ankkhedia commented on a change in pull request #11805: [MXNET-641] fix R
windows install docs
URL: https://github.com/apache/incubator-mxnet/pull/11805#discussion_r204512013
##########
File path: docs/install/windows_setup.md
##########
@@ -114,81 +114,138 @@ For CPU-only package:
install.packages("mxnet")
```
-For GPU-enabled package:
+#### Building MXNet from Source Code(CPU)
+1. Clone the MXNet github repo.
-```r
- cran <- getOption("repos")
- cran["dmlc"] <-
"https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU";
- options(repos = cran)
- install.packages("mxnet")
+```sh
+git clone --recursive https://github.com/dmlc/mxnet
```
-#### Building MXNet from Source Code
+The `--recursive` is to clone all the submodules used by MXNet. You will be
editing the ```"/mxnet/R-package"``` folder.
+2. Download prebuilt GPU-enabled MXNet libraries for Windows from [Windows
release](https://github.com/yajiedesign/mxnet/releases). You will need
`mxnet_x64_vc14_cpu.7z` and `prebuildbase_win10_x64_vc14.7z` where X stands for
your CUDA toolkit version
+3. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only
64-bit operating systems, so you need the x64 folder.
+4. Copy the following shared libraries (.dll files) into the
```R-package/inst/libs/x64``` folder:
+```
+libgcc_s_seh-1.dll
+libgfortran-3.dll
+libmxnet.dll
+libmxnet.lib
+libopenblas.dll
+libquadmath-0.dll
+mxnet.dll
+unzip.exe
+unzip32.dll
+vcomp140.dll
+wget.exe
+```
+These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty`,
`mxnet_x64_vc14_cpu/build`, `mxnet_x64_vc14_cpu/lib`.
-Run the following commands to install the MXNet dependencies and build the
MXNet R package.
+5. Copy the header files from `dmlc`, `mxnet`, `mxshadow` and `nnvm` from
mxnet_x64_vc14_cpu/include and mxnet_x64_vc14_cpu/nvnm/include into
`./R-package/inst/include`. It should look like:
-```r
- Rscript -e "install.packages('devtools', repo =
'https://cloud.r-project.org/')"
```
+./R-package/inst
+└── include
+ ├── dmlc
+ ├── mxnet
+ ├── mshadow
+ └── nnvm
+
+```
+6. Make sure that R is added to your ```PATH``` in the environment variables.
Running the ```where R``` command at the command prompt should return the
location.
+7. Also make sure that Rtools in installed and added to your ```PATH``` in the
environment variables.
+8. Temporary patch - im2rec currently results in crashes during the build.
Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned
repository and comment out the two im2rec lines in R-package/src/mxnet.cc.
+9. Now open the Windows CMD with admin rights and change the directory to the
`mxnet` folder(cloned repository). Then use the following commands
+to build R package:
-```bash
- cd R-package
- Rscript -e "library(devtools); library(methods);
options(repos=c(CRAN='https://cloud.r-project.org/'));
install_deps(dependencies = TRUE)"
- cd ..
- make rpkg
+```bat
+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://cloud.r-project.org')); 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
```
+
### Installing MXNet on a Computer with a GPU Processor
+To install MXNet on a computer with a GPU processor, choose from two options:
+
+* Use the prebuilt binary package
+* Build the library from source code
+
+However, few dependencies remains same for both options. You will need the
following:
-To install MXNet R package on a computer with a GPU processor, you need the
following:
+* Install Microsoft Visual Studio 2017 (required by CUDA)
-* Microsoft Visual Studio 2013
+* Install [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit)
-* The NVidia CUDA Toolkit
+* Download and install [CuDNN 7](https://developer.nvidia.com/cudnn) (to
provide a Deep Neural Network library)
-* The MXNet package
+Note: A pre-requisite to above softwares is
[Nvidia-drivers](http://www.nvidia.com/Download/index.aspx?lang=en-us) which we
assume is installed.
Review comment:
Usually it comes pre-installed with Windows system, however this needs to be
an additional step in Windows Server images on ec2. So, added as note
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