Ax is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.

Adaptive experimentation is the machine-learning guided process of iteratively exploring a (possibly infinite) parameter space in order to identify optimal configurations in a resource-efficient manner. Ax currently supports Bayesian optimization and bandit optimization as exploration strategies. Bayesian optimization in Ax is powered by BoTorch, a modern library for Bayesian optimization research built on PyTorch.

For full documentation and tutorials, see the Ax website

• Expressive API: Ax has an expressive API that can address many real-world optimization tasks. It handles complex search spaces, multiple objectives, constraints on both parameters and outcomes, and noisy observations. It supports suggesting multiple designs to evaluate in parallel (both synchronously and asynchronously) and the ability to early-stop evaluations.

• Strong performance out of the box: Ax abstracts away optimization details that are important but obscure, providing sensible defaults and enabling practitioners to leverage advanced techniques otherwise only accessible to optimization experts.

• State-of-the-art methods: Ax leverages state-of-the-art Bayesian optimization algorithms implemented in BoTorch, to deliver strong performance across a variety of problem classes.

• Flexible: Ax is highly configurable, allowing researchers to plug in novel optimization algorithms, models, and experimentation flows.

• Production ready: Ax offers automation and orchestration features as well as robust error handling for real-world deployment at scale.

To run a simple optimization loop in Ax (using the Booth response surface as the artificial evaluation function):

>>>fromaximportClient, RangeParameterConfig>>>client=Client() >>>client.configure_experiment( parameters=[ RangeParameterConfig( name="x1", bounds=(-10.0, 10.0), parameter_type=ParameterType.FLOAT, ), RangeParameterConfig( name="x2", bounds=(-10.0, 10.0), parameter_type=ParameterType.FLOAT, ), ], ) >>>client.configure_optimization(objective="-1 * booth") >>>for_inrange(20): >>>fortrial_index, parametersinclient.get_next_trials(max_trials=1).items(): >>>client.complete_trial( >>>trial_index=trial_index, >>>raw_data={>>>"booth": (parameters["x1"] +2*parameters["x2"] -7) **2>>>+ (2*parameters["x1"] +parameters["x2"] -5) **2>>> }, >>> ) >>>client.get_best_parameterization()

Ax requires Python 3.10 or newer. A full list of Ax's direct dependencies can be found in setup.py.

We recommend installing Ax via pip, even if using Conda environment:

pip install ax-platform

Installation will use Python wheels from PyPI, available for OSX, Linux, and Windows.

Note: Make sure the pip being used to install ax-platform is actually the one from the newly created Conda environment. If you're using a Unix-based OS, you can use which pip to check.

Ax can be installed with additional dependencies, which are not included in the default installation. For example, in order to use Ax within a Jupyter notebook, install Ax with the notebook extra:

pip install "ax-platform[notebook]"

Extras for using Ax with MySQL storage (mysql), for running Ax's tutorial's locally (tutorials), and for installing all dependencies necessary for developing Ax (dev) are also available.

You can install the latest (bleeding edge) version from GitHub using pip.

The bleeding edge for Ax depends on bleeding edge versions of BoTorch and GPyTorch. We therefore recommend installing those from Github, as well as setting the following environment variables to allow the Ax to use the latest version of both BoTorch and GPyTorch.

export ALLOW_LATEST_GPYTORCH_LINOP=true export ALLOW_BOTORCH_LATEST=true pip install git+https://github.com/cornellius-gp/gpytorch.git pip install git+https://github.com/pytorch/botorch.git pip install 'git+https://github.com/facebook/Ax.git#egg=ax-platform'

Please open an issue on our issues page with any questions, feature requests or bug reports! If posting a bug report, please include a minimal reproducible example (as a code snippet) that we can use to reproduce and debug the problem you encountered.

See the CONTRIBUTING file for how to help out.

When contributing to Ax, we recommend cloning the repository and installing all optional dependencies:

pip install git+https://github.com/cornellius-gp/linear_operator.git pip install git+https://github.com/cornellius-gp/gpytorch.git export ALLOW_LATEST_GPYTORCH_LINOP=true pip install git+https://github.com/pytorch/botorch.git export ALLOW_BOTORCH_LATEST=true git clone https://github.com/facebook/ax.git --depth 1 cd ax pip install -e .[tutorial] 

See recommendation for installing PyTorch for MacOS users above.

The above example limits the cloned directory size via the --depth argument to git clone. If you require the entire commit history you may remove this argument.

If you use Ax, please cite the following paper:

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.

@inproceedings{olson2025ax, title ={{Ax: A Platform for Adaptive Experimentation}}, author ={Olson, Miles and Santorella, Elizabeth and Tiao, Louis C. and Cakmak, Sait and Garrard, Mia and Daulton, Samuel and Lin, Zhiyuan Jerry and Ament, Sebastian and Beckerman, Bernard and Onofrey, Eric and Igusti, Paschal and Lara, Cristian and Letham, Benjamin and Cardoso, Cesar and Shen, Shiyun Sunny and Lin, Andy Chenyuan and Grange, Matthew and Kashtelyan, Elena and Eriksson, David and Balandat, Maximilian and Bakshy, Eytan. }, booktitle ={AutoML 2025 ABCD Track}, year ={2025} } 

Ax is licensed under the MIT license.