Modular
Easy to plug in new algorithms and use the library across different domains.
Supports A/B Tests
Field experiments require a range of considerations beyond standard optimization problems.
Production-Ready
Support for industry-grade experimentation and optimization management, including MySQL storage.
conda install pytorch torchvision -c pytorch # OSX only
pip3 install ax-platform # all systems
>>> from ax import optimize
>>> best_parameters, best_values, experiment, model = optimize(
parameters=[
{
"name": "x1",
"type": "range",
"bounds": [-10.0, 10.0],
},
{
"name": "x2",
"type": "range",
"bounds": [-10.0, 10.0],
},
],
# Booth function
evaluation_function=lambda p: (p["x1"] + 2*p["x2"] - 7)**2 + (2*p["x1"] + p["x2"] - 5)**2,
minimize=True,
)
>>> best_parameters
{'x1': 1.02, 'x2': 2.97} # true min is (1, 3)