AWS Machine Learning Blog

Category: Apache MXNet on AWS*

Monitoring GPU Utilization with Amazon CloudWatch

Deep learning requires a large amount of matrix multiplications and vector operations that can be parallelized by GPUs (graphics processing units) because GPUs have thousands of cores. Amazon Web Services allows you to spin up P2 or P3 instances that are great for running Deep Learning frameworks such as MXNet, which emphasizes speeding up the deployment […]

Read More

Apache MXNet Release Adds Support for New NVIDIA Volta GPUs and Sparse Tensor

We are excited about the availability of Apache MXNet version 0.12. Contributors in the MXNet community have collaborated to bring new feature enhancements to users. With this release, MXNet adds two new important features: Support for NVIDIA Volta GPUs, which enable users to dramatically reduce the training and inference time for a neural network model. […]

Read More

Research Spotlight: BMXNet – An Open Source Binary Neural Network Implementation Based On MXNet

by Haojin Yang, Christian Bartz, Martin Fritzsche, and Christoph Meinel | on | in Apache MXNet on AWS* | Permalink | Comments |  Share

This is guest post by Haojin Yang, Martin Fritzsche, Christian Bartz, Christoph Meinel from the Hasso-Plattner-Institut, Potsdam Germany. We are excited to see research drive practical implementation of deep learning on low power devices. This work plays an important part in expanding powerful intelligent capabilities into our everyday lives. In recent years, deep learning technologies have […]

Read More

Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning

Today, AWS and Microsoft announced a new specification that focuses on improving the speed, flexibility, and accessibility of machine learning technology for all developers, regardless of their deep learning framework of choice. The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, […]

Read More

Introducing NNVM Compiler: A New Open End-to-End Compiler for AI Frameworks

You can choose among multiple artificial intelligence (AI) frameworks to develop AI algorithms. You also have a choice of a wide range of hardware to train and deploy AI models. The diversity of frameworks and hardware is crucial to maintaining the health of the AI ecosystem. This diversity, however, also introduces several challenges to AI […]

Read More

Build an Autonomous Vehicle on AWS and Race It at the re:Invent Robocar Rally

Autonomous vehicles are poised to take to our roads in massive numbers in the coming years. This has been made possible due to advances in deep learning and its application to autonomous driving. In this post, we take you through a tutorial that shows you how to build a remote control (RC) vehicle that uses […]

Read More

AWS Deep Learning AMI Now Includes Apache MXNet 0.11 and TensorFlow 1.3

The AWS Deep Learning Amazon Machine Image (AMI) is designed to help you build stable, secure, and scalable deep learning applications on AWS. The AMI comes pre-installed with popular deep learning frameworks. It has GPU drivers and libraries that let you train sophisticated AI models and scale them in the cloud. The latest release of […]

Read More

Bring Machine Learning to iOS apps using Apache MXNet and Apple Core ML

With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app. This enables developers to bring intelligent new features to users with just a few lines of code. Core ML makes machine learning more accessible to mobile developers. […]

Read More

Apple Core ML and Keras Support Now Available for Apache MXNet

We’re excited about the availability of Apache MXNet version 0.11. With this release, MXNet hit major milestones, both in terms of community development and as an incubating Apache project. Contributors—including developers from Apple, Samsung and Microsoft—committed code to this release. There are over 400 contributors on the project so far. The project has now fully […]

Read More