Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans | Amazon Web Services
As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide supply. This imbalance has made GPUs a scarce resource, creating a challenge for customers who need reliable access to GPU compute resources for their ML workloads.
When you encounter GPU capacity limitations, you might consider creating on-demand capacity reservations (ODCRs). ODCRs apply to planned, steady-state workloads with well-understood usage patterns. Short-term ODCR availability for GPU instances, particularly P-type instances, is often limited. Additionally, without a long-term contract, ODCRs are billed at on-demand rates, offering no cost advantage. This makes ODCRs unsuitable for short or exploratory workloads such as testing, evaluations, or events. A guided approach to secure short-term GPU capacity becomes necessary.
In this post, you will learn how to secure reserved GPU capacity for short-term workloads using Amazon Elastic Compute...
Copyright of this story solely belongs to amazon.com. To see the full text click HERE
