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Version: 1.1

Ax Sweeper plugin

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This plugin provides a mechanism for Hydra applications to use the Adaptive Experimentation Platform, aka Ax. Ax can optimize any experiment - machine learning experiments, A/B tests, and simulations.


pip install hydra-ax-sweeper --upgrade


Once installed, add hydra/sweeper=ax to your command line. Alternatively, override hydra/sweeper in your config:

- override hydra/sweeper: ax

We include an example of how to use this plugin. The file

implements the Rosenbrock function (aka Banana function). The return value of the function should be the value that we want to optimize.

To compute the best parameters for the Banana function, clone the code and run the following command in the plugins/hydra_ax_sweeper directory:

python example/ -m 'banana.x=int(interval(-5, 5))' 'banana.y=interval(-5, 10.1)'

The output of a run looks like:

[HYDRA] AxSweeper is optimizing the following parameters:
banana.x: range=[-5, 5]
banana.y: range=[-5.0, 10.1]
ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
[HYDRA] AxSweeper is launching 5 jobs
[HYDRA] Launching 5 jobs locally
[HYDRA] #0 : banana.x=2 banana.y=-0.988
[__main__][INFO] - Banana_Function(x=2, y=-0.988)=2488.883
[HYDRA] #1 : banana.x=-1 banana.y=7.701
[__main__][INFO] - Banana_Function(x=-1, y=7.701)=4493.987
[HYDRA] #2 : banana.x=-1 banana.y=-3.901
[__main__][INFO] - Banana_Function(x=-1, y=-3.901)=2406.259
[HYDRA] #3 : banana.x=-1 banana.y=0.209
[__main__][INFO] - Banana_Function(x=-1, y=0.209)=66.639
[HYDRA] #4 : banana.x=4 banana.y=-4.557
[__main__][INFO] - Banana_Function(x=4, y=-4.557)=42270.006
[HYDRA] New best value: 66.639, best parameters: {'banana.x': -1, 'banana.y': 0.209}

In this example, we set the range of x parameter as an integer in the interval [-5, 5] and the range of y parameter as a float in the interval [-5, 10.1]. Note that in the case of x, we used int(interval(...)) and hence only integers are sampled. In the case of y, we used interval(...) which refers to a floating-point interval. Other supported formats are fixed parameters (e.g. banana.x=5.0), choice parameters (eg banana.x=choice(1,2,3)) and range (eg banana.x=range(1, 10)). Note that interval, choice etc. are functions provided by Hydra, and you can read more about them here. An important thing to remember is, use interval when we want Ax to sample values from an interval. RangeParameter in Ax is equivalent to interval in Hydra. Remember to use int(interval(...)) if you want to sample only integer points from the interval. range can be used as an alternate way of specifying choice parameters. For example python example/ -m banana.x=choice(1, 2, 3, 4) is equivalent to python example/ -m banana.x=range(1, 5).

The values of the x and y parameters can also be set using the config file plugins/hydra_ax_sweeper/example/conf/config.yaml. For instance, the configuration corresponding to the commandline arguments is as follows:

type: range
bounds: [-5, 5]
type: range
bounds: [-5, 10.1]

In general, the plugin supports setting all the Ax supported Parameters in the config. According to the Ax documentation, the required elements in the config are:

  • name - Name of the parameter. It is of type string.
  • type - Type of the parameter. It can take the following values: range, fixed, or choice.
  • bounds - Required only for the range parameters. It should be a list of two values, with the lower bound first.
  • values - Required only for the choice parameters. It should be a list of values.
  • value - Required only for the fixed parameters. It should be a single value.

Note that if you want to sample integers in the range -5 to 5, you need to specify the range as int(interval(-5, 5)) (in the command line) or [-5, 5] (in config). If you want to sample floats in range -5 to 5, you need to specify the range as interval(-5, 5) (in the command line) or [-5.0, 5.0] (in config).

The Ax Sweeper assumes the optimized function is a noisy function with unknown measurement uncertainty. This can be changed by overriding the is_noisy parameter to False, which specifies that each measurement is exact, i.e., each measurement has a measurement uncertainty of zero.

If measurement uncertainty is known or can be estimated (e.g., via a heuristic or via the standard error of the mean of repeated measurements), the measurement function can return the tuple (measurement_value, measurement_uncertainty) instead of a scalar value.

The parameters for the optimization process can also be set in the config file. Specifying the Ax config is optional. You can discover the Ax Sweeper parameters with:

$ python hydra/sweeper=ax --cfg hydra -p hydra.sweeper
# @package hydra.sweeper
_target_: hydra_plugins.hydra_ax_sweeper.ax_sweeper.AxSweeper
max_batch_size: null
max_trials: 10
minimize: true
max_epochs_without_improvement: 10
epsilon: 1.0e-05
name: null
objective_name: objective
minimize: true
parameter_constraints: null
outcome_constraints: null
status_quo: null
verbose_logging: false
random_seed: null
is_noisy: true
params: {}

There are several standard approaches for configuring plugins. Check this page for more information.

Last updated on by Jasha10