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forked from apache/mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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MXNet

Build Status Documentation Status

This is a project that combines lessons and ideas we learnt from cxxnet, minerva and purine2.

  • The interface is designed in collaboration by authors of three projects.
  • Nothing is yet working

Guidelines

  • Use google c style
  • Put module header in include
  • Depend on dmlc-core
  • Doxygen comment every function, class and variable for the module headers
  • Minimize dependency, if possible only depend on dmlc-core
  • Macro Guard CXX11 code by
    • Try to make interface compile when c++11 was not avaialable(but with some functionalities pieces missing)
#include <dmlc/base.h>
#if DMLC_USE_CXX11
  // c++11 code here
#endif
  • For heterogenous hardware support (CPU/GPU). Hope the GPU-specific component could be isolated easily. That is too say if we use USE_CUDA macro to wrap gpu-related code, the macro should not be everywhere in the project.

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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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  • C++ 31.2%
  • Python 28.1%
  • Jupyter Notebook 19.8%
  • Scala 9.4%
  • R 6.2%
  • Cuda 2.0%
  • Other 3.3%