About Deep Learning Containers
Deep Learning Containers is an MCP server in the Documentation category: one stop shop for running AI/ML on AWS.
Install
From source
git clone https://github.com/aws/deep-learning-containersThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
About
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.
🔥 What's New
🚀 Release Highlights
- [2026/05/06] SGLang v0.5.11 — EC2:
0.5.11-gpu-py312-ec2· SageMaker:0.5.11-gpu-py312· Model support for Gemma 4, GLM-5.1, Qwen 3.4, and more - [2026/05/05] vLLM v0.20.1 — EC2:
0.20-gpu-py312-ec2· SageMaker:0.20-gpu-py312· Bug fixes for DeepSeek V4. - [2026/04/30] PyTorch v2.11.0 — EC2:
2.11.0-cu130-amzn2023· SageMaker:2.11.0-cu130-amzn2023-sagemaker· Amazon Linux 2023 with EFA, flash-attn, and transformer-engine. - [2026/04/28] vLLM v0.20.0 — EC2:
0.20-gpu-py312-ec2· SageMaker:0.20-gpu-py312· Introduces support for DeepSeek V4. - [2026/04/20] vLLM v0.19.1 — EC2:
0.19-gpu-py312-ec2· SageMaker:0.19-gpu-py312· This upgrades Transformers to 5.5.4, enabling Gemma 4 support. - [2026/04/07] SGLang v0.5.10 — EC2:
0.5.10-gpu-py312-ec2· SageMaker:0.5.10-gpu-py312
📢 Support Updates
- [2026/04/28] We cannot guarantee security patching on Ubuntu-based vLLM and SGLang images due to the lack of Ubuntu Pro licensing. Customers may continue using these images at their own discretion and risk. We recommend migrating to our Amazon Linux-based images.
- [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
📝 Blog Posts
- 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.
🎓 Workshop
- LLM Deployment on Amazon EKS - Deploy and optimize LLMs on Amazon EKS using vLLM DLCs. See also: Sample Code
License
This project is licensed under the Apache-2.0 License.
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.