CUDA

Installation

Linux

Reference

• Install the latest nvidia drivers from the standard repo, e.g. nvidia-drivers-450
  
• Install Cuda Toolkit separately without the drivers.
  
  • Use one of the deb install options.
• For machine learning, you may also want to install the following:
  • cuDNN
  • TensorRT

Adapted from Tensorflow: Install cuda with apt

See also CUDA Ubuntu Installation

# Add NVIDIA package repositories
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"

wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb
sudo apt update

# Install NVIDIA driver
sudo apt install nvidia-driver-465
# Reboot. Check that GPUs are visible using the command: nvidia-smi
sudo apt install cuda

# Install development and runtime libraries
sudo apt install libcudnn8 libcudnn8-dev
#sudo apt-get install -y libnvinfer6 libnvinfer-dev libnvinfer-plugin6

For TensorFlow and PyTorch, you may need to add LD_LIBRARY_PATH=/usr/local/cuda/lib64 to your environment variables.
You can also do this in PyCharm.

GCC Versions

nvcc sometimes only supports older gcc/g++ versions.
To make it use those by default, create the following symlinks:

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

Alternatively, you can use -ccbin and point to your gcc:

-ccbin /usr/local/cuda/bin/gcc

References

• An Even Easier Introduction To Cuda