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apache / mxnet / 0f4be44eccca029f136f178b11ba2d5b93ba764a
commit0f4be44eccca029f136f178b11ba2d5b93ba764a[log] [tgz]
authorManu Seth <22492939+mseth10@users.noreply.github.com>Fri Feb 26 19:35:16 2021 -0800
committerLeonard Lausen <lausen@amazon.com>Thu Mar 25 00:33:24 2021 +0000
treeab6b30defd2ce32810125983588cb0f7b9046073
parentd82d19f700baa77ae577906f595b8562749852e8 [diff]
Fix nightly CD for python docker image releases (#19772)

* install wget

* test cd docker in ci

* install docker

* install python3-dev and gcc

* remove docker testing from ci

* remove python3-dev

* ecr target

* skip build test

* adding back python3-dev for make

* remove dynamic and pypi stages for testing

* install build-essential

* install zlib

* update python version

* update ld library path

* install openssl

* update test packages for python3.7

* remove call to deleted safe_docker_run.py

* hardcode region for public ecr repo

* use deadsnakes to install python

* revert dependency change

* refactor ecr login

* update ecr repo jenkins global var

* cleanup

* update docker authentication

* add ecr repo

* add back pypi and tests

* remove unused libmxnet pipeline

* update cu112 base docker

* update base docker images to ub18
9 files changed
tree: ab6b30defd2ce32810125983588cb0f7b9046073
  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

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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