Prebuilt Amazon SageMaker Docker Images for TensorFlow, MXNet, Chainer, and PyTorch

Amazon SageMaker provides prebuilt Docker images that include deep learning framework libraries and other dependencies needed for training and inference. With the Amazon SageMaker SageMaker Python SDK, you can train and deploy models using these popular deep learning frameworks. For instructions on installing and using the SDK, see Amazon SageMaker Python SDK.

The following table provides links to the GitHub repositories that contain the source code and Dockerfiles for each framework and for TensorFlow and MXNet Serving. The instructions linked are for using the Python SDK to run training algorithms and host models on Amazon SageMaker.

Framework	Prebuilt Docker Image Source Code	Instructions
TensorFlow	Amazon SageMaker TensorFlow Containers Amazon SageMaker TensorFlow Serving Containers	Using TensorFlow with the SageMaker Python SDK
MXNet	Amazon SageMaker MXNet Containers Amazon SageMaker MXNet Serving Containers	Using MXNet with the SageMaker Python SDK
PyTorch	Amazon SageMaker PyTorch Containers Amazon SageMaker PyTorch Serving Containers	Using PyTorch with the SageMaker Python SDK
Chainer	Amazon SageMaker Chainer SageMaker Containers	Using Chainer with the SageMaker Python SDK

If you are not using the Amazon SageMaker Python SDK and one of its estimators to retrieve the pre-built images, you have to retrieve them yourself. The Amazon SageMaker prebuilt Docker images are stored in Amazon Elastic Container Registry (Amazon ECR). For a complete list of the available pre-built Docker images, see Deep Learning Containers Images.

Amazon SageMaker also provides prebuilt Docker images for scikit-learn and Spark ML. For information about Docker images that enable using scikit-learn and Spark ML solutions in Amazon SageMaker, see Prebuilt Amazon SageMaker Docker Images for Scikit-learn and Spark ML .

You can use prebuilt containers to deploy your custom models or models that have been trained in a framework other than Amazon SageMaker. For an overview of the process of bringing the trained model artifacts into Amazon SageMaker and hosting them at an endpoint, see Bring Your Own Pretrained MXNet or TensorFlow Models into Amazon SageMaker.

You can customize these prebuilt containers or extend them to handle any additional functional requirements for your algorithm or model that the prebuilt Amazon SageMaker Docker image doesn't support. For an example, see Extending Our PyTorch Containers.