Google Git
Sign in
apache / mxnet / refs/heads/v2.0.0.alpha
commit9ed058202ac1f299a1b11caf74c2a719650bf89f[log] [tgz]
authorJoe Evans <github@250hacks.net>Fri Feb 19 13:07:06 2021 -0800
committerLeonard Lausen <lausen@amazon.com>Tue Mar 30 21:19:21 2021 +0000
treedddc0b1118a3502c82aba6b811ceacaa423ad453
parent0f4be44eccca029f136f178b11ba2d5b93ba764a [diff]
CI Infra updates (#19903)

This PR makes a number of changes to make it more stable:

    Remove SafeDocker client which uses python docker package to run containers. Change to use "docker run" command directly using subprocess.call(), because the python-docker client does not support a gpus parameter which newer docker versions use and we don't get timeout issues when using the docker command directly. This will allow us to update our AMIs to use newer docker versions.
    In order to support both docker variants simultaneously, we first try to use the --gpus all parameter to docker run, if it fails with error code 125 (which means docker run command itself failed,) then we retry using the old --runtime nvidia parameter.
    Remove the extra custom codecov calls (which usually fail and have to retry multiple times, even though the initial codecov command works.)
4 files changed
tree: dddc0b1118a3502c82aba6b811ceacaa423ad453
  1. .github/
  2. 3rdparty/
  3. benchmark/
  4. cd/
  5. ci/
  6. cmake/
  7. config/
  8. contrib/
  9. docker/
  10. docs/
  11. example/
  12. include/
  13. licenses/
  14. plugin/
  15. python/
  16. src/
  17. tests/
  18. tools/
  19. .asf.yaml
  20. .clang-tidy
  21. .codecov.yml
  22. .gitattributes
  23. .gitignore
  24. .gitmodules
  25. .mxnet_root
  26. CMakeLists.txt
  27. CODE_OF_CONDUCT.md
  28. CODEOWNERS
  29. conftest.py
  30. CONTRIBUTORS.md
  31. DISCLAIMER-WIP
  32. doap.rdf
  33. KEYS
  34. LICENSE
  35. MKLDNN_README.md
  36. NEWS.md
  37. NOTICE
  38. pytest.ini
  39. rat-excludes
  40. README.md
  41. readthedocs.yml
  42. SECURITY.md
  43. snap.python
README.md

banner

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.

BranchBuild Status
masterCentOS 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.xCentOS 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

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