aws/deep-learning-containers

What it solves

It simplifies the deployment of AI/ML workloads on AWS by providing pre-built, tested, and security-patched Docker images. This removes the need for developers to manually configure complex deep learning environments, install drivers, and manage dependencies for popular frameworks.

How it works

AWS provides a library of Deep Learning Containers (DLCs) that are optimized for AWS infrastructure. These images come pre-installed with essential tools and frameworks such as PyTorch, TensorFlow, vLLM, SGLang, Ray, and llama.cpp, as well as necessary drivers (like CUDA) and communication plugins (like EFA and NCCL) for distributed training and inference.

Who it’s for

ML engineers and data scientists who need to run training or inference workloads on AWS services like Amazon EC2, Amazon EKS, and Amazon SageMaker.

Highlights

• Broad Framework Support: Includes optimized images for vLLM, SGLang, Ray, PyTorch, TensorFlow, and llama.cpp.
• AWS Optimized: Specifically patched and tested for AWS hardware and services (e.g., Graviton ARM64, NVIDIA GPUs).
• Distributed Training: Supports multi-node, multi-GPU distributed training via Ray Train and other integrated plugins.
• Enterprise Ready: Images are regularly updated for security vulnerabilities and provided with clear support timelines.