Author: Tomonori Shimomura

With a one-click installation of the Amazon Elastic Kubernetes Service (Amazon EKS) add-on for SageMaker HyperPod observability, you can consolidate health and performance data from NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. In this post, we walk you through installing and using the unified dashboards of the out-of-the-box observability feature in SageMaker HyperPod. We cover the one-click installation from the Amazon SageMaker AI console, navigating the dashboard and metrics it consolidates, and advanced topics such as setting up custom alerts.

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists, […]