Welcome to Ax Tutorials

Here you can learn about the structure and applications of Ax from examples.

Our 3 API tutorials: Loop, Service, and Developer — are a good place to start. Each tutorial showcases optimization on a constrained Hartmann6 problem, with the Loop API being the simplest to use and the Developer API being the most customizable.

Further, our Bayesian Optimization tutorials include:

• Hyperparameter Optimization for PyTorch  provides an example of hyperparameter optimization with Ax and integration with an external ML library.

• Multi-Task Modeling  illustrates multi-task Bayesian Optimization on a constrained synthetic Hartmann6 problem.

• Benchmarking Suite  demonstrates how to use the Ax benchmarking suite to compare Bayesian Optimization algorithm performances and generate a comparative report with visualizations.

For experiments done in a real-life setting, refer to our field experiments tutorials:

• Bandit Optimization  shows how Thompson Sampling can be used to intelligently reallocate resources to well-performing configurations in real-time.

• Human-in-the-Loop Optimization  walks through manually influencing the course of optimization in real-time.

Finally, we explore the different components available in Ax in more detail.

Building Blocks of Ax  examines the architecture of Ax and the experimentation/optimization process.