Amazon Nova customization on Amazon SageMaker HyperPod

You can customize Amazon Nova models using Amazon Nova recipes and train them on Amazon SageMaker HyperPod. A recipe is a YAML configuration file that provides details to SageMaker AI on how to run your model customization job.

Amazon SageMaker HyperPod offers high-performance computing with optimized GPU instances and Amazon FSx for Lustre storage, robust monitoring through integration with tools like TensorBoard, flexible checkpoint management for iterative improvement, seamless deployment to Amazon Bedrock for inference, and efficient scalable multi-node distributed training-all working together to provide organizations with a secure, performant, and flexible environment to tailor Nova models to their specific business requirements.

Amazon Nova customization on Amazon SageMaker HyperPod stores model artifacts including model checkpoints in a service-managed Amazon S3 bucket. Artifacts in the service-managed bucket are encrypted with SageMaker-managed AWS KMS keys. Service-managed Amazon S3 buckets don't currently support data encryption using customer-managed KMS keys. You can use this checkpoint location for evaluation jobs or Amazon Bedrock inference.

Standard pricing can apply for compute instances, Amazon S3 storage, and FSx for Lustre. For pricing details, see SageMaker HyperPod pricing, Amazon S3 pricing, and FSx for Lustre pricing.