Category: Apache MXNet on AWS

Earlier this week, AWS announced the availability of Model Server for Apache MXNet, an open source component built on top of Apache MXNet for serving deep learning models. Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. With Model Server for Apache MXNet, engineers are […]

Today, Amazon Web Services (AWS), Facebook and Microsoft are pleased to announce that the Open Neural Network Exchange (ONNX) format is production ready. ONNX is an open standard format for deep learning models that enables interoperability between deep learning frameworks such as Apache MXNet, Caffe2, Microsoft Cognitive Toolkit, and PyTorch. ONNX 1.0 enables users to […]

Today AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine and the introduction of a new model serving capability for MXNet. These new capabilities (1) simplify training and deploying deep learning models, (2) enable implementation of cutting-edge performance enhancements, and (3) provide easy interoperability between deep learning frameworks. In […]

In this blog post we demonstrate how to run distributed offline inference on large datasets using Apache MXNet (incubating) and Apache Spark on Amazon EMR. We explain how offline inference is useful, why it is challenging, and how you can leverage MXNet and Spark on Amazon EMR to overcome these challenges. Distributed inference on large […]

Today, AWS is excited to announce support for Apache MXNet and new generation GPU instance types on Amazon EMR, which enables you to run distributed deep neural networks alongside your machine learning workflows and big data processing. Additionally, you can install and run custom deep learning libraries on your EMR clusters with GPU hardware. Through […]

In the first blog post of our autonomous vehicle series, you built your Donkey vehicle and deployed your pilot server onto an Amazon EC2 instance. In the second blog post, you learned to drive the Donkey car, and the Donkey car learned to self-drive. In the third blog post, you learned about the process of streaming telemetry […]

Today, AWS announces the availability of ONNX-MXNet, an open source Python package to import Open Neural Network Exchange (ONNX) deep learning models into Apache MXNet. MXNet is a fully featured and scalable deep learning framework that offers APIs across popular languages such as Python, Scala, and R. With ONNX format support for MXNet, developers can […]

Last week, the Apache MXNet community released version 0.12 of MXNet. The major features were support for NVIDIA Volta GPUs and sparse tensors. The release also included a number of new features for the Gluon programming interface. In particular, these features make it easier to implement cutting-edge research in your deep learning models: Variational dropout, […]

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 […]

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. […]