CUDA: Difference between revisions
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# Add NVIDIA package repositories | # Add NVIDIA package repositories | ||
wget https://developer.download.nvidia.com/compute/cuda/repos/ | wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin | ||
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ | sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600 | ||
sudo apt | 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 | |||
sudo apt install ./nvidia-machine-learning-repo- | 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-get update | sudo apt-get update | ||
# Install NVIDIA driver | # Install NVIDIA driver | ||
sudo apt-get install --no-install-recommends nvidia-driver- | sudo apt-get install --no-install-recommends nvidia-driver-460 | ||
# Reboot. Check that GPUs are visible using the command: nvidia-smi | # Reboot. Check that GPUs are visible using the command: nvidia-smi | ||
# Install development and runtime libraries (~4GB) | # Install development and runtime libraries (~4GB) | ||
sudo apt-get install --no-install-recommends \ | sudo apt-get install --no-install-recommends \ | ||
cuda | cuda libcudnn7 libcudnn7-dev | ||
# Install TensorRT. Requires that libcudnn7 is installed above. | # Install TensorRT. Requires that libcudnn7 is installed above. | ||
sudo apt-get install -y | sudo apt-get install -y libnvinfer6 libnvinfer-dev libnvinfer-plugin6 | ||
</syntaxhighlight> | </syntaxhighlight> | ||
Revision as of 05:22, 6 February 2021
Installation
Linux
- 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:
- Adapted from Tensorflow: Install cuda with apt
# 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-get update
# Install NVIDIA driver
sudo apt-get install --no-install-recommends nvidia-driver-460
# Reboot. Check that GPUs are visible using the command: nvidia-smi
# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
cuda libcudnn7 libcudnn7-dev
# Install TensorRT. Requires that libcudnn7 is installed above.
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++