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All Projects → apache → Incubator Mxnet

apache / Incubator Mxnet

Licence: apache-2.0
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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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.

Branch Build Status
master CentOS CPU Build Status CentOS GPU Build Status Clang Build Status
Edge Build Status Miscellaneous Build Status Sanity Build Status
Unix CPU Build Status Unix GPU Build Status Website Build Status
Windows CPU Build Status Windows GPU Build Status Documentation Status
v1.x CentOS CPU Build Status CentOS GPU Build Status Clang Build Status
Edge Build Status Miscellaneous Build Status Sanity Build Status
Unix CPU Build Status Unix GPU Build Status Website Build Status
Windows CPU Build Status Windows GPU Build Status Documentation Status

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

Channel Purpose
Follow MXNet Development on Github See 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 .
[email protected] mailing list The "dev list". Discussions about the development of MXNet. To subscribe, send an email to [email protected] .
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 Media Get updates about new features and events.

Social Media

Keep connected with the latest MXNet news and updates.

Apache MXNet on Twitter

Contributor and user blogs about MXNet

reddit Discuss MXNet on r/mxnet

Apache MXNet YouTube channel

Apache MXNet on LinkedIn

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

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