Quick Apt Repository way – NVIDIA CUDA 9.x on Ubuntu 18.04 LST installation

The same NVIDIA CUDA 9.1 setup on Ubuntu 18.04 LST using the aptitude repository. However this appears to work and is simple to work with. Reference is taken from this askubuntu discussion.

Lookup the solution to the Nouveau issue from this blogpost

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo ubuntu-drivers autoinstall
sudo reboot

Now install the CUDA toolkit

sudo apt install g++-6
sudo apt install gcc-6
sudo apt install nvidia-cuda-toolkit gcc-6

Screenshot from 2018-07-13 14-18-16

Screenshot from 2018-07-13 14-16-00

Run the installer

root@wind:~/Downloads# ./cuda_9.1.85_387.26_linux --override

Screenshot from 2018-07-13 14-27-36.png

Screenshot from 2018-07-13 14-28-43

Setup the environment variables

# Environment variables
export PATH=/usr/local/cuda-9.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.1/lib64 
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.1/lib

Provide the soft link for the gcc-6 compiler

sudo ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc
sudo ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++
sudo reboot


cd ~/NVIDIA_CUDA-9.1_Samples/
make -j4

Upon completion of the compilation test using device query binary

$ cd ~/NVIDIA_CUDA-9.1_Samples/bin/x86_64/linux/release
$ ./deviceQuery

Screenshot from 2018-07-13 14-41-49.png

$ sudo bash -c "echo /usr/local/cuda/lib64/ > /etc/ld.so.conf.d/cuda.conf"
$ sudo ldconfig



NVIDIA CUDA 9.x on Ubuntu 18.04 LST installation


An installation guide to take you through the NVIDIA graphics driver as well as CUDA toolkit setup on an Ubuntu 18.04 LTS.

A. Know your cards

Verify what graphics card you have on your machine

rahul@karma:~$ lspci | grep VGA
04:00.0 VGA compatible controller: 
NVIDIA Corporation GM204 [GeForce GTX 970] (rev a1)
rahul@karma:~$ sudo lshw -C video
 description: VGA compatible controller
 product: GM204 [GeForce GTX 970]
 vendor: NVIDIA Corporation
 physical id: 0
 bus info: pci@0000:04:00.0
 version: a1
 width: 64 bits
 clock: 33MHz
 capabilities: pm msi pciexpress vga_controller bus_master cap_list rom
 configuration: driver=nouveau latency=0
 resources: irq:30 memory:f2000000-f2ffffff memory:e0000000-efffffff memory:f0000000-f1ffffff ioport:2000(size=128) memory:f3080000-f30fffff

Download the right driver

downloaded the Version 390.67 for GeForce GTX 970

Screenshot from 2018-07-12 17-15-34.png

B. Nouveau problem kills your GPU rush

Hoever there are solutions available

Here is what worked for me

  1. remove all nvidia packages ,skip this if your system is fresh installed
    sudo apt-get remove nvidia* && sudo apt autoremove
  2. install some packages for build kernel:
    sudo apt-get install dkms build-essential linux-headers-generic
  3. now block and disable nouveau kernel driver:
    sudo vim /etc/modprobe.d/nvidia-installer-disable-nouveau.conf

Insert follow lines to the nvidia-installer-disable-nouveau.conf:

blacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off

save and exit.

  1. Disable the Kernel nouveau by typing the following commands(nouveau-kms.conf may not exist,it is ok):
    rahul@wind:~$ echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
    options nouveau modeset=0
  2. build the new kernel by:
    rahul@wind:~$ sudo update-initramfs -u
    update-initramfs: Generating /boot/initrd.img-4.15.0-23-generic
  3. reboot
Run the Installer in run-level 3
$ sudo init 3 
$ sudo bash
$ ./NVIDIA-Linux-x86_64-390.67.run


More instruction on how to stop using the driver before uninstallation
sudo nvidia-installer –uninstall

C. NVIDIA X Server Settings

Install this from the ubuntu software center.
Screenshot from 2018-07-12 17-23-43.png

D. Start the CUDA related setup

We will need the CUDA toolkit 9.1 which is supported for the GTX 970 version with compute 3.0 capability. So download the local installer for Ubuntu.

Screenshot from 2018-07-13 13-55-24.png

Downloaded the “cuda_9.1.85_387.26_linux.run*” local installation file.

$ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt install nvidia-cuda-toolkit gcc-6

Steps are taken from the CUDA 9.1 official documentation

  1. Perform the pre-installation actions.
  2.  Disable the Nouveau drivers. We did this in the above driver installation
  3. Reboot into text mode (runlevel 3). This can usually be accomplished by adding the number “3” to the end of the system’s kernel boot parameters. Change the runlevel ‘sudo init 3’, refer
  4. Verify that the Nouveau drivers are not loaded. If the Nouveau drivers are still loaded, consult your distribution’s documentation to see if further steps are needed to disable Nouveau.
  5. Run the installer and follow the on-screen prompts:
$ chmod +x cuda_9.1.85_387.26_linux
$ rahul@wind:~/Downloads$ ./cuda_9.1.85_387.26_linux --override

Screenshot from 2018-07-13 13-52-19.png

Since we already installed the Driver above we say NO in the NVIDIA accelerated graphic driver installation question.

Screenshot from 2018-07-13 13-54-20.png

This will install the CUDA stuff in the following locations

  • CUDA Toolkit /usr/local/cuda-9.1
  • CUDA Samples $(HOME)/NVIDIA_CUDA-9.1_Samples

We can verify the graphic card using the NVIDIA-SMI command.

Screenshot from 2018-07-12 20-02-08


cd /usr/local/cuda-9.1/bin
sudo ./uninstall_cuda_9.1.pl


E. Environment Variables

rahul@wind:~$ vim ~/.bashrc

# Add the following to the environment variables
export PATH=/usr/local/cuda-9.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.1/lib64 
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.1/lib

rahul@wind:~$ source ~/.bashrc
rahul@wind:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Tue_Jun_12_23:07:04_CDT_2018
Cuda compilation tools, release 9.1, 


F. Test

Ensure you have the right driver versions

rahul@wind:$ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module 390.67 Fri Jun 1 04:04:27 PDT 2018
GCC version: gcc version 7.3.0 (Ubuntu 7.3.0-16ubuntu3)

Change directory to the NVIDIA CUDA Samples and compile them

rahul@wind:~/NVIDIA_CUDA-9.1_Samples$ make

Now run the device query test

rahul@wind:~/NVIDIA_CUDA-9.1_Samples/bin/x86_64/linux/release$ ./deviceQuery
./deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)




Anaconda for your Image Processing, Machine Learning, Neural Networks, Computer Vision development environment using VS Code

Python is a great language and I will not go into explaining why it is so. Here is a brief setup for your development environment in case you are tinkering with computer vision problems and looking at learning neural network on your windows laptop.

Anaconda3 5.0

64 bit Download: https://www.anaconda.com/download

Install Anaconda with the default options.

  • Anaconda Navigator is a great place to look at your environment and activate them as per your need.
  • In case you want to have a Python 2x and 3x environment side by side, then you can create them in navigator. Here I have a base(root) setup with Python 3.6 and an additional Python 2.7 environment.
  • In order to use a particular environment you can click on that environment in the navigator or go to the Anaconda prompt and execute the following command
"(base)C:\Users\Karma>activate Py27"
  • To deactivate use
  • To create a new environment use the following command:
(base)C:\Users\Karma>conda create -n Py27 python=2.7 anaconda


Whenever you want to use a particular environment just go to the environments section and activate it. This will setup your python with the packages and version as configured in that environment.  In the screenshot above I have tensorflow in my base environment while its always better to have a separate environment for this.

In case you are using Cmder like me then go for this:

Considering where you have installed your Anaconda
> C:\Anaconda3\Scripts\activate.bat C:\Anaconda3
> C:\Users\Karma\Anaconda3\Scripts\activate.bat C:\Users\Karma\Anaconda3
> conda info --envs
> conda activate py27
> conda deactivate

Lets try to use package manager “conda” for the setup.

Run the following installation command on Anaconda Command Prompt which will open up showing prompt as (C:\Anaconda3) C:\Users\Karma>:

In order to find packages, you should look at the Anaconda repository ( https://anaconda.org/anaconda/repo )

# Adding the menpo channels and install opencv
conda install -c https://conda.binstar.org/menpo opencv
conda config --add channels menpo
conda install -c menpo opencv

# or directly use conda-forge
conda install -c conda-forge opencv

# Install packages
conda install numpy
conda install scipy
conda install matplotlib

# List packages
conda list


If the OpenCV installation did not go through then we can use the pre-built windows binaries maintained by,

Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv

Download File: You can remove these modules by using “pip uninstall <package>”

(base)λ pip install opencv_python-3.4.0-cp36-cp36m-win_amd64.whl
Processing c:\users\karma\downloads\opencv_python-3.4.0-cp36-cp36m-win_amd64.whl
Installing collected packages: opencv-python
Successfully installed opencv-python-3.4.0
(base)λ pip install opencv_python-3.4.0+contrib-cp36-cp36m-win_amd64.whl
Processing c:\users\karma\downloads\opencv_python-3.4.0+contrib-cp36-cp36m-win_amd64.whl
Installing collected packages: opencv-python
Successfully installed opencv-python-3.4.0+contrib

In my case I used SIFT and SURF implementations which were made available in the contrib packages.

Now, that we have packages set, lets test it out on the python interpreter interface,
Use the following commands on the python CLI.

import numpy as np
import cv2


Instructions: https://www.tensorflow.org/install/install_windows

To install this package with conda run:
conda install -c conda-forge tensorflow

Version changes based on the repository you are trying to download from.

I typically use VS Code but if you like smooth scrolling go for Sublime.

In VS Code I use ms-python.python, tht13.python extensions to simplify my workspace.


Debugging is critical to work with any kind of code. So here is some configuration to get you started here.

  • Verify that the workspace settings.json file has the right python path
"python.pythonPath ": "C:\\Anaconda3\\python.exe"
  • Add a launch.json in your project .vscode folder with the following values
   "name": "Python",
   "type": "python",
   "pythonPath":"${config:python.pythonPath}", "request": "launch", "stopOnEntry": true, "console": "none", "program": "${file}", "cwd": "${workspaceFolder}", "debugOptions": [ "WaitOnAbnormalExit", "WaitOnNormalExit", "RedirectOutput" ] }
This will get you setup for debugging and here is how the debug interface would look like when you have put the breakpoints and stepped through the code.

Good Luck.