AWS Deep Learning Containers (AWS DL Containers) are Docker images pre-installed with deep learning frameworks to make it easy to deploy custom machine learning (ML) environments quickly by letting you skip the complicated process of building and optimizing your environments from scratch. AWS DL Containers support TensorFlow, PyTorch, and Apache MXNet. You can deploy AWS DL Containers on Amazon Sagemaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, Amazon Elastic Container Service (Amazon ECS). The containers are available through Amazon Elastic Container Registry (Amazon ECR) and AWS Marketplace at no cost--you pay only for the resources that you use. Get started with this tutorial.

Docker containers are a popular way to deploy custom ML environments that run consistently in multiple environments. But building and testing container images for deep learning is hard, error-prone, and can take days due to software dependencies and version compatibility issues. These images also need to be optimized to distribute and scale ML workloads efficiently across a cluster of instances, which requires specialized expertise. This process has to be repeated when framework updates are released. All of this is undifferentiated heavy lifting that takes valuable developer time and slows down your pace of innovation.

AWS DL Containers provide Docker images that are pre-installed and tested with the latest versions of popular deep learning frameworks and the libraries they require. AWS DL Containers come optimized to distribute ML workloads efficiently on clusters of instances on AWS, so that you get high performance and scalability right away.