Install-CUDA-12-cuDNN-8.9.5-PyTorch-v1.8.0-or-v1.9.0-and-python-3.9-for-deep-learning

• For avioding future errors plz install all these in a virtual enviroment.

1. Configuration

Ubuntu setup by running the following commands.

sudo apt-get update sudo apt-get upgrade -y
sudo apt-get install -y build-essential cmake unzip pkg-config
sudo apt-get install -y libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev
sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install -y libxvidcore-dev libx264-dev
sudo apt-get install -y libgtk-3-dev
sudo apt-get install -y libopenblas-dev libatlas-base-dev liblapack-dev gfortran
sudo apt-get install -y libhdf5-serial-dev graphviz
sudo apt-get install -y python3-dev python3-tk python-imaging-tk
sudo apt-get install -y linux-image-generic linux-image-extra-virtual
sudo apt-get install -y linux-source linux-headers-generic

2. Nvidia (Driver,CUDA, cuDNN)

2.1 Install Nvidia Driver

• Step 1 : Remove existing Nvidia drivers if any

  sudo apt-get purge nvidia*
  

• Step 2: Add Graphic Drivers PPA

  sudo add-apt-repository ppa:graphics-drivers/ppa
  sudo apt-get update
  

• Step 3: Search available drivers

  ubuntu-drivers devices
  

• Step 4: Install the driver with the best version

  sudo apt-get install nvidia-driver-"best_version"
  

  Reboot your computer after installation!!!

  Verification

  Type nvidia-smi in your evironment to see the GPU info and the processes that are using Nvidia GPU

  

2.2 Install CUDA 12.3 Toolkit

• If you want to install CUDA 12.3 version then follow steps otherwise go to CUDA Toolkit Downloads for your specified version and follow the steps of Base installer only.

• Note - At the time of making this documentation only deb(local) installer type was working.

  Base Installer

  • Run the following code in terminal

    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
    sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
    wget https://developer.download.nvidia.com/compute/cuda/12.3.0/local_installers/cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb
    sudo dpkg -i cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb
    sudo cp /var/cuda-repo-ubuntu2204-12-3-local/cuda-*-keyring.gpg /usr/share/keyrings/
    sudo apt-get update
    sudo apt-get -y install cuda-toolkit-12-3
    

  Add Cuda to Path

  • in Terminal activate your environment and write the following codes.

    sudo nano ~/.bashrc
    

  • Press ESC then i for inserting mode.

  • Now go to end and paste these.

    if [ -d "/usr/local/cuda/bin/" ]; then
    	export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
    fi
    

  Verification

  nvcc --version
  

  

2.3 Install cuDNN

• Just go here. You'll have to log in and download the tar file , so downloading of the right cuDNN binary packages cannot be easily automated.

  

• Once downloaded, un-tar the file and copy the contents to their respective locations:

  tar -xvf cudnn-linux-x86_64-8.9.5.29_cuda12-archive.tar.xz
  

• Copy the following files into the CUDA toolkit directory.

  sudo cp cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include
  sudo cp -P cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64 
  sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
  

3. Now install Tensorflow and PyTorch for verification

• Use the test_gpu.ipynb file in repo