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apache / mxnet / refs/tags/1.5.0
tag61a9f46a731f82320e2eb8ba7470c61ec8c045ef
taggerSheng Zha <zhasheng@amazon.com>Thu Jul 18 20:39:37 2019 -0700
object75a9e187d00a8b7ebc71412a02ed0e3ae489d91f 
1.5.0 Release
commit75a9e187d00a8b7ebc71412a02ed0e3ae489d91f[log] [tgz]
authorLai Wei <royweilai@gmail.com>Thu Jun 27 09:46:32 2019 -0700
committerSheng Zha <szha@users.noreply.github.com>Thu Jun 27 09:46:32 2019 -0700
treef47b733345ff64f0299a98df5206e6db70dfa8e6
parent4d9667121ae6fb643f2a02ab15e25231ed756cde [diff]
[backport 1.5.x]Fix Cached_op with static_shape=true (#15298) (#15380)

* Fix Cached_op with static_shape=true (#15298)

* Fix

* run ci

* trigger
2 files changed
tree: f47b733345ff64f0299a98df5206e6db70dfa8e6
  1. .github/
  2. 3rdparty/
  3. amalgamation/
  4. benchmark/
  5. ci/
  6. cmake/
  7. contrib/
  8. cpp-package/
  9. docker/
  10. docs/
  11. example/
  12. include/
  13. julia/
  14. make/
  15. matlab/
  16. perl-package/
  17. plugin/
  18. python/
  19. R-package/
  20. scala-package/
  21. setup-utils/
  22. src/
  23. tests/
  24. tools/
  25. .clang-tidy
  26. .codecov.yml
  27. .gitattributes
  28. .gitignore
  29. .gitmodules
  30. .mxnet_root
  31. .travis.yml
  32. appveyor.yml
  33. CMakeLists.txt
  34. CODEOWNERS
  35. CONTRIBUTORS.md
  36. dev_menu.py
  37. DISCLAIMER
  38. KEYS
  39. LICENSE
  40. Makefile
  41. mkldnn.mk
  42. MKLDNN_README.md
  43. NEWS.md
  44. NOTICE
  45. README.md
  46. readthedocs.yml
  47. snap.python
  48. snapcraft.yaml
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 more than a deep learning project. It is 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, Scala, C++, Java, Clojure, R, Go, Javascript, Perl, Matlab, 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.