Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio

The article discusses how Amazon SageMaker HyperPod and Amazon SageMaker Studio can enhance machine learning workflows, particularly for foundation model training and fine-tuning. Key highlights include:

• SageMaker HyperPod provides resilient, scalable clusters for large-scale ML training with automated instance repair
• SageMaker Studio offers a unified development environment with integrated tools for ML lifecycle management
• FSx for Lustre enables high-performance, shared file storage across development and training environments
• Users can mount file systems directly to SageMaker Studio, enabling seamless data and code sharing
• Supports two file system mounting options: shared partition or individual user partitions

The article demonstrates a practical example of fine-tuning the DeepSeek-R1-Distill-Qwen-14B model using SageMaker HyperPod with Amazon EKS, showcasing the integrated workflow from development to distributed training.