Amazon SageMaker HyperPod now supports IDEs and Notebooks, enabling AI developers to run JupyterLab, Code Editor, or connect local IDEs to run their interactive AI workloads directly on HyperPod clusters.
AI developers can now run IDEs and notebooks on the same persistent HyperPod EKS clusters used for training and inference. This enables developers to leverage HyperPod’s scalable GPU capacity with familiar tools like HyperPod CLI, while sharing data across IDEs and training jobs through mounted file systems such as FSx, EFS, etc..
Administrators can maximize CPU/GPU investments through unified governance across IDEs, training, and inference workloads using HyperPod Task Governance. HyperPod Observability provides usage metrics including CPU, GPU, and memory consumption, enabling cost-efficient cluster utilization.
This feature is available in all AWS Regions where Amazon SageMaker HyperPod is currently available, excluding China and GovCloud (US) regions. To learn more, visit our documentation.
Categories: marketing:marchitecture/artificial-intelligence,marketing:marchitecture/analytics
Source: Amazon Web Services
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