Detecting silent data corruption on Amazon SageMaker HyperPod
AIAWS explains how silent data corruption, where a GPU returns wrong numbers without raising an error, can be detected in PyTorch DistributedDataParallel training. The post adds three checks to the training loop: non-finite gradient checks, a ring replay audit that recomputes micro-batches on another node, and weight fingerprint comparison.
AIWhy it matters.
The post shows concrete checks and fingerprinting code for catching wrong GPU results that hardware error reports miss, with measured overhead and false-alarm rates.
Source details
AWS Machine Learning Blog · Siddharth Senger · aws.amazon.comPublished · added here
Original title: Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod