In the news
Run Ray on TPU, Part 1: The foundations- Google Developers Blog
Google Developers · Published · 3 min read
In 30 seconds
- What happened
- Ray 2.55 now treats Google Cloud TPUs as first-class accelerators with official APIs, letting Python developers scale distributed workloads on TPU slices via Google Kubernetes Engine.
- Why it matters
- Matters for engineers already using Ray with GPUs who want to run the same code on TPUs, or those building distributed AI workloads needing TPU compute.
- Watch out
- TPU chips must stay on one intact slice via dedicated interconnect; the slice_placement_group API is marked alpha, so its surface may change between releases.
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