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apache / mxnet / 3480ba2c6df02bb907d3a975d354efa8697c4e71
commit3480ba2c6df02bb907d3a975d354efa8697c4e71[log] [tgz]
authorZhaoqi Zhu <zhaoqizhu96@gmail.com>Fri Jul 16 14:30:24 2021 -0700
committerGitHub <noreply@github.com>Fri Jul 16 14:30:24 2021 -0700
treed872333f8c95a2b7481a280ca576af6a1c19318c
parent8fd17cef2ee854239c6e66f2dd3b9467aa222f79 [diff]
[ONNX] Foward port new mx2onnx into master (#20355)

* initial: forward port mx2onnx and remove onnx2mx

* fix sanity

* add onnx operator unit tests

* add test file

* add model test

* fix license & doc

* fix

* marching toward 2.0

* fix typo

* add more ops

* more ops

* more ops

* more ops

* fix softmax and sanity

* more ops

* more ops

* more ops

* naming

* more ops

* more ops

* more ops and bug fix

* more ops and skip unvisited tests

* fix sanity

* fix for onnx18

* more ops

* fix

* fix onnx 18

* more ops

* skip model test

* update read me

* more ops

* more ops

* more ops

* more ops

* more ops

* Update test_models.py
29 files changed
tree: d872333f8c95a2b7481a280ca576af6a1c19318c
  1. .github/
  2. 3rdparty/
  3. benchmark/
  4. cd/
  5. ci/
  6. cmake/
  7. config/
  8. contrib/
  9. cpp-package/
  10. docker/
  11. docs/
  12. example/
  13. include/
  14. licenses/
  15. plugin/
  16. python/
  17. src/
  18. tests/
  19. tools/
  20. .asf.yaml
  21. .clang-tidy
  22. .codecov.yml
  23. .gitattributes
  24. .gitignore
  25. .gitmodules
  26. .mxnet_root
  27. CMakeLists.txt
  28. CODE_OF_CONDUCT.md
  29. CODEOWNERS
  30. conftest.py
  31. CONTRIBUTORS.md
  32. DISCLAIMER
  33. doap.rdf
  34. KEYS
  35. LICENSE
  36. MKLDNN_README.md
  37. NEWS.md
  38. NOTICE
  39. pytest.ini
  40. rat-excludes
  41. README.md
  42. readthedocs.yml
  43. SECURITY.md
  44. snap.python
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