AWS EC2 Capacity Blocks for ML and SageMaker Training Plans for Short-Term GPU Allocation Strategy

GPU shortages in machine learning workloads have become a serious challenge for many companies. According to the AWS Machine Learning Blog, GPU demand exceeds industry-wide supply, making GPUs a scarce resource. To address this issue, AWS is providing a new solution for short-term ML workloads.

Limitations of Traditional GPU Procurement Methods

On-demand GPU instances can be used immediately if capacity is available, but their availability is unstable due to regional supply and demand. After stopping an instance, it may not be possible to reacquire the same capacity, leading to uncertainty and increased costs due to prolonged instance operation.

Spot instances can achieve up to 90% cost savings but may be interrupted by Amazon EC2 when capacity is needed. They are suitable for ML workloads with periodic checkpointing, such as distributed learning jobs or batch inference workloads.

On-demand capacity reservations (ODCRs) are applied to planned and stable workloads, but short-term ODCR availability is limited, especially for P-type instances. Without long-term contracts, they are billed at on-demand rates, providing no cost benefits.

(Source: AWS Machine Learning Blog)

How EC2 Capacity Blocks for ML Work

EC2 Capacity Blocks for ML is a solution for reserving GPU capacity for short-term ML workloads. This service ensures that GPU capacity can be secured for purposes such as load testing, model validation, time-limited workshops, and pre-release inference capacity preparation.

Unlike traditional ODCRs, Capacity Blocks for ML are designed specifically for short-term or exploratory workloads. By making a commitment in advance, GPU capacity is guaranteed for a specified period, eliminating the uncertainty of on-demand instances.

Capacity blocks are reserved for specific periods and instance types, ensuring access to resources during that period. This allows for the execution of important projects, demonstrations, and time-constrained ML tasks with confidence.

Integration with SageMaker Training Plans

SageMaker Training Plans provide reserved capacity for training workloads in Amazon SageMaker. This service is suitable for regular model retraining or large-scale training jobs.

By using Training Plans, the necessary GPU capacity can be reserved in advance for SageMaker training jobs. This improves the predictability of training schedules and prevents project delays due to resource shortages.

By combining both services, a comprehensive GPU capacity management strategy can be built to support various ML workloads, from short-term experiments to production-scale training.

(Source: AWS Machine Learning Blog)

Summary

  • EC2 Capacity Blocks for ML can be used to reserve GPU capacity in advance for short-term ML workloads, such as load testing and model validation.
  • SageMaker Training Plans improve the predictability of training schedules for regular model retraining or large-scale training jobs.
  • The uncertainty of traditional on-demand and spot instances can be eliminated, allowing for the execution of important projects and demonstrations with confidence.
  • By selecting the optimal GPU procurement strategy for each use case, from short-term exploratory workloads to full-scale operations, the balance between cost and availability can be optimized.