Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building generative AI models. It helps quickly scale model development tasks such as training, fine-tuning, or inference across a cluster of hundreds or thousands of AI accelerators.

借助 SageMaker HyperPod 任务治理创新，您可以全面了解和控制生成式人工智能模型开发任务（例如训练和推理）中的计算资源分配。

Nov 29, 2023 · Today, we are introducing Amazon SageMaker HyperPod, which helps reducing time to train foundation models (FMs) by providing a purpose-built infrastructure for distributed training at scale. You can now use SageMaker HyperPod to train FMs for weeks or even months while SageMaker actively monitors the cluster health and provides automated node ...

SageMaker HyperPod is a capability of SageMaker AI that provides an always-on machine learning environment on resilient clusters. You can use these clusters to run any machine learning workloads for developing state-of-the-art machine learning models such as large language models (LLMs) and diffusion models.

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** HyperPod 采用基于 Slurm 的 HPC 高性能弹性计算集群，能够实现单机或跨机器跨 GPU 的大规模并行训练。它提供原生的基于 GPU 或 CPU 的基础设施，您可以自由操控或部署任意框架，充分发挥亚马逊云科技上服务可伸缩的计算能力，线性扩展训练吞吐量。

Use Amazon SageMaker HyperPod recipes to get started with training and fine-tuning publicly available foundation models. To view the available recipes, see SageMaker HyperPod recipes. The recipes are pre-configured training configurations for the following model families:

Aug 16, 2024 · 利用 Amazon SageMaker HyperPod，用户可以像使用 Amazon EC2 实例一样快速启动、登录各种 GPU 资源 （如 G5 A10、P4 A100、P5 H100…...） 并进行 LLM 或 SD 等模型的分布..._hyperpod

Dec 26, 2023 · 今天，我们推出 Amazon SageMaker HyperPod ，它通过为大规模分布式培训提供专用基础设施，帮助缩短基础模型的培训时间。

Scale and accelerate generative AI model development across thousands of AI accelerators. Amazon SageMaker HyperPod provides full visibility and control over compute resource allocation across generative AI model development tasks, such as training and inference.