【1】Matlab深度学习环境配置-入门_gpu coder interface for deep learning libraries su-优快云博客

本文链接：https://blog.youkuaiyun.com/weiniuzhu/article/details/117965570

本文档详细记录了配置MATLAB2019b进行GPU深度学习的步骤，包括CUDA、cuDNN和TensorRT的安装路径。遇到的错误是GPU代码生成失败，但CUDA和cuDNN环境检查已通过。问题在于'coder'未定义，可能缺少相关组件或配置不完整。寻求帮助以解决MATLAB GPU代码生成错误。

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编译环境配置

软件需求：

Matlab 2019b
cudnn cudnn-10.1-windows10-x64-v7.6.4.38
TensorRT-6.0.1.5.Windows10.x86_64.cuda-10.1.cudnn7.6
Cuda 10.1 cuda_10.1.105_418.96_win10

下载路径。

Maltab GPU 环境要求https://ww2.mathworks.cn/help/gpucoder/gs/install-prerequisites.html

CUDNN https://developer.nvidia.com/rdp/cudnn-archive

TenosRThttps://developer.nvidia.com/nvidia-tensorrt-5x-download

CUDA https://developer.nvidia.com/cuda-toolkit-archive

https://blog.youkuaiyun.com/charlotteYue/article/details/106146482

Matlab深度学习入门之树莓派与GPU应用。

打开cmd nvcc –version 或者 nvcc -V ，nvidia-smi ，nvidia-smi -q

NVIDIA-SMI 419.67 Driver Version: 442.19

将cudnn的lib，bin，include复制到cuda包含相同目录下。
将tensort 文件夹复制到cuda\v10.1 下面；同时将tensort 下的lib文件夹中的dll拷贝到cuda\v10.1\bin里边。

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配置环境变量：

CUDA_PATH= C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

CUDA_PATH_V10_1= C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

NVIDIA_CUDNN= C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

NVIDIA_TENSORRT= C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\TensorRT

在Path下添加：

打开matlab 进行输入以下命令

mex -setup:' D:\ProgramFiles\MATLAB\R2019b\bin\win64\mexopts\msvc2017.xml' C -v

coder.checkGpuInstall('gpu','codegen','cudnn','quiet');

gpuDeviceCount

CUDA_PATH

Path to the CUDA® toolkit installation.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\

NVIDIA_CUDNN

Path to the root folder of cuDNN installation. The root folder contains the bin, include, and lib subfolders.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cuDNN\

NVIDIA_TENSORRT

Path to the root folder of TensorRT installation. The root folder contains the bin, data, include, and lib subfolders.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\TensorRT\

OPENCV_DIR

Path to the build folder of OpenCV on the host. This variable is required for building and running deep learning examples.

For example:

C:\Program Files\opencv\build

PATH

Path to the CUDA executables. Generally, the CUDA toolkit installer sets this value automatically.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin

Path to the cudnn.dll dynamic library. The name of this library may be different on your installation.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cuDNN\bin

Path to the nvinfer* dynamic libraries of TensorRT. The name of this library may be different on your installation.

For example:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\TensorRT\lib

Path to the Dynamic-link libraries (DLL) of OpenCV. This variable is required for running deep learning examples.

For example:

C:\Program Files\opencv\build\x64\vc15\bin

Hi there, I was trapped in this error within the whole day. I'm using yolo to dectect objects, while I'm just using it but not trying to compile it. So when I used single or multiple CPUs to run the vehicle_Dataset, I found the training process was unable to be accomplished as matlab was out of memory.

So I turned to try to deploy the taining using my GPU, which is Geforce GTX 1060. I installed the new divers for this Graphic card(ver 430.64), CUDA toolkit 10.1, cuDNN v7.5.1 (April 22, 2019) for CUDA 10.1. Then I created

Variable name: CUDA_PATH

Variable value: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

in the system variables. Also, I added

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\lib64

in the Path.

All the add-on on this page was also installed:

https://ww2.mathworks.cn/help/vision/ug/code-generation-for-object-detection-using-yolo-v2.html?searchHighlight=yolo&s_tid=doc_srchtitle

including "GPU Coder Interface for Deep Learning Libraries support package", Microsoft Visual Studio 2017, "MATLAB Support for MinGW-w64 C/C++ Compiler".

Then, I ran the following code in matlab, which worked very good.

mex -setup:'C:\Program Files\MATLAB\R2018b\bin\win64\mexopts\msvc2017.xml' C -v

mex -setup:'C:\Program Files\MATLAB\R2019a\bin\win64\mexopts\msvc2017.xml' C -v

Then I ran

coder.checkGpuInstall('gpu','codegen','cudnn','quiet');

Matlab inform me as

Error using coder.checkGpuInstall (line 32)

One or more of the system checks did not pass, with the following errors ...

Basic Code Generation: (Test GPU code generation failed with the error 'emlc:compilationError'. View report for further information: View report)

So I click the View report, and it says:

Build error: C++ compiler produced errors. See the Build Log for further details.

While the build logs has 1659 lines... I have attached the ecported reports in tha attachment. So I detected the current setup with coder.checkGpuInstall();

Compatible GPU : PASSED

CUDA Environment : PASSED

Runtime : PASSED

cuFFT : PASSED

cuSOLVER : PASSED

cuBLAS : PASSED

cuDNN Environment : PASSED

Basic Code Generation : FAILED (Test GPU code generation failed with the error 'emlc:compilationError'. View report for further information: View report)

So could anyone help me out of this problem? Thanks a lot in advance!

1. 错误处理

在命令行里，直接输入 nvidia-smi.exe如果不能识别命令，在确保正确安装相应cuda版本的情况下，在环境变量里加入该执行文件路径即可。
即设置Path变量，我的目录为： C:\Program Files\NVIDIA Corporation\NVSMI

So I'm using the provided MATLAB function to check my gpu build but I always end up obtaining this extrange error which says that there is no class or variable "coder". I'm running this under Windows 10 x64, NVIDIA CUDA v10.1 and latest cuDNN libraries on MATLAB R2019a.

coder.checkGpuInstall()

Compatible GPU : PASSED

CUDA Environment : PASSED

Runtime : PASSED

cuFFT : PASSED

cuSOLVER : PASSED

cuBLAS : PASSED

cuDNN Environment : PASSED

Error using coder.checkGpuInstall (line 32)

Undefined variable "coder" or class "coder.gpuConfig"