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

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cui
cui on 2 Nov 2020
Commented: teng yang on 18 Jan 2021
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);

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Accepted Answer

cui
cui on 2 Nov 2020
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

  1 Comment

teng yang
teng yang on 18 Jan 2021
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 ?

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