[MXNET-1229] use OpenBLAS, lapack & OpenCV from conan (#13400)

* - use OpenBLAS, lapack & OpenCV from conan

Signed-off-by: SSE4 <tomskside@gmail.com>

* - add license to the conanfile.py

Signed-off-by: SSE4 <tomskside@gmail.com>

* - remove lapack (OpenBLAS provides it)

* - remove lapack (OpenBLAS provides it)

* - remove lapack (OpenBLAS provides it)

* - add option for conan

* Update CMakeLists.txt
3 files changed
tree: 86d292449cfbf20ba9e8ada2d8360f244e66e30e
  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
  35. conanfile.py
  37. dev_menu.py
  39. KEYS
  41. Makefile
  42. mkldnn.mk
  44. NEWS.md
  45. NOTICE
  46. README.md
  47. readthedocs.yml
  48. snap.python
  49. snapcraft.yaml

Apache MXNet (incubating) for Deep Learning

Build StatusDocumentation StatusGitHub license


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.

Ask Questions

How to Contribute

What's New



  • 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


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


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.