Multi-Region training with Amazon SageMaker HyperPod and Qumulo
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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.
With Amazon SageMaker HyperPod and Qumulo , you can place training compute in one AWS Region and keep your dataset in another. Training large AI models requires massive GPU capacity, but your ideal compute resources and your training data don’t always reside in the same AWS Region. Accessing data across Regions adds network latency and transfer costs. Teams face a choice: either replicate petabytes of data across Regions, or absorb cross-Region latency on every read and accept slower training.…
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