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| #!/bin/bash | |
| ### steps #### | |
| # Verify the system has a cuda-capable gpu | |
| # Download and install the nvidia cuda toolkit and cudnn | |
| # Setup environmental variables | |
| # Verify the installation | |
| ### | |
| ### to verify your gpu is cuda enable check | |
| lspci | grep -i nvidia | |
| ### If you have previous installation remove it first. | |
| sudo apt-get purge nvidia* | |
| sudo apt remove nvidia-* | |
| sudo rm /etc/apt/sources.list.d/cuda* | |
| sudo apt-get autoremove && sudo apt-get autoclean | |
| sudo rm -rf /usr/local/cuda* | |
| # system update | |
| sudo apt-get update | |
| sudo apt-get upgrade | |
| # install other import packages | |
| sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev | |
| # first get the PPA repository driver | |
| sudo add-apt-repository ppa:graphics-drivers/ppa | |
| sudo apt update | |
| # install nvidia driver with dependencies | |
| sudo apt install libnvidia-common-470 | |
| sudo apt install libnvidia-gl-470 | |
| sudo apt install nvidia-driver-470 | |
| # installing CUDA-11.8 | |
| wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.0-1_all.deb | |
| sudo dpkg -i cuda-keyring_1.0-1_all.deb | |
| sudo apt-get update | |
| sudo apt-get -y install cuda | |
| # setup your paths | |
| echo 'export PATH=/usr/local/cuda-11.8/bin:$PATH' >> ~/.bashrc | |
| echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc | |
| source ~/.bashrc | |
| sudo ldconfig | |
| # install cuDNN v8.9.7 | |
| # First register here: https://developer.nvidia.com/developer-program/signup | |
| CUDNN_TAR_FILE="cudnn-linux-x86_64-8.9.7.29_cuda11-archive.tar.xz" | |
| wget https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/11.x/cudnn-linux-x86_64-8.9.7.29_cuda11-archive.tar.xz | |
| tar -xvf ${CUDNN_TAR_FILE} | |
| # copy the following files into the cuda toolkit directory. | |
| sudo cp cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include | |
| $ sudo cp -P cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64 | |
| $ sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* | |
| # Finally, to verify the installation, check | |
| nvidia-smi | |
| nvcc -V | |
| # install Pytorch (an open source machine learning framework) | |
| pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 |
RTX 3090 requires driver version of 515 (not 470).
# install nvidia driver with dependencies
sudo apt install libnvidia-common-515
sudo apt install libnvidia-gl-515
sudo apt install nvidia-driver-515
I am wondering whether these work for installing cuda 11.3 on ubuntu 22.04 also?
Will it work for nvidia-server on ubuntu 20.04 server ?
install nvidia driver with dependencies
sudo apt install libnvidia-common-470-server
sudo apt install libnvidia-gl-470-server
sudo apt install nvidia-driver-470-server
Will it work for nvidia-server on ubuntu 20.04 server ?
install nvidia driver with dependencies
sudo apt install libnvidia-common-470-server sudo apt install libnvidia-gl-470-server sudo apt install nvidia-driver-470-server
@saravananpsg It's works for server. I tested. I also changed 470 to 515 to support 3090.
I also had to change the version from 470 to 515 for a 1070 TI.
sudo apt install libnvidia-common-515
sudo apt install libnvidia-gl-515
sudo apt install nvidia-driver-515
After installing, if nvidia-smi gives a kernel/client version mismatch error, reboot.
This helped A LOT! Thanks!
Thank you! It was veeery helpful!
Thank you verry much you just forgotten a star character after cudnn here :
sudo cp -P cuda/include/cudnn*.h /usr/local/cuda-11.3/include
Verry important because else an error can be encountered while compiling for example pytorch "cudnn_version.h" not found.
Regards
tar -xvf cudnn-linux-x86_64-8.9.7.29_cuda11-archive.tar.xz
xz: (stdin): File format not recognized
tar: Child returned status 1
tar: Error is not recoverable: exiting now
I get this error
I have my tar and xz installed
with this command :
$ sudo cp cudnn--archive/include/cudnn.h /usr/local/cuda/include
message error :
cp: cannot stat 'cudnn--archive/include/cudnn.h': No such file or directory
the same with the other commands :
$ sudo cp cudnn--archive/include/cudnn.h /usr/local/cuda/include
====> cp: cannot stat 'cudnn--archive/include/cudnn.h': No such file or directory
$sudo cp cudnn--archive/include/cudnn.h /usr/local/cuda/include
====> cp: cannot stat 'cudnn--archive/include/cudnn.h': No such file or directory
I had to implement the end of this tutorial:
https://towardsdatascience.com/installing-multiple-cuda-cudnn-versions-in-ubuntu-fcb6aa5194e2
I used his edit of bash so tensorflow (in my case) can choose what cuda toolkit use, and it worked.
Thank you very much @Mahedi-61, much appreciated