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commitf25b92ed94bd46492b903a0b2ace29be78b3f944[log] [tgz]
authorZhenghui Jin <69359374+barry-jin@users.noreply.github.com>Wed Sep 29 12:11:37 2021 -0700
committerGitHub <noreply@github.com>Wed Sep 29 15:11:37 2021 -0400
tree4670146616aba1fd89c63cca3e96a4b5426ae406
parentab222114afb234bce6ebffb99b09e2b16d2ddcec [diff]
[CI] Add Simple GitHub-Action Based License Checker (#20617)

Remove ASF license headers on 3rdparty files based on discussion:

> It depends on what you mean by significant, usually changes made to 3rd party files stay under the original license. See [1] on this, note that "Do not add the standard Apache License header to the top of third-party source files.”. If the license was to change, and the headers replaced then that needs to be noted in NOTICE. Please make sure that the license is noted in LICENSE.

https://lists.apache.org/thread.html/r4010c53f976819c94f3450469db8b936f7c15cb046a61f0d488674d3%40%3Cgeneral.incubator.apache.org%3E
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README.md

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Apache MXNet (incubating) for Deep Learning

GitHub release (latest SemVer) GitHub stars GitHub forks GitHub contributors GitHub issues good first issue GitHub pull requests by-label GitHub license Twitter Twitter Follow

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.

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

  • NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
  • Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
  • Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
  • Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
  • Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
  • Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
  • Cloud-friendly and directly compatible with AWS and Azure.

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 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 .
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 .
discuss.mxnet.io Asking & answering MXNet usage questions.
Apache Slack #mxnet Channel Connect with MXNet and other Apache developers. To join the MXNet slack channel send request to the dev list .
Follow MXNet on Social MediaGet updates about new features and events.

Social Media

Keep connected with the latest MXNet news and updates.

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