Saturday 9 March 2019

Build mxnet 1.3.1 on windows

    If you were like me, tried to build mxnet 1.3.1 on windows, you may suffer a lot of pains since mxnet do not have decent support on windows, apparently the developers of mxnet do not perform enough tests(maybe none) on windows before they release the stable version. Despite of all of the troubles mxnet brought, it is still a nice tool of deep learning, that is why I am still prefer to work with it.

    I believe one of the best way to make the open source project become better is contribute something back to it, that is why I would like to write down how to build mxnet 1.3.1 on windows step by step.

1. Do not build mxnet on windows with intel mkl

    Do not do this unless you are asking for trouble, please check the details on stackoverflow and issue 14343.

2. Build openBLAS with native msvc ABI

    The openBLAS post at here do not work with vc2015 anymore(if you updated your vc2015), the abi are not compatible with msvc. The easiest solution to solve this issue is build the openBLAS by yourself.The steps are

a. Clone openBLAS of xianyi from github
b. Compile openBLAS as the instruction shown here. Do not install Anaconda and miniconda together, just pick one of them. If you do not know where is your vcvars64.bat on your pc, I suggest you use Everything to find the path.
c. Copy the files(cblas.h, f77blas.h) from the generated  folder into the build folder.

3. Clone mxnet fork by me

git clone --recursive
cd mxnet 
git checkout 1.3.1_win_compile_fix
    This branch fix some type mismatch errors.

4. Comment out codes in

     This file is under the folder "mxnet\src\operator\random", there is a function ShuffleForwardGPU, 
comment out the implementation else there will have a lot of compile times errors(no suitable 
user-defined conversion from "mshadow::Tensor<mxnet::gpu, 1, mxnet::index_t>" to 
"const mshadow::Tensor<mshadow::gpu, 1, unsigned int>" exists).
     I guess this function would not be called when doing inference task, after all who would like to 
make their inference results become unpredictable? If you were like me, only want to use 
cpp_package to do the inference task, you should be safe to comment out the codes. 

5. Open cmake

  Open your cmake and select msvc with 64bits.

6. Configuration

   The most important note are

1. Do not use anything related to intel MKL.

2. Do not build cpp_package at the first time

    Without mkl mxnet cannot exploit full power of the cpu, but with it your app cannot run at all, 
depending on how you build it, your app may throw the error 
"Intel MKL FATAL ERROR: Cannot load mkl_intel_thread.dll." or 
"Check failed: MXNDArrayWaitToRead(blob_ptr_->handle_) == 0 (-1 vs. 0)"

    If you do not need cuda, uncheck following options, USE_CUDA and USE_CUDNN. After that, click Configure->uncheck BUILD_TESTING->click Configure->click generate.

7. Build mxnet without cpp_package

    a. Open your ALL_BUILD.vcxproj
    b. Navigate to the project "mxnet"
    c. Right click your mouse, select "Properties"
    d. Select Linker->Input
    e. Link to flangmain.lib, flangrti.lib, flang.lib, ompstub.lib. For example, my paths are


    If you do not know where are they, use Everything to find the path.

    f. Navigate to the project "ALL_Build"
    g.Right click your mouse, click build.

8. Configure cmake to build with cpp_package

    Now we can build mxnet with cpp_package, let us go to cmake again and change some settings.

a. (Optional)Change your install path, else you may not be able to install(ex : change to C:/Users/yyyy/programs/Qt/3rdLibs/mxnet/build_gpu_1_3_1_temp/install).

b. Make sure you have set the PATH of python, if you are building 32/64bits version of mxnet, you need python of 32/64bits, else you wouldn't be able to generate op.h. I suggest you use Rapid environment to manage your path on windows.

If your vc complain it cannot find the python exe, reopen your vc.

c. Check USE_CPP_PACKAGE->uncheck BUILD_TESTING->configure->generate

d. Remove the example projects since they will hinder the build process, those projects are alexnet, charRNN, googleNet, inception_bn, lenet, lenet_with_mxdataiter, mlp, mlp_cpu, mlp_gpu, resnset.

e. Go to your build/Release folder, copy the libmxnet.dll into any folder which could be found by
the windows(the path in the Path), let us assume that path call global_path.

f. Open your Developer command prompt(mine is developer command prompt for vs2015), let us call it DCP

g. Navigate your DCP to global_path

h. Enter "dumpbin /dependents libmxnet.dll", this command will show you the dependencies of this
dll. In my case, it show


We only need to copy flangrti.dll, flang.dll, ompstub.dll into global_dll in order to generate op.h, because another dll already exist in the PATH. Again, please use Everything to find the path.

i. Your mxnet need to link to Link to flangmain.lib, flangrti.lib, flang.lib, ompstub.lib again since generate clear them.

j. Navigate to the project "ALL_Build"   

k.  Right click your mouse, click build.

l. Navigate to the project "INSTALL"

m.  Right click your mouse, click build.

n. Copy cpp-package\include\mxnet-cpp into build/install/include

o. Copy mxnet\3rdparty\tvm\nnvm\include\nnvm into build/install/include

9. Add mx_float for scale, int for num_filter

    The op.h generated by this solution, there are two parameters lack type declaration, you need to add them by yourself.


    Congratulation, now you have build the mxnet successfully, to tell you the truth, this is not a pleasant journey, there are too many bugs/issues when I try to build mxnet1.3.1on windows(1.4.0 got more bugs on windows when you try to build it) , there are many bugs should be found before they release the major version if them have tried to build mxnet on windows. I believe windows and cpp_package are not their main concern yet, let us hope that 

a. Someday they can put more love into windows and cpp_package. Windows still dominate market of desktop/laptop and cpp_package is a much better choice than python if you want to do edge deployment.

b. Adopt a commit system like opencv(whenever you commit your codes, opencv build it on every single platforms they support), this could prevent a log of bugs, the later you adopt, the more cost you need to pay for cross-platform.

c. Let us cross our finger, hope them can fix all of these bugs before next version release


  1. Thanks for the blog. The Apache/MXNet community will work on the improvements for the windows build and usage :)

    1. Thanks, hope these issues can solve asap. Any plan to support wasm in the future?

  2. Sir, can you explain step 4, there is the following line:

    NNVM_REGISTER_OP (_shuffle);
    .set_attr ("FCompute ", ShuffleForwardGPU);

    Do I just comment on the set_attribute code? (line 103)