In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
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This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:
lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)
A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly.
In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers.
This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa
in the command-line.
# imports | |
import numpy as np | |
import cv2 | |
import matplotlib.pyplot as plt | |
# The Hough Transform is a popular algorithm for detecting any shape that can | |
# be represented in a parametric mathmatical form in binary images. This | |
# usually means that images need to be thresholded or filtered prior to running |
This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software
- NVIDIA graphics card driver (v450.57)
- CUDA (v11.0.2)
- cuDNN (v8.0.2.39)
on an Ubuntu Linux system, in particular Ubuntu 20.04.
The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.
I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).
So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht
# Stop all containers | |
docker stop `docker ps -qa` | |
# Remove all containers | |
docker rm `docker ps -qa` | |
# Remove all images | |
docker rmi -f `docker images -qa ` | |
# Remove all volumes |