# Ray 2.55 正式支持 Google Cloud TPU，通过 KubeRay 自动编排多主机切片

- 来源：Google Developers Blog（RSS）
- 发布时间：2026-07-21 00:59
- AIHOT 分数：56
- AIHOT 链接：https://aihot.virxact.com/items/cmrth189u36hobitl8fiuyu4z
- 原文链接：https://developers.googleblog.com/run-ray-on-tpu-part-1-the-foundations

## AI 摘要

Ray 2.55 首次为 Google Cloud TPU 提供一等支持，开发者可通过 Ray 任务与 Actor API 在 TPU 上运行分布式 Python 负载。

## 正文

Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.