Title

AWS re:Invent 2023 - [LAUNCH] Reserve GPU capacity with Amazon EC2 Capacity Blocks for ML (CMP105)

Summary

• Introduction: Jake Siddall, a product manager within EC2, introduced the new EC2 Capacity Blocks product for reserving GPU capacity for machine learning workloads.
• Machine Learning on AWS: AWS is a popular choice for running ML workloads, with over 100,000 customers using their services. Generative AI has seen significant growth, and AWS has played a role in democratizing ML.
• Service Offerings: AWS offers a comprehensive set of AI and ML services across three layers: ML frameworks and infrastructure, Amazon SageMaker, and AI services.
• EC2 Instances for ML: AWS provides various EC2 instances optimized for ML workloads, including GPU-based instances for training (P4, P5) and inference (G5, G5G), as well as custom ML silicon instances (DL1, Tranium, Inferentia).
• P5 Instances: P5 instances are the latest and highest performing instances for deep learning training, featuring NVIDIA H100 GPUs and are now supported by Capacity Blocks.
• GPU Scarcity: The demand for GPUs has outpaced supply, making them scarce. Customers often face long wait times and hold onto GPUs even when not in use.
• Provisioning Options: On-demand capacity reservations (ODCRs), spot instances, and the new EC2 Capacity Blocks are available for provisioning GPU instances.
• EC2 Capacity Blocks: Capacity Blocks allow customers to reserve GPU capacity for a future date for a specific duration, offering more flexibility and potentially lower costs than ODCRs.
• Usage and Cost: Capacity Blocks can be used for various scenarios, from single-instance experiments to large-scale training. They can also help avoid waste by supplementing baseline capacity with burst capacity.
• Demo: A live demo showed how to search for, purchase, and use Capacity Blocks in the AWS Management Console, including setting up launch templates and integrating with EKS clusters.

Insights

• Democratization of ML: AWS's role in making ML accessible to a wide audience is significant, especially with the rise of generative AI applications.
• EC2 Capacity Blocks: The introduction of EC2 Capacity Blocks is a strategic move to address the GPU scarcity issue, providing customers with a more predictable and flexible way to secure GPU resources.
• Cost Optimization: Capacity Blocks offer dynamic pricing, which can be lower than on-demand rates, providing cost savings for customers with intermittent or bursty GPU needs.
• Integration with EKS: The ability to integrate Capacity Blocks with EKS clusters and auto-scaling groups demonstrates AWS's commitment to providing seamless and scalable ML infrastructure solutions.
• Future Instance Types: The mention of introducing more instance types to the Capacity Blocks model suggests ongoing innovation and expansion of AWS's ML infrastructure offerings.