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

I successfully connected the jetson nano and correctly identified cuda, but I still reported an error when I checked it. How can I solve it?

jetsonObj = jetson('myIP','myname','mypassword')
camlist = getCameraList(jetsonObj)

Checking for CUDA availability on the Target...

Checking for 'nvcc' in the target system path...

Checking for cuDNN library availability on the Target...

Checking for TensorRT library availability on the Target...

Checking for prerequisite libraries is complete.

Gathering hardware details...

Checking for third-party library availability on the Target...

Gathering hardware details is complete.

Board name : NVIDIA Jetson TX1

CUDA Version : 10.2

cuDNN Version : 8.0

TensorRT Version : 7.1

GStreamer Version : 1.14.5

V4L2 Version : 1.14.2-1

SDL Version : 1.2

Available Webcams :

Available GPUs : NVIDIA Tegra X1

jetsonObj =

jetson with properties:

DeviceAddress: '***'

Port: 22

BoardName: 'NVIDIA Jetson TX1'

CUDAVersion: '10.2'

cuDNNVersion: '8.0'

TensorRTVersion: '7.1'

SDLVersion: '1.2'

V4L2Version: '1.14.2-1'

GStreamerVersion: '1.14.5'

GPUInfo: [1×1 struct]

WebcamList: []

camlist =

1×3 table

Camera Name Video Device Available Resolutions

__________________________ _____________ ________________________________________________

"vi-output, imx219 8-0010" "/dev/video0" "[1280 720],[1920 1080],[3264 1848],[3264 2464]"

then i check env:envCfg = coder.gpuEnvConfig('jetson');

envCfg.BasicCodegen = 1;

envCfg.Quiet = 1;

envCfg.HardwareObject = jetsonObj;

coder.checkGpuInstall(envCfg);

Answer 1

[cui,xingxing]

I solved the problem by referring to it, ~/.bashrc also needs to add an additional environment variable

case $- in
    *i*) ;;
      *)
              export PATH=${PATH}:/usr/local/cuda-10.2/bin
              export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-10.2/lib64
              

export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/lib/aarch64-linux-gnu

   
           return;;
esac

[teng yang]

Hi! I have the same problem.But I have a new error after using your method.It is like this:

    One or more of the system checks did not pass, with the following errors ...
    Deep Learning (cuDNN) Code Generation: (Test GPU code generation failed with 
    the error 'emlc:compilationError'. View report for further
    information: View report)
    

Do you have any suggestion ?