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.
- Hyperparameter Optimization via Raytune provides an example of parallelized hyperparameter optimization using Ax + Raytune.
- Multi-Task Modeling illustrates multi-task Bayesian Optimization on a constrained synthetic Hartmann6 problem.
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.