We are excited to announce the general availability of the Amazon SageMaker HyperPod Command Line Interface (CLI) and Software Development Kit (SDK). These tools make it easier for developers and ML practitioners to build, train, and deploy large-scale AI models on SageMaker HyperPod.
SageMaker HyperPod CLI offers a simple, consistent command-line experience for managing HyperPod clusters and quick experimentation. The SDK offers intuitive programmatic access to HyperPod’s distributed training and inference capabilities and enables developers to have granular control over their workload configurations. With these tools, data scientists and ML engineers can easily launch training jobs, deploy scalable inference endpoints, and monitor cluster performance. Using simple commands, customers can access system logs and HyperPod observability dashboards, enabling them to debug issues and accelerate model development. These new developer interfaces enable customers to build and deploy production generative AI models faster on SageMaker HyperPod.
The HyperPod CLI and SDK are available in all AWS commercial regions where SageMaker HyperPod is supported. To get started with the SageMaker HyperPod CLI and SDK, visit the SageMaker HyperPod Developer Guide.
Categories:
Source: Amazon Web Services
Latest Posts
- Updates available for Microsoft 365 Apps for Monthly Enterprise Channel [MC1387170]
- Updates available for Microsoft 365 Apps for Current Channel [MC1387169]
- Amazon OpenSearch Service launches MCP Apps for agentic observability
- OpenAI GPT-5.4 and GPT-5.5 models now available in US East (N. Virginia) on Amazon Bedrock
![(Updated) Microsoft 365 admin center: New Multi-admin notifications for Microsoft 365 Backup [MC1102791]](https://mwpro.co.uk/wp-content/uploads/2024/08/pexels-googledeepmind-25630342-150x150.webp "(Updated) Microsoft 365 admin center: New Multi-admin notifications for Microsoft 365 Backup [MC1102791] 7")
The new CLI and SDK for SageMaker HyperPod seem like great additions for AI workflows. I’m especially interested in how the streamlined experience can help speed up experimentation and model deployment, particularly for large-scale models.