Nageen Chand
Nageen Chand Author of this blog
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How To Install Nvidia Drivers , Cuda And Tensorflow On Linux

How To Install Nvidia Drivers , Cuda And Tensorflow On Linux

Getting GPU working for tensorflow and CUDA is a difficult task for naive linux users. In this guide, you can easily understand the installation process. This guide is focussed on debian based distros, but it can be used on any linux distro with respective command for the task according to distro.

So, What do we need for working tensorflow and nvidia drivers on linux

  • A linux Machine, ofcourse.
  • Nvidia Drivers Installed.
  • Compatible tensorflow version Installed
  • Tensorflow compatible Cuda Version Installed.
  1. Make sure You have nvidia Drivers installed
    I have nvidia geforce 940mx, So the latest drivers for me was v430.14
    You can download and install Latest drivers from here

  2. Reboot and make sure nvidia drivers are installed successfully

    • run ` nvidia-smi ` in terminal
      It should give some output in tabular form like here :


    • make sure Nouveau drivers are disbaled ( These are automatically disabled after nvidia drivers installation)
      to check: run ` lsmod | grep nouveau ` in terminal

      • If there is no output
        • then its disabled and we are good to go.
      • else
        • Create a file at ` /etc/modprobe.d/blacklist-nouveau.conf ` with the following contents:
          blacklist nouveau
          options nouveau modeset=0

          #Regenerate the kernel initramfs:

          sudo update-initramfs -u # (Debian based)
          sudo grub-mkconfig -o /boot/grub/grub.cfg # (Arch linux based)
  3. Download cuda runfile from this link (in my case I used cuda 10.0,you can check latest cuda version supported by tf here)
    as shown here :
  4. Installing CUDA
    open terminal in the file location folder and run
    • sudo sh # change the name of file accordingly
    • accept the terms and conditons by typing accept when prompt asks for it
    • when a selection menu comes up select everything accept drivers
    • and install
    • wait for installation (might take 5-10 mins)
  5. Installing cuDNN 7.5.0 (compatible with cuda 10.0,Note : 10.1 was not supported at that time)
    • Download from
      NOTE: you will need account to download this file, so create a account if not already
      # select : "cuDNN Library for Linux"
      # extract the downloaded file
      tar -zxvf cudnn-10.0-linux-x64-v7.5.0.56.tgz
      #Move the unpacked contents to your CUDA directory
      sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.0/lib64/
      sudo cp  cuda/include/cudnn.h /usr/local/cuda-10.0/include/
      # Give read access to all users
      sudo chmod a+r /usr/local/cuda-10.0/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
  6. Install libcupti-dev
    sudo apt-get install libcupti-dev nvidia-modprobe
  7. Post Install actions (define paths in ~/.bashrc or ~/.zshrc (for zsh users) )
    export PATH=/usr/local/cuda-10.0/bin:$PATH
    export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
  8. Finally install tensorflow-gpu using pip
    pip install --upgrade tensorflow-gpu
  9. Check the status of tf in python3
     from tensorflow.python.client import device_lib

    Output should be something like :

    [name: "/device:CPU:0"
    device_type: "CPU"
    memory_limit: 268435456
    locality {
    incarnation: 2919472738789042941
    , name: "/device:XLA_GPU:0"
    device_type: "XLA_GPU"
    memory_limit: 17179869184
    locality {
    incarnation: 17258321204030932820
    physical_device_desc: "device: XLA_GPU device"
    , name: "/device:XLA_CPU:0"
    device_type: "XLA_CPU"
    memory_limit: 17179869184
    locality {
    incarnation: 1501423454042607117
    physical_device_desc: "device: XLA_CPU device"
    , name: "/device:GPU:0"
    device_type: "GPU"
    memory_limit: 1649016832
    locality {
     bus_id: 1
     links {
    incarnation: 1732355850559614807
    physical_device_desc: "device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0"

    And you will have a Working GPU with tensorflow. Happy Coding.