This repository contains sample code and configurations for using AWS Deep Learning Containers (DLCs) in various scenarios. AWS Deep Learning Containers are Docker images pre-installed with deep learning frameworks and tools, optimized for performance on AWS infrastructure.
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vllm-samples/: Samples for deploying vLLM (a high-throughput serving engine for LLMs) using AWS Deep Learning Containers
- deepseek/: Samples for deploying DeepSeek models
- eks/: Configuration files and instructions for deploying DeepSeek models on Amazon EKS with GPU support, EFA, and FSx Lustre integration
- deepseek/: Samples for deploying DeepSeek models
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mlflow/: Samples for using SageMaker managed MLflow with Deep Learning Containers and Deep Learning AMIs
- dlc-with-mlflow/: Sample for integrating AWS DLCs with SageMaker managed MLflow for training. See README for detailed instructions.
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xgboost/: Samples for training XGBoost models using the SageMaker XGBoost Deep Learning Container
- fraud-detection-distributed/: Distributed multi-GPU fraud detection with XGBoost and Dask. See README.
AWS Deep Learning Containers provide optimized environments with pre-installed deep learning frameworks and tools:
- Performance Optimized: Tuned for maximum performance on AWS infrastructure
- Pre-configured: Ready-to-use environments with popular frameworks
- Regularly Updated: Latest versions of frameworks and security patches
- AWS Integration: Seamless integration with AWS services like EKS, ECS, and SageMaker
Learn more about AWS Deep Learning Containers.