New in AWS Deep Learning Containers: Support for Amazon SageMaker and MXNet 1.4.1 with CUDA 10.0
AWS Deep Learning (DL) Containers now come with libraries and packages required for model training and inference in Amazon SageMaker. Amazon SageMaker is a fully managed service that enables you to build, train, and deploy machine learning models at scale. You can now use DL Containers seamlessly across Amazon SageMaker, Amazon Elastic Container Service for Kubernetes (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). This release of DL Containers also updates Apache MXNet images to 1.4.1 with support for CUDA 10.0.
AWS Deep Learning (DL) Containers now come with libraries and packages required for model training and inference in Amazon SageMaker. Amazon SageMaker is a fully managed service that enables you to build, train, and deploy machine learning models at scale. You can now use DL Containers seamlessly across Amazon SageMaker, Amazon Elastic Container Service for Kubernetes (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). This release of DL Containers also updates Apache MXNet images to 1.4.1 with support for CUDA 10.0.
AWS DL Containers are continually updated with the latest deep learning frameworks and libraries. DL Containers provide optimized and validated Docker images that allow developers to easily setup custom machine learning environments for training and inference on Amazon SageMaker, Amazon EC2, Amazon ECS, and Amazon EKS. Docker images are available for TensorFlow and Apache MXNet on CPU and GPU hardware for training and inference. You can get DL Containers through Amazon Elastic Container Registry (Amazon ECR) and AWS Marketplace at no cost--you pay only for the resources that you use.
For more information, see AWS Deep Learning Containers and dockerfiles for DL Containers. To get started with AWS DL Containers, check out the documentation and the tutorial.