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English(EN) Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

AWS SageMaker HyperPod 新增 SDC 检测功能,以保护大规模 AI 训练

Amazon SageMaker HyperPod 引入了新的方法来检测和从大规模 AI 模型训练期间的静默数据损坏 (SDC) 中恢复。SDC 发生时,硬件(如 GPU)会产生不正确但看似合理的结果,而不会引发错误,这可能导致计算资源浪费和模型性能下降。新检查已集成到 PyTorch 的 DistributedDataParallel 训练循环中,可识别非有限梯度、比较权重指纹并执行环重放审计,以查明故障硬件。 AI

影响 增强了大规模 AI 训练基础设施的可靠性,减少了计算浪费,并提高了模型质量。

排序理由 这是一篇关于现有云服务新功能的技木博客文章,并非新的模型发布或研究突破。

在 AWS Machine Learning Blog 阅读 →

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AWS SageMaker HyperPod 新增 SDC 检测功能,以保护大规模 AI 训练

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报道来源 [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Siddharth Senger ·

    在 Amazon SageMaker HyperPod 上检测和恢复静默数据损坏

    Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automa…