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Adaptive Experimentation Platform

Why Ax?Get startedTutorials

Key Features

Modular

Modular

Easy to plug in new algorithms and use the library across different domains.

Supports A/B Tests

Supports A/B Tests

Field experiments require a range of considerations beyond standard optimization problems.

Production-Ready

Production-Ready

Support for industry-grade experimentation and optimization management, including MySQL storage.

Get Started

  1. Install Ax:
    pip3 install ax-platform
  2. Run an optimization:


    >>> from ax import Client, RangeParameterConfig

    >>> client = Client()
    >>> client.configure_experiment(
          parameters=[
              RangeParameterConfig(
                  name="x1",
                  bounds=(-10.0, 10.0),
                  parameter_type="float",
              ),
              RangeParameterConfig(
                  name="x2",
                  bounds=(-10.0, 10.0),
                  parameter_type="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()
    {'x1': 1.02, 'x2': 2.97}  # true min is (1, 3)

References

Ax: A Platform for Adaptive Experimentation

@inproceedings{olson2025ax,
  title = {{Ax: A Platform for Adaptive Experimentation}},
  author = {
    Olson, Miles and Santorella, Elizabeth and Tiao, Louis C. and
    Cakmak, Sait and Eriksson, David and Garrard, Mia and Daulton, Sam and
    Balandat, Maximilian and Bakshy, Eytan and Kashtelyan, Elena 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
  },
  booktitle = {AutoML 2025 ABCD Track},
  year = {2025}
}