CUDA

Installation

I suggest using conda to install cuda for version control your project.

Note that nvidia-smi lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.
You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver.

Conda

See nvidia/cuda-toolkit and nvidia/cuda-libraries-dev

For example:

# Install the runtime only
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
# Install the runtime and the development tools
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-libraries-dev cuda-nvcc

Ubuntu

CUDA Toolkit

Expand

Details

See CUDA Ubuntu Installation

# Set UBUNTU_VERSION to 2004 or 2204 or 2404
UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g')

# Install nvidia driver
sudo apt install nvidia-driver-565

Notes

• For machine learning, use Anaconda or Docker's CUDA since different versions of TensorFlow and PyTorch require different CUDA versions.

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