Is PyTorch faster than MXNet or TensorFlow
Posted on: Mar 24, 2020
The AWS Deep Learning Containers are available today with the latest framework versions of TensorFlow (2.1.0 and 1.15.2), PyTorch 1.4.0 and MXNet 1.6.0. This release adds the Amazon SageMaker Python SDK to the containers. Updates have also been made to the Amazon SageMaker Experiments package. Amazon SageMaker Experiments is a feature of Amazon SageMaker. It enables sorting, tracking, comparison and evaluation of machine learning (ML) experiments and model versions. The training containers for TensorFlow 2.1.0 python3 now also include SageMaker Debugger. This feature allows data scientists to save and test model sensors as part of training assignments.
You can start the new versions of deep learning containers in Amazon SageMaker, Amazon Elastic Container Service for Kubernetes (Amazon EKS), self-managed Kubernetes, Amazon EC2 and Amazon Elastic Container Service (Amazon ECS). For full lists of frameworks, end-of-life announcements for certain features, and versions supported by the AWS Deep Learning Containers, see the PyTorch 1.4.0, MXNet 1.6.0, TensorFlow 2.1.0, and TensorFlow 1.15.2 Release Notes .
Please visit the AWS Marketplace for more details. A list of the available containers can be found in our documentation. Use our introductory guides and tutorials for beginners to advanced in our developer guide to quickly familiarize yourself with the AWS Deep Learning Containers. You can also register on our discussion forum to receive announcements and post your questions.
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