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AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. This project is licensed under the Apache-2.0 License. Ensure you have access to an AWS account i.e. setup your environment such that awscli can access your account via either an IAM user or an IAM role.
Features
- Run training and inference on Deep Learning Containers for Amazon EC2 using MXNet, PyTorch, TensorFlow, and TensorFlow 2
- Deep Learning Containers for Amazon EKS offer CPU, GPU
- AWS Deep Learning Containers are available as Docker images in Amazon ECR
- Each Docker image is built for training or inference on a specific Deep Learning framework version
- Add artifacts to your build context
- Run tests locally
Project Samples
