Multi-Region training with Amazon SageMaker HyperPod and Qumulo
How-To How to actually use this
What changed: SageMaker HyperPod now supports training clusters in one AWS region accessing datasets stored in a different region via Qumulo Cloud Native storage.
How to use it:
- Deploy a Qumulo Cloud Native file system in your data region (e.g., us-east-1) and mount it to your HyperPod cluster in the compute region (e.g., us-west-2).
- Configure the cross-region VPC peering or Transit Gateway connection between the two regions.
- Launch the HyperPod cluster with the Qumulo mount points specified in the cluster configuration.
- Start training; allow the first epoch to complete the NeuralCache warmup phase.
- Monitor throughput metrics to verify performance matches co-located baseline after warmup.
Good for: ML engineers training large models across geographic or regulatory boundaries.
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.
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