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width=\"300\" src=\"https://ax.dev/img/ax_logo_lockup.svg\" alt=\"Ax Logo\" /\u003e\n\n\u003chr/\u003e\n\n[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat\u0026labelColor=005BBB)](https://opensource.fb.com/support-ukraine)\n[![Build Status](https://img.shields.io/pypi/v/ax-platform.svg)](https://pypi.org/project/ax-platform/)\n[![Build Status](https://img.shields.io/pypi/pyversions/ax-platform.svg)](https://pypi.org/project/ax-platform/)\n[![Build Status](https://img.shields.io/pypi/wheel/ax-platform.svg)](https://pypi.org/project/ax-platform/)\n[![Build Status](https://github.com/facebook/Ax/workflows/Build%20and%20Test%20Workflow/badge.svg)](https://github.com/facebook/Ax/actions?query=workflow%3A%22Build+and+Test+Workflow%22)\n[![codecov](https://codecov.io/gh/facebook/Ax/branch/main/graph/badge.svg)](https://codecov.io/gh/facebook/Ax)\n[![Build Status](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\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\n[BoTorch](https://github.com/facebookexternal/botorch), a modern library for\nBayesian optimization research built on PyTorch.\n\nFor full documentation and tutorials, see the [Ax website](https://ax.dev)\n\n## Why Ax?\n\n- **Expressive API**: Ax has an expressive API that can address many real-world\n  optimization tasks. It handles complex search spaces, multiple objectives,\n  constraints on both parameters and outcomes, and noisy observations. It\n  supports suggesting multiple designs to evaluate in parallel (both\n  synchronously and asynchronously) and the ability to early-stop evaluations.\n\n- **Strong performance out of the box**: Ax abstracts away optimization details\n  that are important but obscure, providing sensible defaults and enabling\n  practitioners to leverage advanced techniques otherwise only accessible to\n  optimization experts.\n\n- **State-of-the-art methods**: Ax leverages state-of-the-art Bayesian\n  optimization algorithms implemented in [BoTorch](https://botorch.org/), to\n  deliver strong performance across a variety of problem classes.\n\n- **Flexible:** Ax is highly configurable, allowing researchers to plug in novel\n  optimization algorithms, models, and experimentation flows.\n\n- **Production ready:** Ax offers automation and orchestration features as well\n  as robust error handling for real-world deployment at scale.\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\u003e\u003e\u003e from ax import Client, RangeParameterConfig\n\n\u003e\u003e\u003e client = Client()\n\u003e\u003e\u003e client.configure_experiment(\n      parameters=[\n          RangeParameterConfig(\n              name=\"x1\",\n              bounds=(-10.0, 10.0),\n              parameter_type=ParameterType.FLOAT,\n          ),\n          RangeParameterConfig(\n              name=\"x2\",\n              bounds=(-10.0, 10.0),\n              parameter_type=ParameterType.FLOAT,\n          ),\n      ],\n)\n\u003e\u003e\u003e client.configure_optimization(objective=\"-1 * booth\")\n\n\u003e\u003e\u003e for _ in range(20):\n\u003e\u003e\u003e     for trial_index, parameters in client.get_next_trials(max_trials=1).items():\n\u003e\u003e\u003e         client.complete_trial(\n\u003e\u003e\u003e             trial_index=trial_index,\n\u003e\u003e\u003e             raw_data={\n\u003e\u003e\u003e                 \"booth\": (parameters[\"x1\"] + 2 * parameters[\"x2\"] - 7) ** 2\n\u003e\u003e\u003e                 + (2 * parameters[\"x1\"] + parameters[\"x2\"] - 5) ** 2\n\u003e\u003e\u003e             },\n\u003e\u003e\u003e         )\n\n\u003e\u003e\u003e client.get_best_parameterization()\n```\n\n## Installation\n\nAx requires Python 3.11 or newer. A full list of Ax's direct dependencies can be\nfound in [pyproject.toml](https://github.com/facebook/Ax/blob/main/pyproject.toml).\n\nWe recommend installing Ax via pip, even if using Conda environment:\n\n```shell\npip install ax-platform\n```\n\nInstallation will use Python wheels from PyPI, available for\n[OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files).\n\n_Note_: Make sure the `pip` being used to install `ax-platform` is actually the\none from the newly created Conda environment. If you're using a Unix-based OS,\nyou can use `which pip` to check.\n\n### Installing with Extras\n\nAx can be installed with additional dependencies, which are not included in the\ndefault installation. For example, in order to use Ax within a Jupyter notebook,\ninstall Ax with the `notebook` extra:\n\n```shell\npip install \"ax-platform[notebook]\"\n```\n\nExtras for using Ax with MySQL storage (`mysql`), for running Ax's tutorial's\nlocally (`tutorials`), and for installing all dependencies necessary for\ndeveloping Ax (`dev`) are also available.\n\n## Install Ax from source\n\nYou can install the latest (bleeding edge) version from GitHub using `pip`.\n\nThe bleeding edge for Ax depends on bleeding edge versions of BoTorch and\nGPyTorch. We therefore recommend installing those from Github as well.\n\n```shell\npip install git+https://github.com/cornellius-gp/gpytorch.git\npip install git+https://github.com/pytorch/botorch.git\n\npip install 'git+https://github.com/facebook/Ax.git#egg=ax-platform'\n```\n\n## Join the Ax Community\n\n### Getting help\n\nPlease open an issue on our [issues page](https://github.com/facebook/Ax/issues)\nwith any questions, feature requests or bug reports! If posting a bug report,\nplease include a minimal reproducible example (as a code snippet) that we can\nuse to reproduce and debug the problem you encountered.\n\n### Contributing\n\nSee the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.\n\nWhen contributing to Ax, we recommend cloning the\n[repository](https://github.com/facebook/Ax) and installing all optional\ndependencies:\n\n```\npip install git+https://github.com/cornellius-gp/linear_operator.git\npip install git+https://github.com/cornellius-gp/gpytorch.git\npip install git+https://github.com/pytorch/botorch.git\ngit clone https://github.com/facebook/ax.git --depth 1\ncd ax\npip install -e .[tutorial]\n```\n\nSee recommendation for installing PyTorch for MacOS users above.\n\nThe above example limits the cloned directory size via the\n[`--depth`](https://git-scm.com/docs/git-clone#Documentation/git-clone.txt---depthltdepthgt)\nargument to `git clone`. If you require the entire commit history you may remove\nthis argument.\n\n## Citing Ax\n\nIf you use Ax, please cite the following paper:\n\u003e [M. Olson, E. Santorella, L. C. Tiao, S. Cakmak, D. Eriksson, M. Garrard, S. Daulton, M. Balandat,  E. Bakshy, E. Kashtelyan, Z. J. Lin, S. Ament, B. Beckerman, E. Onofrey, P. Igusti, C. Lara, B. Letham, C. Cardoso, S. S. Shen, A. C. Lin, and M. Grange. Ax: A platform for Adaptive Experimentation. In AutoML 2025 ABCD Track, 2025.](https://openreview.net/forum?id=U1f6wHtG1g)\n\n```\n@inproceedings{olson2025ax,\n  title = {{Ax: A Platform for Adaptive Experimentation}},\n  author = {\n    Olson, Miles and Santorella, Elizabeth and Tiao, Louis C. and\n    Cakmak, Sait and Garrard, Mia and Daulton, Samuel and\n    Lin, Zhiyuan Jerry  and Ament, Sebastian and Beckerman, Bernard and\n    Onofrey, Eric and Igusti, Paschal and Lara, Cristian and\n    Letham, Benjamin and Cardoso, Cesar and Shen, Shiyun Sunny and\n    Lin, Andy Chenyuan and Grange, Matthew and Kashtelyan, Elena and\n    Eriksson, David and Balandat, Maximilian and Bakshy, Eytan.\n  },\n  booktitle = {AutoML 2025 ABCD Track},\n  year = {2025}\n}\n```\n\n## License\n\nAx is licensed under the [MIT license](./LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebook%2Fax","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffacebook%2Fax","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebook%2Fax/lists"}