AWS Publishes Tutorial on Detecting Silent Data Corruption in SageMaker HyperPod
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On November 12, 2026, AWS published on its Machine Learning Blog a method for detecting silent data corruption (SDC) in distributed training on Amazon SageMaker HyperPod. The method adds three checks to the PyTorch DDP training loop: NaN/Inf gradient checks before and after all-reduce, loopback replay audits, and weight fingerprint comparisons after optimizer updates, covering three fault points: the compute path, all-reduce, and weight storage. The event is currently at the stage of releasing this technical tutorial; the report is a first-party source from AWS, with no further developments reported.
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- AWS Machine Learning BlogDetecting and recovering from silent data corruption on Amazon SageMaker HyperPod
AWS describes how to detect silent data corruption (SDC) on Amazon SageMaker HyperPod: add three checks to the PyTorch DDP training loop, including NaN/Inf gradient checks before and after all-reduce, loopback replay audits, and weight fingerprint comparisons after optimizer updates, covering three fault sites: the compute path, all-reduce, and weight storage.
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