AWS Machine Learning Blog

Tag: PyTorch

Reduce deep learning training time and cost with MosaicML Composer on AWS

In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. […]

A personalized ‘shop-by-style’ experience using PyTorch on Amazon SageMaker and Amazon Neptune

Remember the screech of the dial-up and plain-text websites? It was in that era that the website launched in the summer of 1995. Like the rest of the web, has gone through a digital experience makeover that includes slick web controls, rich media, multi-channel support, and intelligent content placement. Nonetheless, there are certain […]

Deploying PyTorch inference with MXNet Model Server

Training and inference are crucial components of a machine learning (ML) development cycle. During the training phase, you teach a model to address a specific problem. Through this process, you obtain binary model files ready for use in production. For inference, you can choose among several framework-specific solutions for model deployment, such as TensorFlow Serving […]

PyTorch 1.0 preview now available in Amazon SageMaker and the AWS Deep Learning AMIs

Amazon SageMaker and the AWS Deep Learning AMIs (DLAMI) now provide an easy way to evaluate the PyTorch 1.0 preview release. PyTorch 1.0 adds seamless research-to-production capabilities, while retaining the ease-of-use that has enabled PyTorch to rapidly gain popularity. The AWS Deep Learning AMI comes pre-built with PyTorch 1.0, Anaconda, and Python packages, with CUDA and […]

Amazon SageMaker now supports PyTorch and TensorFlow 1.8

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]

AWS Deep Learning AMIs now support Chainer and latest versions of PyTorch and Apache MXNet

The AWS Deep Learning AMIs provide fully-configured environments so that artificial intelligence (AI) developers and data scientists can quickly get started with deep learning models. The Amazon Machine Images (AMIs) now include Chainer (v3.4.0), a flexible and intuitive deep learning (DL) framework, as well as the latest versions of Apache MXNet and PyTorch. The Chainer define-by-run […]

Updated AWS Deep Learning AMIs: New Versions of TensorFlow, Apache MXNet, Keras, and PyTorch

We’re excited to update the AWS Deep Learning AMIs with significantly faster training on NVIDIA Tesla V100 “Volta” GPUs across many frameworks, including TensorFlow, PyTorch, Keras, and the latest Apache MXNet 1.0 release. There are two main flavors of the AMIs available today. The Conda-based AWS Deep Learning AMI packages the latest point releases of […]

AWS Deep Learning AMI Now Supports PyTorch, Keras 2 and Latest Deep Learning Frameworks

Today, we’re pleased to announce an update to the AWS Deep Learning AMI. The AWS Deep Learning AMI, which lets you spin up a complete deep learning environment on AWS in a single click, now includes PyTorch, Keras 1.2 and 2.0 support, along with popular machine learning frameworks such as TensorFlow, Caffe2 and Apache MXNet. […]