AWS Deep Learning Containers (DLC) is Amazon’s open collection of prebuilt, optimized Docker images for training and serving deep learning models on AWS. Maintained by AWS on GitHub with about 1,200 stars, it is described by the project as a one-stop shop for running AI/ML on AWS.
Setting up GPU drivers, CUDA, framework builds and dependencies is the least interesting part of machine learning, and a common source of broken environments. Deep Learning Containers remove that work: AWS publishes ready-to-run images for popular frameworks such as PyTorch and TensorFlow, tuned for AWS compute and kept current with security patches. The same images run across Amazon EC2, ECS, EKS and SageMaker, so teams can develop in one place and deploy in another without rebuilding their environment. The repository contains the Dockerfiles and build system behind the images, letting advanced users audit or customize exactly what ships to production.
Images are published in training and inference variants for both GPU and CPU use, with per-framework release notes and security scanning as part of the release process. For organizations standardizing on containers, that cadence matters: framework upgrades become an image tag change that can be tested and rolled back, rather than a rebuild of hand-maintained Dockerfiles.
Deep Learning Containers are free: the images can be pulled and used at no charge. You only pay for the underlying AWS compute and storage resources you run them on.
AWS Deep Learning Containers are for ML engineers and platform teams standardizing training and inference environments on AWS who would rather start from a maintained image than assemble CUDA stacks by hand.