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 interruptions to silent data corruption, and those were only the cases that interrupted the job and were traced to SDC.
Amazon SageMaker HyperPod already replaces nodes that report a fault: the health-monitoring agent watches for GPU errors, deep health checks stress-test nodes, and automatic node recovery swaps out bad hardware. SDC slips past all of these features, because the hardware never reports anything. Detection has to come from the training job itself.
In this post, we add three checks to a PyTorch DistributedDataParallel (DDP) training loop to detect SDC. When a check fires, we map the suspect rank to its node, contain the corruption, and have SageMaker HyperPod replace the node. Each check covers a different place where a fault can land:
48xlarge instances (32 NVIDIA H100 GPUs), and repeated every test on three instances, the minimum the checks need. The full code of this solution is available in the accompanying GitHub repository .
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