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Apache MXNet for Deep Learning

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Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines.

Apache MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Licensed under an Apache-2.0 license.

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Features

Contents

What's New

Ecosystem News

Stay Connected

ChannelPurpose
Follow MXNet Development on GithubSee what's going on in the MXNet project.
MXNet Confluence Wiki for Developers <i class="fas fa-external-link-alt">MXNet developer wiki for information related to project development, maintained by contributors and developers. To request write access, send an email to send request to the dev list <i class="far fa-envelope"></i>.
dev@mxnet.apache.org mailing listThe "dev list". Discussions about the development of MXNet. To subscribe, send an email to dev-subscribe@mxnet.apache.org <i class="far fa-envelope"></i>.
discuss.mxnet.io <i class="fas fa-external-link-alt"></i>Asking & answering MXNet usage questions.
Apache Slack #mxnet Channel <i class="fas fa-external-link-alt">Connect with MXNet and other Apache developers. To join the MXNet slack channel send request to the dev list <i class="far fa-envelope"></i>.
Follow MXNet on Social MediaGet updates about new features and events.

Social Media

Keep connected with the latest MXNet news and updates.

<p> <a href="https://twitter.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/twitter.svg?sanitize=true" height="30px"/> Apache MXNet on Twitter</a> </p> <p> <a href="https://medium.com/apache-mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/medium_black.svg?sanitize=true" height="30px"/> Contributor and user blogs about MXNet</a> </p> <p> <a href="https://reddit.com/r/mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/reddit_blue.svg?sanitize=true" height="30px" alt="reddit"/> Discuss MXNet on r/mxnet</a> </p> <p> <a href="https://www.youtube.com/apachemxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/youtube_red.svg?sanitize=true" height="30px"/> Apache MXNet YouTube channel</a> </p> <p> <a href="https://www.linkedin.com/company/apache-mxnet"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/social/linkedin.svg?sanitize=true" height="30px"/> Apache MXNet on LinkedIn</a> </p>

History

MXNet emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015