One stop shop for running AI/ML on AWS
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______________________________________________________________________ ## ๐ฅ What's New ### ๐ Release Highlights - **[2026/03/10]** Released v0.17.0 [vLLM DLCs](https://gallery.ecr.aws/deep-learning-containers/vllm) - EC2/EKS/ECS: `public.ecr.aws/deep-learning-containers/vllm:0.17-gpu-py312-ec2` - SageMaker: `public.ecr.aws/deep-learning-containers/vllm:0.17-gpu-py312` - **[2026/03/09]** Released v0.5.9 [SGLang DLCs](https://gallery.ecr.aws/deep-learning-containers/sglang) - EC2/EKS/ECS: `public.ecr.aws/deep-learning-containers/sglang:0.5.9-gpu-py312-ec2` - SageMaker: `public.ecr.aws/deep-learning-containers/sglang:0.5.9-gpu-py312` - **[2026/03/09]** Released v0.16.0 [vLLM DLCs](https://gallery.ecr.aws/deep-learning-containers/vllm) - EC2/EKS/ECS: `public.ecr.aws/deep-learning-containers/vllm:0.16-gpu-py312-ec2` - SageMaker: `public.ecr.aws/deep-learning-containers/vllm:0.16-gpu-py312` - **[2025/11/17]** Released first [SGLang DLCs](https://gallery.ecr.aws/deep-learning-containers/sglang) - SageMaker: `public.ecr.aws/deep-learning-containers/sglang:0.5.5-gpu-py312` ### ๐ข 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](https://aws.github.io/deep-learning-containers/reference/support_policy/) ### ๐ Hot Off the Press - ๐ **[Master Distributed Training on Amazon EKS](https://aws.amazon.com/blogs/machine-learning/configure-and-verify-a-distributed-training-cluster-with-aws-deep-learning-containers-on-amazon-eks/)** \- Set up and validate a distributed training environment on Amazon EKS for scalable ML model training across multiple nodes. - ๐ **[Level Up with Amazon SageMaker AI & MLflow](https://aws.amazon.com/blogs/machine-learning/use-aws-deep-learning-containers-with-amazon-sagemaker-ai-managed-mlflow/)** \- Integrate AWS DLCs with Amazon SageMaker AI's managed MLflow service for streamlined experiment tracking and model management. - ๐ **[Deploy LLMs Like a Pro on Amazon EKS](https://aws.amazon.com/blogs/architecture/deploy-llms-on-amazon-eks-using-vllm-deep-learning-containers/)** \- Deploy and serve Large Language Models efficiently on Amazon EKS using vLLM Deep Learning Containers. - ๐ฏ **[Web Automation with Meta Llama 3.2 Vision](https://aws.amazon.com/blogs/machine-learning/fine-tune-and-deploy-meta-llama-3-2-vision-for-generative-ai-powered-web-automation-using-aws-dlcs-amazon-eks-and-amazon-bedrock/)** \- Fine-tune and deploy Meta's Llama 3.2 Vision model for AI-powered web automation. - โก **[Supercharge Your DL Environment](https://aws.amazon.com/blogs/machine-learning/streamline-deep-learning-environments-with-amazon-q-developer-and-mcp/)** \- Integrate AWS DLCs with Amazon Q Developer and Model Context Protocol (MCP). ### ๐ Hands-on Workshop - ๐ **[LLM Deployment on Amazon EKS Workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/c22b50fb-64b1-4e18-8d0f-ce990f87eed3/en-US)** - Deploy and optimize LLMs on Amazon EKS using vLLM Deep Learning Containers. For more information, see [Sample Code](https://github.com/aws-samples/sample-vllm-on-eks-with-dlc) ______________________________________________________________________ ## 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. ### ๐ฏ 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 ### ๐ช 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 ### ๐ฎ Perfect For - Data Scientists building and training models - ML Engineers deploying production workloads - DevOps teams managing ML infrastructure - Researchers exploring cutting-edge AI capabilities ### ๐ 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](https://aws.github.io/deep-learning-containers/security/). ______________________________________________________________________ ## Quick Links - [Getting Started](https://aws.github.io/deep-learning-containers/get_started/) - Get up and running in minutes - [Tutorials](https://aws.github.io/deep-learning-containers/tutorials/) - Step-by-step guides - [Available Images](https://aws.github.io/deep-learning-containers/reference/available_images/) - Browse all container images - [Support Policy](https://aws.github.io/deep-learning-containers/reference/support_policy/) - Framework versions and timelines - [Security](https://aws.github.io/deep-learning-containers/security/) - Security policy ## Getting Help - [GitHub Issues](https://github.com/aws/deep-learning-containers/issues) - Report bugs or request features ## License This project is licensed under the Apache-2.0 License.