Undetected mathematical errors threaten distributed artificial intelligence training because typical safeguards like driver alerts and checksums fail to intercept compute-level faults. To combat this issue, a multi-step detection approach integrates non-finite gradient scans, ring replay audits, and weight fingerprint comparisons into PyTorch training workflows. The framework was evaluated on clusters featuring up to 32 NVIDIA H100 GPUs using software-injected faults. Once a corrupted rank is mapped to its Amazon Elastic Kubernetes Service node, operators can cordon the faulty hardware and resume workloads safely from an intact checkpoint.
Key points
Standard safeguards such as memory error-correcting code and transmission checksums fail…: Standard safeguards such as memory error-correcting code and transmission checksums fail to detect internal compute math errors.
Suspect GPU ranks are pinpointed to individual nodes through Amazon Elastic Kubernetes Se…: Suspect GPU ranks are pinpointed to individual nodes through Amazon Elastic Kubernetes Service for rapid operational isolation.
Workloads can be restarted from uncorrupted checkpoints after the platform cordons and re…: Workloads can be restarted from uncorrupted checkpoints after the platform cordons and replaces the compromised physical hardware.
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Artificial Intelligence
Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod
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.
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