Run Ray on TPU, Part 1: The foundations

Ray, a distributed-computing framework, now supports Google Cloud TPUs as a first-class accelerator, enhancing its capability to schedule tasks across clusters using TPUs similarly to GPUs. This integration is facilitated by Google's Kubernetes Engine (GKE), which provisions TPU slices—fixed groups of interconnected TPU chips—and labels them for Ray's use. With this setup, Ray Core can reserve entire TPU slices atomically, ensuring that distributed tasks are executed efficiently without manual placement coding. The public API for TPU support in Ray, though marked as alpha, allows developers to leverage slice placement through a single function call, making it easier to deploy and manage AI workloads. This new capability promises seamless scaling of Python applications on TPUs using familiar Ray AI libraries, and further exploration in Part 2 will demonstrate practical applications like serving large language models and training with JaxTrainer.

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