On terminal run:
sudo apt-get purge nvidia *
sudo add-apt-repository ppa:graphics-drivers
sudo apt-get update
sudo apt install nvidia-driver-450
Reboot your pc and run nvidia-smi to check that everything is working.
If you get the error NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver Disable secure boot.
Note:
We used nvidia-driver-450 since it was the recommended driver after running ubuntu-drivers devices
Visit Nvidia developer and select:
- Operating System Linux
- Architecture: x86_64
- Distribution: Ubuntu
- Version: 18.04
- Installer Type: deb(local)
Then download the base installer and patch 1.
Open a terminal on the file location and execute:
sudo dpkg -i cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
Double click the patch 1 to install it.
On terminal run:
sudo nano ~/.bashrc
Add the following lines at the end of your .bashrc file
export PATH=/usr/local/cuda-10.0/bin${PATH:+:$PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
Close all terminals.
Note: You need to have an Nvidia Developer Account for this step
Visit cuDNN developer:
Click on cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
Download cuDNN Library for Linux
Move the tar file to a desire location and run
tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
Let's create a python virtual environment and install tensorflow to check if cuda is working properly
python3 -m venv your_env_name
source your_env_name/bin/activate
pip install tensorflow-gpu
Start pyhon
python
Run
import tensorflow as tf
hello = tf.constant('hello tensorflow')
with tf.Session() as sesh:
sesh.run(hello)
If the tensorflow installation fails try running:
sudo apt-get install python3-dev