PyTorch on Google Cloud TPUs
summary
PyTorch gains official Google Cloud TPU support in version 1.3 via torch_xla, using a lazy tensor architecture that records graphs and compiles them with XLA; users run existing ResNet and transformer code with almost no changes. Experimental Cloud TPU Pods support scales from 32/64-core slices up to 2,048 cores and shows large speedups (ResNet‑50 training measured faster than single‑host v3-8). Key details: run with torch_xla.distributed launcher, add spawn, call the torch_xla optimizer step to trigger execution, and watch compile time metrics for debug.
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chapters (15)
- 0:00Introduction
- 8:41Example Code
- 9:27Model
- 10:14Devices
- 10:47Data Sets
- 11:39Distributed Data Parallel
- 12:22Distributed Launcher
- 12:40Spawn Call
- 13:27Training
- 14:22Gradient Clipping
- 14:51Data Loaders
- 15:40Logging Debugging
- 16:33Getting Started
- 16:52Final Thoughts
- 18:00Facebook
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