Adaptive Experimentation Platform

Key Features

Get Started

1. Install Ax:
   • Linux
   • Mac
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

@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}
}