Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

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Published: Nov 11, 2026, 11:30 PM UTC
Collected: Nov 11, 2026, 11:39 PM UTC

Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.

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Silent data corruption (SDC) happens when hardware, such as a GPU, returns a wrong but plausible number and raises no error. In distributed training, the wrong number can flow into the gradients and weights of every replica. Loss can still look normal for hours, and by the time a team notices an unexplained divergence or a weaker model, the compute is spent. It happens at scale: during a 54-day period of Llama 3 405B pre-training on up to 16K H100 GPUs, Meta attributed 6 of 419 unexpected job…

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