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English(EN) 🤖 【AWS ML Blog】Reduce inference cold starts on Amazon SageMaker HyperPod with model caching Amazon SageMaker HyperPod now supports model caching for inference,

AWS SageMaker HyperPod 添加模型缓存以大幅缩短推理冷启动时间

作者 PulseAugur 编辑部 · [2 个来源] ·

Amazon SageMaker HyperPod 引入了一项模型缓存功能，旨在显著减少推理冷启动时间。此新功能将模型权重和容器镜像预加载到集群节点上，使 Pod 能够从本地 NVMe 存储高速访问数据，而不是依赖于较慢的网络下载。因此，推理 Pod 可以在几秒钟内开始提供流量，而不是之前所需的几十分钟，从而提高了自动扩展的响应速度。 AI

影响 加速在 AWS 上部署和扩展 LLM 推理，可能降低运营成本并改善用户体验。

排序理由 这是对现有云 ML 平台的特性增强，而不是新的前沿模型发布或核心研究。

在 Mastodon — sigmoid.social 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AWS SageMaker HyperPod 添加模型缓存以大幅缩短推理冷启动时间

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这是对现有云 ML 平台的特性增强，而不是新的前沿模型发布或核心研究。
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infra, product
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报道来源 [2]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Kareem Syed-Mohammed ·

    使用模型缓存减少 Amazon SageMaker HyperPod 上的推理冷启动

    Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to…

  2. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    🤖 【AWS ML Blog】通过模型缓存减少 Amazon SageMaker HyperPod 上的推理冷启动 Amazon SageMaker HyperPod 现在支持推理的模型缓存

    🤖 【AWS ML Blog】Reduce inference cold starts on Amazon SageMaker HyperPod with model caching Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images o... # AI # TechNews 🔗 https:// aws.amazon.com/blogs/machine-l earnin…

    链接 aws.amazon.com/…/reduce-inference-cold-st…

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