On-Demand Capacity Reservations and Capacity Blocks for ML
Capacity Reservations allow you to reserve compute capacity for Amazon EC2 instances in a specific Availability Zone. There are two types of Capacity Reservations serving different use cases.
Types of Capacity Reservations
The following are some common use cases for On-Demand Capacity Reservations:
-
Scaling events – Create On-Demand Capacity Reservations ahead of your business-critical events to ensure that you can scale when you need to.
-
Regulatory requirements and disaster recovery – Use On-Demand Capacity Reservations to satisfy regulatory requirements for high availability, and reserve capacity in a different Availability Zone or Region for disaster recovery.
The following are some common use cases for Capacity Blocks for ML:
-
Machine learning (ML) model training and fine-tuning – Get uninterrupted access to the GPU instances that you reserved to complete ML model training and fine-tuning.
-
ML experiments and prototypes – Run experiments and build prototypes that require GPU instances for short durations.
When to use On-Demand Capacity Reservation
Use On-Demand Capacity Reservations if you have strict capacity requirements, and your current or future business-critical workloads require capacity assurance. With On-Demand Capacity Reservations, you can ensure that you'll always have access to the Amazon EC2 capacity you've reserved for as long as you need it.
When to use Capacity Blocks for ML
Use Capacity Blocks for ML when you need to ensure that you have uninterrupted access to GPU instances for a defined period of time starting on a future date. Capacity Blocks are ideal for training and fine-tuning ML models, short experimentation runs, and handling temporary surges in inference demand in the future. With Capacity Blocks, you can ensure that you'll have access to GPU resources on a specific date to run your ML workloads.
