Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans | Amazon Web Services

基本信息

• 来源: blogs_podcasts
• 原始来源: https://aws.amazon.com/blogs/machine-learning/secure-short-term-gpu-capacity-for-ml-workloads-with-ec2-capacity-blocks-for-ml-and-sagemaker-training-plans

来源摘要/节选

公开展示已截断至最多 800 个字符；请访问原始来源查看完整上下文。

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.…

来源说明

当前只保存了公开页面节选，不代表原文全文。请以原始来源为准。

本页只呈现已做哈希绑定的来源证据，不包含基于旧正文或缺失原文的扩展推断。