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Run Ray on TPU, Part 2: Ray AI libraries- Google Developers Blog
Google Developers · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Google released Ray AI libraries optimized for TPU: Ray Serve for multi-host model serving, Ray Data with iter_jax_batches for efficient batching, and JaxTrainer for distributed JAX training.
- 为何重要
- Engineers deploying large language models or training jobs on Google Cloud TPUs who want production-grade serving, data pipelines, and training with automatic slice placement handling.
- 注意
- Multi-host models must declare topology field in config; omitting it causes workers to scatter across slices, hanging jobs silently. JAX imports must occur inside worker functions, not at module scope.
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