Amazon SageMaker HyperPod now supports custom AMIs, enabling customers to deploy clusters with pre-configured, security-hardened environments that meet their specific organizational requirements. Customers deploying AI/ML workloads on HyperPod need customized environments that meet strict security, compliance, and operational requirements while maintaining fast cluster startup times, but often struggle with complex lifecycle configuration scripts that slow deployment and create inconsistencies across cluster nodes.
This capability allows customers to build upon HyperPod’s performance-optimized base AMIs while incorporating customized security agents, compliance tools, proprietary libraries, and specialized drivers directly into the image, delivering faster startup times, improved reliability, and enhanced security compliance. Security teams can embed organizational policies directly into base images, allowing AI/ML teams to use pre-approved environments that accelerate time-to-training while meeting enterprise security standards. You can specify custom AMIs when creating new HyperPod clusters using the CreateCluster API, adding instance groups with UpdateCluster API, or patching existing clusters with UpdateClusterSoftware API. Custom AMIs must be built using HyperPod’s public base AMIs to maintain compatibility with distributed training libraries and cluster management capabilities.
This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more about custom AMI support, see the Amazon SageMaker HyperPod User Guide.
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Source: Amazon Web Services
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