Deploy SageMaker AI Inference Endpoints with Reserved GPU Capacity
This article explains how to reserve GPU capacity on AWS SageMaker for AI inference, search for available resources, and deploy inference endpoints on the reserved capacity.
Why it matters
Reserving GPU capacity for AI inference on SageMaker helps data scientists guarantee the necessary compute resources for their models, improving reliability and performance.
Key Points
- 1Reserve GPU capacity for AI inference on AWS SageMaker
- 2Search for available p-family GPU resources to reserve
- 3Deploy SageMaker inference endpoints on the reserved capacity
- 4Manage the inference endpoint lifecycle within the reservation
Details
The article outlines a data scientist's workflow for reserving GPU capacity on AWS SageMaker to run AI inference workloads. It describes how to search for available p-family GPU resources, create a training plan reservation, and then deploy a SageMaker inference endpoint on the reserved capacity. This allows data scientists to ensure they have the necessary GPU resources provisioned for model evaluation and inference, and manage the lifecycle of the inference endpoint within the reservation period.
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