reference deployment

NVIDIA Clara Train SDK on AWS

Application-development framework for medical-imaging research and AI

This Partner Solution deploys NVIDIA Clara Train to the Amazon Web Services (AWS) Cloud. Clara Train is NVIDIA’s domain-optimized application-development framework for medical-imaging researchers and artificial intelligence (AI) developers. Clara Train SDK includes an AI-assisted annotation developer toolkit that can be integrated into existing medical viewers, accelerating the creation of AI-ready, annotated medical-imaging datasets.

Clara Train also provides a TensorFlow-based training framework with domain-specific pretrained models that accelerate AI development with techniques like transfer learning, federated learning, and automated machine learning. Models trained with Clara Train are packaged as Medical Model Archives (MMARs), which provide a standardized format for training workflows and collaborations.

This Partner Solution is for not only researchers and developers but also health IT infrastructure architects, administrators, and DevOps professionals who are planning to implement or extend their Clara Train SDK workloads to the AWS Cloud.

This deployment provides scalable access to NVIDIA V100 Tensor Core graphics processing units (GPUs) and the Amazon Elastic Compute Cloud (Amazon EC2) P3 instance type, with pay-as-you-go pricing. This deployment is based on Amazon Elastic Container Service (Amazon ECS) and Amazon EC2. Amazon Elastic File System (Amazon EFS) is used for storage shared between containers.

This Partner Solution was created by NVIDIA in collaboration with AWS. NVIDIA is an AWS Partner.

AWS Service Catalog administrators can add this architecture to their own catalog.  

  •  What you'll build
  • The Partner Solution sets up the following:

    • A highly available architecture that spans two Availability Zones.*
    • A virtual private cloud (VPC) configured with public and private subnets, according to AWS best practices, to provide you with your own virtual network on AWS.*
    • In the public subnets:
      • Managed network address translation (NAT) gateways to allow outbound internet access for resources in the private subnets.*
      • A Linux bastion host in an Auto Scaling group to allow inbound Secure Shell (SSH) access to Amazon EC2 host instances in the private subnets.*
    • In the private subnets:
      • One instance of the NVIDIA Clara Train SDK container deployed with the Amazon EC2 launch type, on a GPU-enabled Amazon EC2 instance, in an Auto Scaling group.
      • One P3 Amazon EC2 instance.
    • Amazon EFS, a fully managed elastic network file system (NFS) to persist and share data across container instances.
    • Amazon ECS, a fully managed container orchestration service for running and managing Docker containers on a cluster.
    • An Application Load Balancer to route traffic to the NVIDIA Clara Train APIs and over HTTPS.

    * The template that deploys the Partner Solution into an existing VPC skips the components marked by asterisks and prompts you for your existing VPC configuration.

  •  How to deploy
  • To deploy this Partner Solution, follow the instructions in the deployment guide, which includes these steps.

    1. If you don't already have an AWS account, sign up at, and sign in to your account.
    2. Launch the Partner Solution. The stack takes about 30 minutes to deploy. Before you create the stack, choose the AWS Region from the top toolbar. Choose one of the following options:
    3. Test the deployment.

    Amazon may share user-deployment information with the AWS Partner that collaborated with AWS on this solution.  

  •  Costs and licenses
  • By using this Partner Solution, and by using the Clara Train SDK container and download models, you accept the terms and conditions of the included licenses. Licenses are available with the NVIDIA Clara Train SDK documentation and in the model application .zip files.

    You are responsible for the cost of the AWS services and any third-party licenses used while running this solution. There is no additional cost for using the solution.

    This solution includes configuration parameters that you can customize. Some of these settings, such as instance type, affect the cost of deployment. For cost estimates, refer to the pricing pages for each AWS service you use. Prices are subject to change.

    Tip: After you deploy a solution, create AWS Cost and Usage Reports to track associated costs. These reports deliver billing metrics to an Amazon Simple Storage Service (Amazon S3) bucket in your account. They provide cost estimates based on usage throughout each month and aggregate the data at the end of the month. For more information, refer to What are AWS Cost and Usage Reports?