AWS Machine Learning Blog

Category: Apache MXNet on AWS*

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 […]

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Introducing Model Server for Apache MXNet

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 […]

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Announcing the Availability of ONNX 1.0

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 […]

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AWS Contributes to Milestone 1.0 Release of Apache MXNet Including the Addition of a New Model Serving Capability

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 […]

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Distributed Inference Using Apache MXNet and Apache Spark on Amazon EMR

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 […]

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Run Deep Learning Frameworks with GPU Instance Types on Amazon EMR

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 […]

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Building an Autonomous Vehicle, Part 4:  Using Behavioral Cloning with Apache MXNet for Your Self-Driving Car

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 […]

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Announcing ONNX Support for Apache MXNet

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 […]

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Apache MXNet Version 0.12 Extends Gluon Functionality to Support Cutting Edge Research

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, […]

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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 […]

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