Updates to AWS Deep Learning Containers with Amazon Elastic Inference for TensorFlow and PyTorch & Training and Inference For TensorFlow

Posted on: May 11, 2020

The AWS Deep Learning Containers for Elastic Inference are available today with the framework versions PyTorch 1.3.1, TensorFlow 1.15.0, and TensorFlow 2.0.0. The PyTorch 1.3.1 upgrade includes the newly added SageMaker Inference and SageMaker PyTorch Inference. The TensorFlow 1.15.0 and TensorFlow 2.0.0 upgrades include the latest versions of TensorFlow Model Server for use with Elastic Inference. You can launch the new versions of the Deep Learning Containers on Amazon SageMaker, on Amazon EC2, and on Amazon Elastic Container Service (Amazon ECS). For a complete list of packages and versions supported by these Deep Learning Containers, see the release notes.

The AWS Deep Learning Containers with Amazon Elastic Inference (EI) with PyTorch and TensorFlow allow you to run inference calls on PyTorch 1.3.1, TensorFlow 1.15.0, and TensorFlow 2.0.0 on Elastic Inference Accelerators. Amazon EI allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances and Amazon ECS tasks to reduce the cost of running deep learning inference by up to 75%. These Docker images have been tested with Amazon SageMaker, EC2, and ECS. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices. 

The AWS Deep Learning Containers for Training and Inference are available with the latest framework versions of TensorFlow 1.15.2 and 2.1.0. The TensorFlow upgrades includes the latest version of SMDebug, Sagemaker-tensorflow-training and Sagemaker-container. You can launch the new versions of the Deep Learning Containers on Amazon SageMaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). For a complete list of frameworks and versions supported by the AWS Deep Learning Containers, see the release notes.

The AWS Deep Learning Containers for TensorFlow include containers for training on CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with Amazon SageMaker, EC2, ECS, and EKS, and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, and other required software components to provide a seamless user experience for deep learning workloads. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices. 

More details can be found in the marketplace, and a list of available containers can be found in our documentation. Get started quickly with the AWS Deep Learning Containers using the getting-started guides and beginner to advanced level tutorials in our developer guide. You can also subscribe to our discussion forum to get launch announcements and post your questions.