Ax is a platform for sequential experimentation. It relies on BoTorch for implementing Bayesian Optimization algorithms, but provides higher-level APIs that make it easy and convenient to specify problems, visualize results, and benchmark new algorithms. It also comes with powerful metadata management, storage of results, and deployment-related APIs. Ax makes it convenient to use BoTorch in most standard Bayesian Optimization settings. Simply put, BoTorch provides the building blocks for the engine, while Ax makes it easy to drive the car.
Ax provides a
that is a sensible default for modeling and optimization which can be customized
by specifying and passing in bespoke model constructors, acquisition functions,
and optimization strategies.
This model bridge utilizes a number of built-in transformations, such as
normalizing input spaces and outputs to ensure reasonable fitting of GPs.
See the Ax Docs for more
When to use BoTorch through Ax
If it's simple to use BoTorch through Ax for your problem, then use Ax. It dramatically reduces the amount of bookkeeping one needs to do as a Bayesian optimization researcher, such as keeping track of results, and transforming inputs and outputs to ranges that will ensure sensible handling in (G)PyTorch. The functionality provided by Ax should apply to most standard use cases.
Even if you want something more custom, it may still be easier to use the Ax framework. For instance, say you want to experiment with using a different kind of surrogate model, or a new type of acquisition function, but leave the rest of the the Bayesian Optimization loop untouched. It is then straightforward to plug your custom BoTorch model or acquisition function into Ax to take advantage of Ax's various loop control APIs, as well as its powerful automated metadata management, data storage, etc. See the Using a custom BoTorch model in Ax tutorial for more on how to do this.
When not to use Ax
If you're working in a non-standard setting, such as structured feature or design spaces, or where the model fitting process requires interactive work, then using Ax may not be the best solution for you. In such a situation, you might be better off writing your own full Bayesian Optimization loop in BoTorch. The q-Noisy Constrained EI tutorial and variational auto-encoder tutorial give examples of how this can be done.
You may also consider working purely in BoTorch if you want to be able to understand and control every single aspect of your BayesOpt loop - Ax's simplicity necessarily means that certain powerful BoTorch features will not be fully exposed to the user.
Prototyping in BoTorch
The modular design of BoTorch makes it very easy to prototype and debug individual components in an interactive fashion in a Jupyter notebook just like you might do with PyTorch. Once these building blocks have been designed and tested, they can easily be integrated into Ax.