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APIs

The modular design of Ax enables three different usage modes, with different balances of structure to flexibility and reproducibility. Navigate to the "Tutorials" page for an in-depth walk-through of each API and usage mode.

NOTE: We recommend the Service API for the vast majority of use cases. This API provides an ideal balance of flexibility and simplicity for most users, and we are in the process of consolidating Ax usage around it more formally.

From most lightweight to fullest functionality, our APIs are:

  • Loop API (tutorial) is intended for synchronous optimization loops, where trials can be evaluated right away. With this API, optimization can be executed in a single call and experiment introspection is available once optimization is complete. Use this API only for the simplest use cases where running a single trial is fast and only one trial should be running at a time.
  • [RECOMMENDED] Service API (tutorial) can be used as a lightweight service for parameter-tuning applications where trials might be evaluated in parallel and data is available asynchronously (e.g. hyperparameter or simulation optimization). It requires little to no knowledge of Ax data structures and easily integrates with various schedulers. In this mode, Ax suggests one-arm trials to be evaluated by the client application, and expects them to be completed with metric data when available. This is our most popular API and a good place to start as a new user. Use it to leverage nearly full hyperparameter optimization functionality of Ax without the need to learn its architecture and how things work under the hood.
    • In both the Loop and the Service API, it is possible to configure the optimization algorithm via an Ax GenerationStrategy (tutorial), so use of Developer API is not required to control the optimization algorithm in Ax.
  • Developer API (tutorial) is for ad-hoc use by data scientists, machine learning engineers, and researchers. The developer API allows for a great deal of customization and introspection, and is recommended for those who plan to use Ax to optimize A/B tests. Using the developer API requires some knowledge of Ax architecture. Use this API if you are looking to perform field experiments with BatchTrial-s, customize or contribute to Ax, or leverage advanced functionality that is not exposed in other APIs.
    • While not an API, the Scheduler (tutorial) is an important and distinct use-case of the Ax Developer API. With the Scheduler, it's possible to run a configurable, managed closed-loop optimization where trials are deployed and polled in an async fashion and no human intervention/oversight is required until the experiment is complete. Use the Scheduler when you are looking to configure and start a full experiment that will need to interact with an external system to evaluate trials.

Here is a comparison of the three APIs in the simple case of evaluating the unconstrained synthetic Branin function:

from ax import optimize
from ax.utils.measurement.synthetic_functions import branin

best_parameters, values, experiment, model = optimize(
    parameters=[
        {
            "name": "x1",
            "type": "range",
            "bounds": [-5.0, 10.0],
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 10.0],
        },
    ],
    evaluation_function=lambda p: (branin(p["x1"], p["x2"]), 0.0),
    minimize=True,
)
from ax.service.ax_client import AxClient, ObjectiveProperties
from ax.utils.measurement.synthetic_functions import branin

ax_client = AxClient()
ax_client.create_experiment(
    name="branin_test_experiment",
    parameters=[
        {
            "name": "x1",
            "type": "range",
            "bounds": [-5.0, 10.0],
            "value_type": "float",
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 10.0],
        },
    ],
    objectives={"branin": ObjectiveProperties(minimize=True)},
)

for _ in range(15):
    parameters, trial_index = ax_client.get_next_trial()
    ax_client.complete_trial(trial_index=trial_index, raw_data=branin(parameters["x1"], parameters["x2"]))

best_parameters, metrics = ax_client.get_best_parameters()
from ax import *


class MockRunner(Runner):
    def run(self, trial):
        return {"name": str(trial.index)}


branin_search_space = SearchSpace(
    parameters=[
        RangeParameter(
            name="x1", parameter_type=ParameterType.FLOAT, lower=-5, upper=10
        ),
        RangeParameter(
            name="x2", parameter_type=ParameterType.FLOAT, lower=0, upper=15
        ),
    ]
)
exp = Experiment(
    name="test_branin",
    search_space=branin_search_space,
    optimization_config=OptimizationConfig(
        objective=Objective(
            metric=BraninMetric(name="branin", param_names=["x1", "x2"]),
            minimize=True,
        ),
    ),
    runner=MockRunner(),
)

sobol = Models.SOBOL(exp.search_space)
for i in range(5):
    trial = exp.new_trial(generator_run=sobol.gen(1))
    trial.run()
    trial.mark_completed()

best_arm = None
for i in range(15):
    gpei = Models.BOTORCH_MODULAR(experiment=exp, data=exp.fetch_data())
    generator_run = gpei.gen(1)
    best_arm, _ = generator_run.best_arm_predictions
    trial = exp.new_trial(generator_run=generator_run)
    trial.run()
    trial.mark_completed()

exp.fetch_data()
best_parameters = best_arm.parameters
from ax import *
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.service import Scheduler

# Full `Experiment` and `GenerationStrategy` instantiation
# omitted for brevity, refer to the "Tutorials" page for detail.
experiment = Experiment(...)
generation_strategy = GenerationStrategy(...)

scheduler = Scheduler(
    experiment=experiment,
    generation_strategy=generation_strategy,
    options=SchedulerOptions(),  # Configurations for how to run the experiment
)

scheduler.run_n_trials(100)  # Automate running 100 trials and reporting results
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