SageMaker HyperPod 跨区域训练：计算与数据分离的架构与验证
Multi-Region training with Amazon SageMaker HyperPod and Qumulo
精选理由
AWS 官方教程，讲怎么用 HyperPod 加 Qumulo 把训练算力和数据分开放在两个 Region，还附了吞吐量对比数据，做大规模训练的可以参考。
AWS 发布了一篇使用 Amazon SageMaker HyperPod 与 Cloud Native Qumulo 进行跨区域训练的教程。核心架构是把训练计算放在一个 AWS Region，数据集放在另一个 Region，通过 Qumulo 的 NeuralCache 做缓存加速。验证结果显示，经过短暂的 NeuralCache 预热后，远程集群的吞吐量可以追平计算与数据同区域部署的集群。文章给出了完整架构说明和验证数据。
原文 · AWS Machine Learning Blog
Multi-Region training with Amazon SageMaker HyperPod and Qumulo
Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster's throughput after a brief NeuralCache warmup.