This foundation course helps you get started with running your own hyperparameter tuning experiments efficiently using Ray Tune.
In this module, you’ll learn how to use Ray Tune to run distributed hyperparameter tuning, starting from a baseline PyTorch MNIST training loop and scaling experiments across available GPUs. By the end, you’ll be able to define a Tune training function, specify a search space, and execute parallel trials to find better-performing model configurations.
