Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPod

This article introduces elastic training on Amazon SageMaker HyperPod, enabling ML workloads to automatically scale based on available cluster resources while maintaining training quality.

• Training jobs dynamically scale up/down without manual intervention or infrastructure reconfiguration
• Maintains constant global batch size and learning rate across different data-parallel configurations
• Gracefully handles resource preemption for higher-priority workloads without terminating entire jobs
• Uses PyTorch Distributed Checkpoint (DCP) for automatic model/optimizer state resharding
• Integrates with Kubernetes, task governance, and SageMaker HyperPod observability
• Llama-3 70B fine-tuning benchmark showed throughput improvement from 2K to 14K tokens/second
• Pre-built recipes available for Llama and GPT-OSS models requiring only YAML configuration

Elastic training reduces GPU idle hours, cuts infrastructure costs, and accelerates model development by automatically utilizing available capacity while eliminating manual scaling overhead.