Adaptive Experimentation Platform
Easy to plug in new algorithms and use the library across different domains.
Field experiments require a range of considerations beyond standard optimization problems.
Support for industry-grade experimentation and optimization management, including MySQL storage.
pip3 install ax-platform
conda install pytorch -c pytorch
pip3 install ax-platform
>>> 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)
@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}
}