Run Ray on TPU, Part 1: The foundations
TL;DR: If you already scale Python with Ray on GPUs, your code can now run on TPU (Tensor Processing Unit, Google's AI accelerator chip) with fully supported official APIs you already know. The task-and-actor model, a JaxTrainer, the same Ray Serve deployment just pointed at TPUs orchestrated by Google Kubernetes Engine (GKE).As of Ray 2.55, Google Cloud TPUs are a first-class accelerator in Ray. This means that TPUs are now in Ray's release pipelines with official pre-built images and support across the core libraries, instead of the old "experimental" path where you built your own containers and leaned on community help.
- ▪TL;DR: If you already scale Python with Ray on GPUs, your code can now run on TPU (Tensor Processing Unit, Google's AI accelerator chip) with fully supported official APIs you already know.
- ▪The task-and-actor model, a JaxTrainer, the same Ray Serve deployment just pointed at TPUs orchestrated by Google Kubernetes Engine (GKE).As of Ray 2.55, Google Cloud TPUs are a first-class accelerator in Ray.
- ▪This means that TPUs are now in Ray's release pipelines with official pre-built images and support across the core libraries, instead of the old "experimental" path where you built your own containers and leaned on community help.
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TL;DR: If you already scale Python with Ray on GPUs, your code can now run on TPU (Tensor Processing Unit, Google's AI accelerator chip) with fully supported official APIs you already know. The task-and-actor model, a JaxTrainer, the same Ray Serve deployment just pointed at TPUs orchestrated by Google Kubernetes Engine (GKE).As of Ray 2.55, Google Cloud TPUs are a first-class accelerator in Ray. This means that TPUs are now in Ray's release pipelines with official pre-built images and support across the core libraries, instead of the old "experimental" path where you built your own containers and leaned on community help.
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