This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey. We are aware some courses have accessibility limitations and are working to address. If you require accommodation, please contact AWS Training and Certification Customer Support.
This course explores elastic training capabilities on Amazon SageMaker HyperPod, enabling ML workloads to automatically scale based on resource availability. You will learn how to maximize GPU utilization, reduce training costs, and accelerate model development while maintaining training quality.
By the end of this course, you will be able to:
- Explain how static resource allocation impacts infrastructure utilization
- Describe the architecture and benefits of elastic training on SageMaker HyperPod
- Configure and deploy elastic training jobs with appropriate scaling policies
- Implement checkpoint management using PyTorch Distributed Checkpoint (DCP)
- Integrate elastic training with HyperPod capabilities for production workloads
