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, both for setting up the experiment and visualizing results.

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

Visualizations  illustrates the different plots available to view and understand your results.