You know maize mean?
The public CUDA GPG key does not appear to be installed.
참고
도커를 사용하는 경우 CUDA 및 CuDNN 설치가 필수가 아닙니다.
도커를 사용하여 설치하고 환경 구성에서 시간을 절약합시다.
Error messege
$ sudo dpkg -i cudnn-local-repo-ubuntu2004-8.4.1.50_1.0-1_amd64.deb
(Reading database ... 142484 files and directories currently installed.)
Preparing to unpack cudnn-local-repo-ubuntu2004-8.4.1.50_1.0-1_amd64.deb ...
Unpacking cudnn-local-repo-ubuntu2004-8.4.1.50 (1.0-1) over (1.0-1) ...
Setting up cudnn-local-repo-ubuntu2004-8.4.1.50 (1.0-1) ...
The public CUDA GPG key does not appear to be installed.
To install the key, run this command:
sudo cp /var/cudnn-local-repo-ubuntu2004-8.4.1.50/cudnn-local-E3EC4A60-keyring.gpg /usr/share/keyrings/
Error operate When install CuDNN in Ubuntu server 20.04 version
Analyne error message
The public CUDA GPG key does not appear to be installed
To install the key, run this command:
sudo cp /var/cudnn-local-repo-ubuntu2004-8.4.1.50/cudnn-local-E3EC4A60-keyring.gpg /usr/share/keyrings/
Anticipate solution is create the CUDA GPG key in “/var/cudnn-local-repo-ubuntu…/….gpg” and copy to “/usr/share/keyrings/”
Solution
Check Offical Nvidia CuDNN Installation Guide
https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb
# Download the Debian local repository installation package. Before issuing the following commands, you must replace X.Y and 8.x.x.x with your specific CUDA and cuDNN versions.
# Procedure
# 1. Navigate to your <cudnnpath> directory containing the cuDNN Debian local installer file.
# 2. Enable the local repository.
sudo dpkg -i cudnn-local-repo-${OS}-8.x.x.x_1.0-1_amd64.deb
# Or
sudo dpkg -i cudnn-local-repo-${OS}-8.x.x.x_1.0-1_arm64.deb
# 3. Import the CUDA GPG key.
sudo cp /var/cudnn-local-repo-*/cudnn-local-*-keyring.gpg /usr/share/keyrings/
# 4. Refresh the repository metadata.
sudo apt-get update
# 5. Install the runtime library.
sudo apt-get install libcudnn8=8.x.x.x-1+cudaX.Y
# 6. Install the developer library.
sudo apt-get install libcudnn8-dev=8.x.x.x-1+cudaX.Y
# 7. Install the code samples and the cuDNN library documentation.
sudo apt-get install libcudnn8-samples=8.x.x.x-1+cudaX.Y
Verifying the install on linux
https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#verify
# 2.4. Verifying the Install on Linux
# To verify that cuDNN is installed and is running properly, compile the mnistCUDNN sample located in the /usr/src/cudnn_samples_v8 directory in the Debian file.
# Procedure
# Copy the cuDNN samples to a writable path.
cp -r /usr/src/cudnn_samples_v8/ $HOME
# Go to the writable path.
cd $HOME/cudnn_samples_v8/mnistCUDNN
# Compile the mnistCUDNN sample.
make clean && make
##
# Run the mnistCUDNN sample.
./mnistCUDNN
# If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following:
# Test passed!
If the following compilation errors are reported when “sudo make” is executed: fatal error: FreeImage. H
mnistCUDNN sudo make
CUDA_VERSION is 11010
Linking agains cublasLt = true
CUDA VERSION: 11010
TARGET ARCH: x86_64
HOST_ARCH: x86_64
TARGET OS: linux
SMS: 35 50 53 60 61 62 70 72 75 80 86
test.c:1:10: fatal error: FreeImage.h: No such file or directory
1 | #include "FreeImage.h"
| ^~~~~~~~~~~~~
compilation terminated.
Then execute:sudo apt-get install libfreeimage3 libfreeimage-dev, and then revalidate.