Run Ray on TPU, Part 2: Ray AI libraries

The second part of the guide on using Ray with TPUs explores the AI libraries necessary for building effective workloads, focusing on Ray Serve, Ray Data, and Ray Train. It highlights the importance of the topology field in Ray Serve for deploying multi-host models on TPU slices, which prevents deployment issues by ensuring tensor-parallel model workers remain on a shared ICI mesh. Ray Data is discussed in terms of its iter_jax_batches() function, which optimizes data flow into TPUs by delivering JAX-native batches directly, thus avoiding bottlenecks. Meanwhile, Ray Train is simplified by JaxTrainer, which manages distributed training with JAX on TPUs, removing the need for complex configuration and providing features like checkpointing and fault tolerance. The guide also mentions the availability of official TPU Docker images and enhanced Ray Dashboard metrics for monitoring TPU usage, emphasizing the ease of transitioning from GPU to TPU usage with Ray while teasing future enhancements in TPU support.

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