Updates to AWS Deep Learning Containers for PyTorch 1.4.0 and MXNet 1.6.0
The AWS Deep Learning Containers are available with the latest framework versions of PyTorch 1.4.0 and MXNet 1.6.0. The PyTorch 1.4.0 upgrade includes newly added SageMaker Inference, SageMaker PyTorch Inference, and the latest version of SageMaker PyTorch Training. The MXNet 1.6.0 upgrade includes the latest version of GluonCV, SageMaker MXNet Training, SageMaker Inference and SageMaker MXNet Inference. 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 for PyTorch 1.4.0 and MXNet 1.6.0.
The AWS Deep Learning Containers are available with the latest framework versions of PyTorch 1.4.0 and MXNet 1.6.0. The PyTorch 1.4.0 upgrade includes newly added SageMaker Inference, SageMaker PyTorch Inference, and the latest version of SageMaker PyTorch Training. The MXNet 1.6.0 upgrade includes the latest version of GluonCV, SageMaker MXNet Training, SageMaker Inference and SageMaker MXNet Inference. 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 for PyTorch 1.4.0 and MXNet 1.6.0.
The AWS Deep Learning Containers for PyTorch and MXNet 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.