Why does my GPU set-up error with 'emlc:compilationError'?

When I run the setup command "coder.checkGpuInstall", it fails with 'host' gpuEnvConfig but not 'Jetson' when running a code generation test.

Answer 1

[MathWorks Support Team]

The host and Jetson are two different environments. In the case of Jetson, the codegen is performed on the host and directory is copied over to the Jetson and built.

Try running the check from the GUI to view the HTML report. The GUI is described below:

https://www.mathworks.com/help/gpucoder/ref/gpucodersetup-app.html

From there, you may be able to find build logs errors which give more information. (Note: There is no difference between the GUI and command line version of checkGpuInstall except that GUI produces an HTML report). The following checks can also be used at the command line to test basic and deep learning code gen:

% Basic Code gen
gpuEnvObj = coder.gpuEnvConfig;
gpuEnvObj.GpuId = 0;
gpuEnvObj.BasicCodegen = 1;
gpuEnvObj.BasicCodeexec = 1;
coder.checkGpuInstall(gpuEnvObj)

% Deep code gen
gpuEnvObj = coder.gpuEnvConfig;
gpuEnvObj.GpuId = 0;
gpuEnvObj.DeepLibTarget = 'cudnn';
gpuEnvObj.DeepCodegen = 1;
gpuEnvObj.DeepCodeexec = 1;
coder.checkGpuInstall(gpuEnvObj)

If  a check fails, most likely there is something wrong with the host environment. Please make sure they have:

1. MATLAB Coder and Parallel Computing Toolbox installed in addition to GPU Coder

2. Required 3rd party products

https://www.mathworks.com/help/gpucoder/gs/install-prerequisites.html

3. Properly set up environment variables

https://www.mathworks.com/help/gpucoder/gs/setting-up-the-toolchain.html

4. A CUDA-enabled GPU

https://developer.nvidia.com/cuda-gpus

Get the GPU information from MATLAB command window:

n = gpuDeviceCount;
for i = 1:n
gpuDevice(i)
end

5. The correct CUDA Toolkit Version given the MATLAB release

https://www.mathworks.com/help/parallel-computing/gpu-computing-requirements.html

6. Up-to-date Nvidia GPU card drivers