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"description": "![\"Ax](\"website/static/img/ax_logo_lockup.svg\")
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\n\n[](https://pypi.org/project/ax-platform/)\n[](https://pypi.org/project/ax-platform/)\n[](https://pypi.org/project/ax-platform/)\n[](https://travis-ci.com/facebook/Ax)\n[](https://codecov.io/gh/facebook/Ax)\n[](LICENSE.md)\n\nAx is an accessible, general-purpose platform for understanding, managing,\ndeploying, and automating adaptive experiments.\n\nAdaptive experimentation is the machine-learning guided process of iteratively\nexploring a (possibly infinite) parameter space in order to identify optimal\nconfigurations in a resource-efficient manner. Ax currently supports Bayesian\noptimization and bandit optimization as exploration strategies. Bayesian\noptimization in Ax is powered by [BoTorch](https://github.com/facebookexternal/botorch),\na modern library for Bayesian optimization research built on PyTorch.\n\nFor full documentation and tutorials, see the [Ax website](https://ax.dev)\n\n## Why Ax?\n\n* **Versatility**: Ax supports different kinds of experiments, from dynamic ML-assisted A/B testing, to hyperparameter optimization in machine learning.\n* **Customization**: Ax makes it easy to add new modeling and decision algorithms, enabling research and development with minimal overhead.\n* **Production-completeness**: Ax comes with storage integration and ability to fully save and reload experiments.\n* **Support for multi-modal and constrained experimentation**: Ax allows for running and combining multiple experiments (e.g. simulation with a real-world \"online\" A/B test) and for constrained optimization (e.g. improving classification accuracy without signifant increase in resource-utilization).\n* **Efficiency in high-noise setting**: Ax offers state-of-the-art algorithms specifically geared to noisy experiments, such as simulations with reinforcement-learning agents.\n* **Ease of use**: Ax includes 3 different APIs that strike different balances between lightweight structure and flexibility. Using the most concise Loop API, a whole optimization can be done in just one function call. The Service API integrates easily with external schedulers. The most elaborate Developer API affords full algorithm customization and experiment introspection.\n\n## Getting Started\n\nTo run a simple optimization loop in Ax (using the\n[Booth response surface](https://www.sfu.ca/~ssurjano/booth.html) as the\nartificial evaluation function):\n\n```python\n>>> from ax import optimize\n>>> best_parameters, best_values, experiment, model = optimize(\n parameters=[\n {\n \"name\": \"x1\",\n \"type\": \"range\",\n \"bounds\": [-10.0, 10.0],\n },\n {\n \"name\": \"x2\",\n \"type\": \"range\",\n \"bounds\": [-10.0, 10.0],\n },\n ],\n # Booth function\n evaluation_function=lambda p: (p[\"x1\"] + 2*p[\"x2\"] - 7)**2 + (2*p[\"x1\"] + p[\"x2\"] - 5)**2,\n minimize=True,\n )\n\n# best_parameters contains {'x1': 1.02, 'x2': 2.97}; the global min is (1, 3)\n```\n\n## Installation\n\n### Requirements\nYou need Python 3.6 or later to run Ax.\n\nThe required Python dependencies are:\n\n* [botorch](https://www.botorch.org)\n* jinja2\n* pandas\n* scipy\n* sklearn\n* plotly >=2.2.1\n\n### Stable Version\n\n#### Installing via pip\nWe recommend installing Ax via pip (even if using Conda environment):\n\n```\nconda install pytorch torchvision -c pytorch # OSX only (details below)\npip3 install ax-platform\n```\n\nInstallation will use Python wheels from PyPI, available for [OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files).\n\n*Recommendation for MacOS users*: PyTorch is a required dependency of BoTorch, and can be automatically installed via pip.\nHowever, **we recommend you [install PyTorch manually](https://pytorch.org/get-started/locally/#anaconda-1) before installing Ax, using the Anaconda package manager**.\nInstalling from Anaconda will link against MKL (a library that optimizes mathematical computation for Intel processors).\nThis will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, installing PyTorch from pip does not link against MKL.\n\nIf you need CUDA on MacOS, you will need to build PyTorch from source. Please consult the PyTorch installation instructions above.\n\n#### Optional Dependencies\n\nTo use Ax with a notebook environment, you will need Jupyter. Install it first:\n```\npip3 install jupyter\n```\n\nIf you want to store the experiments in MySQL, you will need SQLAlchemy:\n```\npip3 install SQLAlchemy\n```\n\n### Latest Version\n\n#### Installing from Git\n\nYou can install the latest (bleeding edge) version from Git:\n\n```\npip3 install git+ssh://git@github.com/facebook/Ax.git#egg=Ax\n```\n\nSee recommendation for installing PyTorch for MacOS users above.\n\nAt times, the bleeding edge for Ax can depend on bleeding edge versions of BoTorch (or GPyTorch). We therefore recommend installing those from Git as well:\n```\npip3 install git+https://github.com/cornellius-gp/gpytorch.git\npip3 install git+https://github.com/pytorch/botorch.git\n```\n\n#### Optional Dependencies\n\nIf using Ax in Jupyter notebooks:\n\n```\npip3 install git+ssh://git@github.com/facebook/Ax.git#egg=Ax[notebook]\n```\n\nIf storing Ax experiments via SQLAlchemy in MySQL or SQLite:\n```\npip3 install git+ssh://git@github.com/facebook/Ax.git#egg=Ax[mysql]\n```\n\n## Join the Ax Community\nSee the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.\n\nWhen contributing to Ax, we recommend cloning the [repository](https://github.com/facebook/Ax) and installing all optional dependencies:\n\n```\n# bleeding edge versions of GPyTorch + BoTorch are recommended\npip3 install git+https://github.com/cornellius-gp/gpytorch.git\npip3 install git+https://github.com/pytorch/botorch.git\n\ngit clone https://github.com/facebook/ax.git\ncd ax\npip3 install -e .[notebook,mysql,dev]\n```\n\nSee recommendation for installing PyTorch for MacOS users above.\n\n## License\n\nAx is licensed under the [MIT license](LICENSE.md).\n\n\n",
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