[](https://pypi.org/project/ax-platform/)
[](https://pypi.org/project/ax-platform/)
[](https://pypi.org/project/ax-platform/)
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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](https://github.com/facebookexternal/botorch), a modern library for
Bayesian optimization research built on PyTorch.
For full documentation and tutorials, see the [Ax website](https://ax.dev)
## Why Ax?
- **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](https://botorch.org/), 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.
## Getting Started
To run a simple optimization loop in Ax (using the
[Booth response surface](https://www.sfu.ca/~ssurjano/booth.html) as the
artificial evaluation function):
```python
>>> from ax import Client, 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 _ in range(20):
>>> for trial_index, parameters in client.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()
```
## Installation
Ax requires Python 3.11 or newer. A full list of Ax's direct dependencies can be
found in [pyproject.toml](https://github.com/facebook/Ax/blob/main/pyproject.toml).
We recommend installing Ax via pip, even if using Conda environment:
```shell
pip install ax-platform
```
Installation will use Python wheels from PyPI, available for
[OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files).
_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.
### Installing with Extras
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:
```shell
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.
To use fully Bayesian (SAAS) models -- e.g. `SAASBO` / `SAAS_MTGP`, or the API's
`method="quality"` generation strategy -- install the `fully_bayesian` extra,
which pulls in the optional JAX / NumPyro backend:
```shell
pip install "ax-platform[fully_bayesian]"
```
## Install Ax from source
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.
```shell
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'
```
## Join the Ax Community
### Getting help
Please open an issue on our [issues page](https://github.com/facebook/Ax/issues)
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.
### Contributing
See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.
When contributing to Ax, we recommend cloning the
[repository](https://github.com/facebook/Ax) 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
pip install git+https://github.com/pytorch/botorch.git
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`](https://git-scm.com/docs/git-clone#Documentation/git-clone.txt---depthltdepthgt)
argument to `git clone`. If you require the entire commit history you may remove
this argument.
## Citing Ax
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.](https://openreview.net/forum?id=U1f6wHtG1g)
```
@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}
}
```
## License
Ax is licensed under the [MIT license](./LICENSE).