AWS Deep Learning Containers
Pre-built Docker images for running AI/ML workloads on AWS.
Tested for performance and patched for security vulnerabilities.
What are DLCs?¶
AWS Deep Learning Containers (DLCs) are Docker images pre-configured with deep learning frameworks and tools. AWS builds, tests, and security-patches them so you can focus on your workload instead of environment setup.
Each image includes a framework (e.g. vLLM, PyTorch, Ray), its dependencies, and optimized libraries — ready to run on AWS compute services. All DLC images are provided at no cost — you only pay for the compute resources you use.
Getting Started¶
It's easy to get started. For example, to run a large language model server:
docker pull public.ecr.aws/deep-learning-containers/vllm:server-cuda
docker run --gpus all -p 8000:8000 \
public.ecr.aws/deep-learning-containers/vllm:server-cuda \
--model openai/gpt-oss-20b
Query the server:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "openai/gpt-oss-20b", "messages": [{"role": "user", "content": "Hello!"}]}'
LLM serving is just one example. DLCs cover a range of AI/ML workloads — explore guides by use case:
-
Serve Large Language Models
Deploy large language models with vLLM on EC2, EKS, or Amazon SageMaker AI.
-
Serve Multimodal Models
Serve TTS, image generation, video generation, and omni-chat models with vLLM-Omni.
-
Serve ML Models
Deploy any ML model with Ray Serve on EC2 or Amazon SageMaker AI — NLP, vision, audio, and tabular.
-
Train Models
Run distributed training with PyTorch on GPU or CPU, with EFA, NCCL, flash-attn, and DeepSpeed pre-installed.
-
Build Your Own Image
Use the lightweight Base images (CUDA + Python on Amazon Linux 2023) as the
FROMfor your custom AI/ML container.
For step-by-step walkthroughs on EKS, SageMaker, and more, see our blog posts. You can also subscribe to release notifications to stay up to date with new images.
Looking for something else? Let us know on GitHub.