Install Cuda Ubuntu 16.04

03.02.2020by

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Today, I screwed up my Ubuntu, again. One thing I learned while I have been being used Ubuntu is that, reinstalling Ubuntu can be much faster than stucking in a difficult problem which might not be able to be solved forever. Today, after realizing all environment variables are screwed, I quickly reinstalled Ubuntu 16.04 (rather than Ubuntu 14.04!) and set up the environment for machine learning research as follows.

  1. Install Cuda 6.5 Ubuntu 16.04
  2. Install Cuda Ubuntu 16.04 Server

I am leaving my records for myself as well as for those who also need a manual to quickly setup Ubuntu 16.04. Before starting the installations, I would like to remark that the shortcuts of copy / paste in terminal(Ctrl+T) are Ctrl+Shift+CorV rather than Ctrl+CorV. You can copy and paste the following example codes for your installation except for specific filenames.

All notes are written based on python 2. For installing on python 3 rather than python 2, you may need little changes such as using pip3 and python3 instead of pip and python.

Install commands

  • pip installation
  • To keep up your up-to-date system, run the followings evertime you install any packages

Nvidia Driver

  • [Warning] This is the most dangerous part in Ubuntu setting. Installing a wrong driver can make fatal errors for your system, e.g., infinite login loop. Thus, make sure to backup all your important files before installation.

  • [Warning] Don’t install Nvidia graphic driver by using, e.g., sudo apt-get install nvidia-361

  • I followed this article for Nvidia driver installation.
  • First, download a Nvidia driver file which is compatible with your graphic card from here.
  • You need to block nouveau before installing the driver. Make a new .config file as follows.
  • Now, you need to reboot and log into tty mode. You can access tty mode via Ctrl+Alt+F1.
  • If you face with fatal errors (e.g. infinite login loop), try to login tty mode(Ctrl+Alt+F1) and delete the all nvidia files by sudo apt-get purge nvidia*

CUDA and cuDNN for Ubuntu 14.04

  • CUDA for Ubuntu 16.04 is not released yet. I found an [article][L_CUDA_16.04] for installing CUDA on Ubuntu 16.04, but I don’t recommend it because it is too dangerous (or challenging).
  • You may refer to the CUDA installation guide given by the official Tensorflow website.
  • First, download a CUDA installation file(.deb) from [here][]

See also: NVIDIA CUDA with Ubuntu 16.04 beta on a laptop (if you just cannot wait) Notes: Yes, there is the possibility to install it via apt-get install cuda. I strongly suggest not to use it, as it changes the paths and makes the installation of other tools more difficult. The above options provide the complete CUDA Toolkit for application development. Runtime components for deploying CUDA-based applications are available in ready-to-use containers from NVIDIA GPU Cloud.

  • Go to cuDNN website and download cuDNN(v4) library files after registration.
  • uncompress the files and copy files as follows
  • Open bashrc file and add these lines
  • Install CUDA 9.2 and cuDNN 7.1 for PyTorch (GPU) on Ubuntu 16.04 NVIDIA recently released CUDA 9.2 and cuDNN 7.1, which have been supported by PyTorch but not TensorFlow.
  • Nvidia Driver [Warning] This is the most dangerous part in Ubuntu setting. Installing a wrong driver can make fatal errors for your system, e.g., infinite login loop. Thus, make sure to backup all your important files before installation.
  • Installing Cuda Toolkit & cudDNN w/ Ubuntu 16.04 Documentation • 25 FEB 2018 • 2 mins read. Open a terminal by pressing Ctrl + Alt + T Copy all lines per codeblock and paste lines into terminal using Shift + Ctrl + V. To begin you must have the 384 (or later) NVidia drivers installed, this can easily be done from Ubuntu’s built in additional drivers (press windows key and search.
  • How can the answer be improved?
  • To verify our CUDA installation, install the sample tests by $ sudo./cuda-samples.9.0.10-linux.run. After the installation finishes, configure the runtime library.

Install Cuda 6.5 Ubuntu 16.04

Anaconda

  • Download Anaconda from here
9.1
  • Restart the terminal or run $ source ~/.bashrc

Tensorflow on Anaconda

  • Follow the instruction of the office Tensorflow site
  • If you have installed CUDA and cuDNN, then you may be able to install Tensorflow(gpu)
  • Test if your Tensorflow is working
  • To escape from the virtual conda environment, run $ source deactivate

Troubleshooting

  • If you see a failure message in import tensorflow even though you have successfully installed Tensorflow, it may be because you have used sudo during the installations of Anaconda or Tensorflow.
  • In this case, uninstall Anaconda (simply delete the Anaconda folder with $ sudo rm -rf ~/anaconda2) and correctly follow the instructions again.
  • If you have installed Tensorflow(gpu) but fail to load libcudart.so.7.5, it may be because you didn’t change the permission of the lib files or edit .bashrc file. Please check the instruction for CUDA installation again.

Theano on Anaconda

-First, create a conda environment for Theano

  • Search a proper version of Theano before installing it using conda.

Python packages on Conda

  • You can manage each Conda virtual environment, independently. In other words, you can install and use different versions of python packages in different virtual environment.
  • After running the virtual environment by source activate tf, you can check the list of installed packages by conda list
  • Here is some packages you may want to have installed. Note that we are going to use conda install instead of pip install.
  • For loading .RData to python.

Git

  • Git for using GitHub repository
  • Test if your Git is working

R

  • R now supports Ubuntu 16.04 LTS
  • First, edit /etc/apt/sources.list file.
  • After modification,
  • You can download and install RStudio from here

Java, Ruby, and Jekyll

  • The followings are thing required for Jekyll

Troubleshooting

  • If you are using Ubuntu 14.04, you may get ruby v1.9 instead of v2.3. To get ruby v2.3, follow the instructions bellow.

* I will keep updating this post for including, e.g., CUDA installation on Ubuntu 16.04. for GPU computing.

The followings are additional notes for my Ubuntu setting.

Install Cuda Ubuntu 16.04 Server

Language setting (Korean)

  • Language support - Install / Remove Languages - Check Korean
  • Keyboard input method system : fcitx
  • Reboot
  • Text Entry - click + - Add Hangul(Fcitx)
  • Set a shortcut for switch between EN/KOR as, e.g. Shift+Space
  • Reboot if any problem occurs

Display

  • To prevent the sticky mouse cursor on the monitor edge, Screen Display - Sticky Edge Off

Shortcuts

  • Keyboard - Shortcut Tab
  • I prefer to set my own shortcuts for Screenshots
  • “Semi-maximize the window” is already assigned to “Ctrl+Super+Left/Right”
  • Click and hold the super key to see shortcut assignemnets

Software from Ubuntu Software

  • Atom editor
  • VLC media player
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