MXNet R installation with GPU support on Windows

r_install_gpu_win.md

MXNet R installation with GPU support on Windows

Clone the MXNet github repo

git clone --recursive https://github.com/dmlc/mxnet

The --recursive is to clone all the submodules used by MXNet.

Download prebuilt shared libraries for Windows.

Please download mxnet_x64_vc14_gpu.7z and prebuildbase_win10_x64_vc14.7z from https://github.com/yajiedesign/mxnet/releases

Download cuDNN from NVIDIA website

Please download cuDNN from https://developer.nvidia.com/cudnn

Add necessary dlls into the R-package folder

1. Please mkdir the folder structure: ./R-package/inst/libs/x64;

2. Copy the following dlls into ./R-package/inst/libs/x64:

cublas64_80.dll
cudart64_80.dll
cudnn64_5.dll
curand64_80.dll
libgcc_s_seh-1.dll
libgfortran-3.dll
libmxnet.dll
libmxnet.lib
libopenblas.dll
libquadmath-0.dll
nvrtc64_80.dll

These dlls can be found in prebuildbase_win10_x64_vc14/3rdparty/cudart, prebuildbase_win10_x64_vc14/3rdparty/openblas/bin, mxnet_x64_vc14_gpu/build, mxnet_x64_vc14_gpu/lib and the cuDNN downloaded from NVIDIA.

Add necessary header files into the R-package folder

1. Please mkdir the folder structure: ./R-package/inst/include;

2. Copy the header files from dmlc, mxnet and nnvm into ./R-package/inst/include. It should look like:

./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    └── nnvm

Build the R package using Rtools

Now please open the Windows CMD and change the directory to the mxnet folder. Then use the following commands to build R package:

echo import(Rcpp) > R-package\NAMESPACE
echo import(methods) >> R-package\NAMESPACE
Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
cd R-package
Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
cd ..

R CMD INSTALL --no-multiarch R-package

Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
rm R-package/NAMESPACE
Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"

R CMD INSTALL --build --no-multiarch R-package

EDIT: Solved. managed to install it with the prebuilt GPU package, problem was CUDA version was not up to date.

Hello, thank you for the instructions, unfortunately not working for me on Windows 10, R 3.4.1, Rtools 3.4 (tried also Rtools 3.3 and 3.2).

As soon as it begins, compilation fails with this message:
In file included from executor.cc:9:0: ./base.h:12:23: fatal error: dmlc/base.h: No such file or directory
#include <dmlc/base.h> ^
compilation terminated.

I would be be grateful to get any hint on what the problem can be.

The above method does not work. I get an error at this point:

C:\Users\aliha\mxnet>R CMD INSTALL --no-multiarch R-package
`In R CMD INSTALL

• installing to library 'C:/Users/aliha/Documents/R/win-library/3.4'
• installing source package 'mxnet' ...
  ** libs
  Warning: running command 'make -f "Makevars.win" -f "C:/PROGRA1/R/R-3.4.3/etc/x64/Makeconf" -f "C:/PROGRA1/R/R-3.4.3/share/make/winshlib.mk" SHLIB_LDFLAGS='$(SHLIB_CXXLDFLAGS)' SHLIB_LD='$(SHLIB_CXXLD)' SHLIB="mxnet.dll" WIN=64 TCLBIN=64 OBJECTS="executor.o export.o im2rec.o io.o kvstore.o mxnet.o ndarray.o symbol.o"' had status 127
  ERROR: compilation failed for package 'mxnet'
• removing 'C:/Users/aliha/Documents/R/win-library/3.4/mxnet'

@thirdwing
Would it be possible to include how you installed opencv in your Windows Server session?
There seems to be some ambiguity as to the correct way.
#8936

@alihashmiii
Your particular issue seems to be that the c++ compiler is not found, more specifically, the Rtools is not set on your path correctly (status 127) and isn't mxnet bug.

If you are looking for a specific cuda version for your installation you might have some luck copying the *.dll and *.lib files from a python installation.
For instance, if you wanted the cuda 9.1 build (which is not available from https://github.com/yajiedesign/mxnet/releases upon writing this) you could:

pip install mxnet-cu91

If you don't know where pip installs packages you can:

pip show mxnet-cu91

You would then go to the directory indicated by "Location:" and find the "mxnet" dir.
In my case it was: "C:\Python\Python36\Lib\site-packages\mxnet"
You would then follow the steps from the guide, but using the *.dll and *.lib from the python installation when possible.