, y = make_classification(\n        n_samples=400, n_features=20, n_informative=10, random_state=42\n    )\n    return cross_val_score(model, X, y, cv=3, n_jobs=-1).mean()\n\nstudy = optuna.create_study(direction=\"maximize\")\nstudy.optimize(objective, n_trials=30)\n```\n\n## Inspiration\n\nHypster draws inspiration from Meta's [hydra](https://github.com/facebookresearch/hydra) and [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen) framework.\nThe API design is influenced by [Optuna's](https://github.com/optuna/optuna) \"define-by-run\" API.\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgilad-rubin%2Fhypster","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgilad-rubin%2Fhypster","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgilad-rubin%2Fhypster/lists"}