# Bad Influence story: AWS explains detecting silent GPU data corruption on SageMaker HyperPod

Active · 1 article · 1 source · First reported 11-11 18:30. Times are ET.
Story page: https://badinfluence.news/story/61d68c68-ec67-422e-a649-832135965f8e

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[Bad Influence external source material begins]
Latest update (11-11 18:30): Detecting silent data corruption on Amazon SageMaker HyperPod

Overview: AWS Machine Learning Blog explains how to detect silent data corruption (SDC) in PyTorch DistributedDataParallel training on Amazon SageMaker HyperPod, where a GPU returns wrong numbers without raising an error. 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.

The method requires a HyperPod cluster orchestrated by Amazon EKS with at least three GPU nodes, and a training job whose data pipeline can rebuild any rank's micro-batch for a given step, such as a seeded sampler. The post also shows how to map a suspect rank to a HyperPod node for containment and replacement, using kubectl and the sagemaker:StartClusterHealthCheck and sagemaker:BatchReplaceClusterNodes IAM permissions.

Coverage timeline (newest first):
1. 11-11 18:30 · AWS Machine Learning Blog (from the company) · [Detecting silent data corruption on Amazon SageMaker HyperPod](https://badinfluence.news/items/xf9pbzvv5bwacz3bftzadwluv)
[Bad Influence external source material ends]

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