Multi-Region training with Amazon SageMaker HyperPod and Qumulo
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This article presents a solution for multi-region AI training by pairing Amazon SageMaker HyperPod with Qumulo’s Cloud Native Qumulo (CNQ) and Cloud Data Fabric (CDF). This architecture allows teams to keep training compute in one AWS Region while storing the dataset in another, eliminating the need for costly data replication. The core technology, NeuralCache, uses predictive caching to learn data access patterns and pre-fetch blocks from the hub region to local NVMe storage on spoke clusters. Validation tests showed that after an initial warmup of 100–150 batches, remote clusters achieved throughput equivalent to co-located setups, reaching 98–100% GPU utilization. The study demonstrated this using LLaMA v3 models across US East (Ohio) and US West (Oregon) regions with approximately 60 ms network latency. Although cold starts initially show reduced performance due to cache warming, the impact becomes negligible at scale, affecting less than 1% of wall-clock time for large jobs. The authors provide a detailed walkthrough for configuring VPC peering, deploying CNQ instances, and running distributed PyTorch training jobs.
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