deep-learning-containers

<h1 align="center">AWS Deep Learning Containers</h1>

One stop shop for running AI/ML on AWS

Docs · Available Images · Tutorials

🔥 What's New

🚀 Release Highlights

• [2026/02/06] Released v0.15.1 vLLM DLCs
  • EC2/EKS/ECS: public.ecr.aws/deep-learning-containers/vllm:0.15-gpu-py312-ec2
  • SageMaker: public.ecr.aws/deep-learning-containers/vllm:0.15-gpu-py312
• [2025/11/17] Released first SGLang DLCs
  • SageMaker: public.ecr.aws/deep-learning-containers/sglang:0.5.5-gpu-py312

📢 Support Updates

• [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

🎉 Hot Off the Press

• 🌐
  Master Distributed Training on Amazon EKS
  - Set up and validate a distributed training environment on Amazon EKS for scalable ML model training across multiple nodes.
• 🔄
  Level Up with Amazon SageMaker AI & MLflow
  - Integrate AWS DLCs with Amazon SageMaker AI's managed MLflow service for streamlined experiment tracking and model management.
• 🚀
  Deploy LLMs Like a Pro on Amazon EKS
  - Deploy and serve Large Language Models efficiently on Amazon EKS using vLLM Deep Learning Containers.
• 🎯
  Web Automation with Meta Llama 3.2 Vision
  - Fine-tune and deploy Meta's Llama 3.2 Vision model for AI-powered web automation.
• ⚡
  Supercharge Your DL Environment
  - Integrate AWS DLCs with Amazon Q Developer and Model Context Protocol (MCP).

🎓 Hands-on Workshop

• 🚀 LLM Deployment on Amazon EKS Workshop -
  Deploy and optimize LLMs on Amazon EKS using vLLM Deep Learning Containers. For more information, see
  Sample Code

About

AWS Deep Learning Containers (DLCs) are a suite of Docker images that streamline the deployment of AI/ML workloads on Amazon SageMaker AI, Amazon EKS, and
Amazon EC2.

🎯 What We Offer

• Pre-optimized Environments - Production-ready containers with optimized deep learning frameworks
• Latest AI/ML Tools - Quick access to cutting-edge frameworks like vLLM, SGLang, and PyTorch
• Multi-Platform Support - Run seamlessly on Amazon SageMaker AI, Amazon EKS, or Amazon EC2
• Enterprise-Ready - Built with security, performance, and scalability in mind

💪 Key Benefits

• Rapid Deployment - Get started in minutes with pre-configured environments
• Framework Flexibility - Support for popular frameworks like PyTorch, TensorFlow, and more
• Performance Optimized - Containers tuned for AWS infrastructure
• Regular Updates - Quick access to latest framework releases and security patches
• AWS Integration - Seamless compatibility with AWS AI/ML services

🎮 Perfect For

• Data Scientists building and training models
• ML Engineers deploying production workloads
• DevOps teams managing ML infrastructure
• Researchers exploring cutting-edge AI capabilities

🔒 Security & Compliance

Our containers undergo rigorous security scanning and are regularly updated to address vulnerabilities, ensuring your ML workloads run on a secure
foundation.

For more information on our security policy, see Security.

Quick Links

• Getting Started - Get up and running in minutes
• Tutorials - Step-by-step guides
• Available Images - Browse all container images
• Support Policy - Framework versions and timelines
• Security - Security policy

Getting Help

• GitHub Issues - Report bugs or request features

License

This project is licensed under the Apache-2.0 License.