One stop shop for running AI/ML on AWS
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AWS Deep Learning Containers (DLCs) are pre-built Docker images for running AI/ML workloads on AWS. Each image is tested and patched for security vulnerabilities. For more details, visit our documentation.
- [2026/04/07] SGLang v0.5.10 — EC2:
0.5.10-gpu-py312-ec2· SageMaker:0.5.10-gpu-py312 - [2026/04/07] vLLM v0.19.0 — EC2:
0.19-gpu-py312-ec2· SageMaker:0.19-gpu-py312 - [2026/03/26] vLLM v0.18.0 — EC2:
0.18-gpu-py312-ec2· SageMaker:0.18-gpu-py312 - [2026/03/23] PyTorch Training v2.10.0 — EC2:
2.10.0-gpu-py313-cu130-ubuntu22.04-ec2· SageMaker:2.10.0-gpu-py313-cu130-ubuntu22.04-sagemaker
- [2026/02/10] Extended support for PyTorch 2.6 Inference containers until June 30, 2026
- PyTorch 2.6 Inference images will continue to receive security patches and updates through end of June 2026
- For complete framework support timelines, see our Support Policy
- Distributed Training on Amazon EKS - Configure and validate a distributed training cluster with DLCs on Amazon EKS.
- DLCs with Amazon SageMaker AI & MLflow - Use DLCs with SageMaker AI managed MLflow for experiment tracking and model management.
- LLM Serving on Amazon EKS with vLLM - Deploy and serve LLMs on Amazon EKS using vLLM DLCs.
- Fine-tuning Meta Llama 3.2 Vision - Fine-tune and deploy Llama 3.2 Vision for web automation using DLCs, Amazon EKS, and Amazon Bedrock.
- DLCs with Amazon Q Developer and MCP - Streamline deep learning environments with Amazon Q Developer and Model Context Protocol.
- LLM Deployment on Amazon EKS - Deploy and optimize LLMs on Amazon EKS using vLLM DLCs. See also: Sample Code
This project is licensed under the Apache-2.0 License.