[DO NOT REVIEW] Subgraph API (#12104)

* Initial commit

* Add unit tests

* Fix lint

* Fix lint

* Clean up

* Add graph partitiong to Bind

* Add property name to graph partitioning c api

* Fix unit test gpu context

* Address cr

* Move subgraph to attrs.subgraphs and fix the example

* Fix lint

* Add var version unit test

* Address cr

* Enable unit test that was flaky
21 files changed
tree: 0f624f26212e7bc95c347f95e928fc24e2f67ff5
  1. .codecov.yml
  2. .gitattributes
  3. .github/
  4. .gitignore
  5. .gitmodules
  6. .mxnet_root
  7. .travis.yml
  8. 3rdparty/
  9. CMakeLists.txt
  13. Jenkinsfile
  14. KEYS
  17. Makefile
  18. NEWS.md
  19. NOTICE
  20. R-package/
  21. README.md
  22. amalgamation/
  23. appveyor.yml
  24. benchmark/
  25. ci/
  26. cmake/
  27. contrib/
  28. cpp-package/
  29. docker/
  30. docs/
  31. example/
  32. include/
  33. make/
  34. matlab/
  35. mkldnn.mk
  36. perl-package/
  37. plugin/
  38. python/
  39. readthedocs.yml
  40. scala-package/
  41. setup-utils/
  42. snap.python
  43. snapcraft.yaml
  44. src/
  45. tests/
  46. tools/

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 also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Join the chat at https://gitter.im/dmlc/mxnet

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, R, Scala, C++ and Julia
  • Cloud-friendly and directly compatible with S3, HDFS, and Azure

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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.