A reference architecture for securely sharing one Amazon SageMaker HyperPod EKS cluster across multiple teams, using AWS IAM Identity Center for authentication, per-team SageMaker Domains and Kubernetes namespaces for isolation, HyperPod Task Governance for fairness, and namespace-level cost allocation for chargeback.

Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router’s production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.

In this post, we show you how to build a multi-Region inference architecture on Amazon Sagemaker Hyperpod.

Amazon SageMaker HyperPod Data Capture enables recording production inference data to improve models through a complete flywheel of capture, analysis, labeling, and fine-tuning.

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Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router's production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.

RobCo scaled its computer vision training pipeline using Ray on Amazon SageMaker HyperPod, reducing costs by 15% and eliminating GPU capacity wait times.

This article presents three detection checks for silent data corruption in distributed PyTorch training on Amazon SageMaker HyperPod, enabling automatic node replacement when faults are identified.

Introduction RobCo, a Munich-based company building autonomous industrial robots, scaled the computer vision training behind its robots with Ray on Amazon SageMaker HyperPod. Founded in 2020 out of the Technical University of Munich, RobCo builds modular robots that automate machine tending, palletizing, and depalletizing. The robots rely on object detection and localization models, which RobCo [...]

Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.

Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.

Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers.

Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.

Groups Conversations All groups and messages Sign in Machine Learning, Deep Learning and AI Conversations About Privacy • Terms     Accelerate agentic AI with NVIDIA Nemotron models on Amazon EKS and SageMaker HyperPod EKS 0 views Skip to first unread message Eyal Estrin unread, 3:03 AM (15 minutes ago)    to https://aws.amazon.com/blogs/machine-learning/accelerate-agentic-ai-with-nvidia-nemotron-models-on-amazon-eks-and-sagemaker-hyperpod-eks/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward