Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
lihaofd and TaoLv Support Quantized Fully Connected by INT8 GEMM (#12922)
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* disable qfc cpu case since s8u8s32 is only supported by MKL BLAS library

* retrigger to ci testing

* move implementation to cc file and add  STORAGE_TYPE_ASSIGN_CHECK

* fix typo bug

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Latest commit 1eb3344 Dec 14, 2018
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amalgamation Fix #12672, importing numpy scalars (zero-dimensional arrays) (#12678) Oct 4, 2018
benchmark/python Add more models to benchmark_score (#12780) Oct 21, 2018
ci [MXNET-1251] Basic configuration to do static-linking (#13621) Dec 15, 2018
cmake Improve dev_menu usability, local build and virtualenv (#13529) Dec 13, 2018
contrib/clojure-package [Clojure] Correct the versions in the README so they correspond to th… Dec 13, 2018
cpp-package [MXNET-1083] Add the example to demonstrate the inference workflow us… Dec 15, 2018
docker [MXNET-951] Python dockerfiles built on pip binaries and build/releas… Sep 29, 2018
docs Complimentary gluon DataLoader improvements (#13606) Dec 14, 2018
example Update MXNetTutorialTemplate.ipynb (#13568) Dec 8, 2018
include Update version to v1.5.0 including clojure package (#13566) Dec 7, 2018
julia updated reference to Apache MXNet (#13645) Dec 14, 2018
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matlab Adding Apache Header to .m, .cfg, R and .mk files (#9499) Jan 22, 2018
perl-package CudnnFind() usage improvements (#12804) Oct 26, 2018
plugin add input argument in warpctc layer (#11167) Jun 6, 2018
python Complimentary gluon DataLoader improvements (#13606) Dec 14, 2018
scala-package [MXNET-1195] Cleanup Scala README file (#13582) Dec 15, 2018
setup-utils Update expected result in osx python install script (#10842) May 10, 2018
src Support Quantized Fully Connected by INT8 GEMM (#12922) Dec 15, 2018
tests Support Quantized Fully Connected by INT8 GEMM (#12922) Dec 15, 2018
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.clang-tidy [MXNET-860] Remove std::moves that have no affect (#12730) Oct 4, 2018
.codecov.yml Enable C++ coverage (#12642) Sep 24, 2018
.gitattributes [R] To ignore R-pkg when releasing on github (#7007) Jul 13, 2017
.gitignore Improve CCache handling (#13456) Dec 14, 2018
.gitmodules [MXNET-703] TensorRT runtime integration (#11325) Aug 10, 2018
.mxnet_root CI docker revamp; Add Jetson, Raspberry and CentOS 7 build [MXNET-42]… Mar 9, 2018
.travis.yml Disable travis tests (#13137) Nov 6, 2018
CMakeLists.txt Revert "Feature/mkldnn static (#13628)" (#13638) Dec 14, 2018
CODEOWNERS add gigasquid (Carin Meier) to the Clojure language binding (#12198) Aug 21, 2018
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dev_menu.py Improve dev_menu usability, local build and virtualenv (#13529) Dec 13, 2018
mkldnn.mk Revert "Feature/mkldnn static (#13628)" (#13638) Dec 14, 2018
readthedocs.yml [docs] add favicon and fix index html title Mar 25, 2016
snap.python Add snapcraft packaging (#4852) Mar 23, 2017
snapcraft.yaml Update version to v1.5.0 including clojure package (#13566) Dec 7, 2018

README.md


Apache MXNet (incubating) for Deep Learning

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Apache MXNet (incubating) 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, scaling effectively to multiple GPUs and multiple machines.

MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

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How to Contribute

What's New

Contents

Features

  • Design notes providing useful insights that can re-used by other DL projects
  • Flexible configuration for arbitrary computation graph
  • Mix and match imperative and symbolic programming to maximize flexibility and efficiency
  • Lightweight, memory efficient and portable to smart devices
  • Scales up to multi GPUs and distributed setting with auto parallelism
  • Support for Python, R, Scala, C++ and Julia
  • Cloud-friendly and directly compatible with S3, HDFS, and Azure

License

Licensed under an Apache-2.0 license.

Reference Paper

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

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.