ML on AWS. (original) (raw)

One stop shop for running AI/ML on AWS.

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🔥 What's New

🚀 Release Highlight

[2025/12/09] We released v0.12.0 vLLM DLC, available in EC2/EKS/ECS public.ecr.aws/deep-learning-containers/vllm:0.12.0-gpu-py312-ec2 and SageMaker public.ecr.aws/deep-learning-containers/vllm:0.12.0-gpu-py312
[2025/11/17] We released first Sglang DLC, available in SageMaker public.ecr.aws/deep-learning-containers/sglang:0.5.5-gpu-py312

🎉 Hot Off the Press

Learn to set up and validate a distributed training environment on Amazon EKS using AWS Deep Learning Containers for scalable ML model training across multiple nodes. Checkout Master Distributed Training on EKS for details 🌐
Seamlessly integrate AWS Deep Learning Containers with Amazon SageMaker's managed MLflow service to streamline your ML experiment tracking, model management, and deployment workflow. Checkout Level Up with SageMaker AI & MLflow for details 🔄
Deploy and serve Large Language Models efficiently on Amazon EKS using vLLM Deep Learning Containers for optimized inference performance and scalability. Checkout Deploy LLMs Like a Pro on EKS for details 🚀
Learn to fine-tune and deploy Meta's Llama 3.2 Vision model for AI-powered web automation by combining AWS DLCs, Amazon EKS, and Bedrock to enable visual understanding in your applications. Checkout Web Automation with Meta Llama 3.2 Vision for details 🎯
Discover how to simplify and accelerate your deep learning workflow by integrating AWS Deep Learning Containers with Amazon Q Developer and Model Context Protocol (MCP) for streamlined environment setup and management. Checkout Supercharge Your DL Environment for details ⚡

🎓 Hands-on Workshop

Learn how to deploy and optimize Large Language Models (LLMs) on Amazon EKS using vLLM Deep Learning Containers for high-performance inference at scale. Checkout the Workshop Guide and Sample Code for details 🚀

About

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

License

This project is licensed under the Apache-2.0 License.