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apache / mxnet / afc76b0f82839dfe00f7cab04be6de3df94564fe
commitafc76b0f82839dfe00f7cab04be6de3df94564fe[log] [tgz]
authorLeonard Lausen <lausen@amazon.com>Thu Dec 03 19:58:14 2020 -0700
committerGitHub <noreply@github.com>Thu Dec 03 18:58:14 2020 -0800
treeed1aadd4c2e0cb5b19e45ff72a761ba9682c2bb9
parent619eab2657d10884a3db6d28d3bc10e14fd3534c [diff]
Update Ubuntu images used on CI to 20.04 (#19588)

* Update Ubuntu images used on CI to 20.04

This helps ensure MXNet to work well on recent Linux distributions (while ensuring it continues to work well on ancient distributions based on the CentOS7 CI pipeline)

* Preserve Ubuntu 18.04 images for TensorRT pipeline as NVidia failed to make TensorRT available for Ubuntu 20.04

* Temporarily disable NVML on CI

[2020-12-03T18:33:10.380Z] OSError: /work/mxnet/python/mxnet/../../build/libmxnet.so: undefined symbol: nvmlDeviceGetComputeRunningProcesses_v2
10 files changed
tree: ed1aadd4c2e0cb5b19e45ff72a761ba9682c2bb9
  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. README.md
  40. readthedocs.yml
  41. SECURITY.md
  42. 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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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