CUDA: Difference between revisions
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==Installation== | ==Installation== | ||
I suggest using conda to install cuda for version control your project. | |||
Note that <code>nvidia-smi</code> lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.<br> | |||
You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver. | |||
===Conda=== | |||
See [https://anaconda.org/nvidia/cuda-toolkit nvidia/cuda-toolkit] and [https://anaconda.org/nvidia/cuda-libraries-dev nvidia/cuda-libraries-dev] | |||
For example: | |||
<syntaxhighlight lang="bash"> | |||
# 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 | |||
</syntaxhighlight> | |||
===Ubuntu=== | ===Ubuntu=== | ||
[https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] | [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] | ||
{{hidden | Details | | |||
See [https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation CUDA Ubuntu Installation] | See [https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation CUDA Ubuntu Installation] | ||
<syntaxhighlight lang="bash"> | <syntaxhighlight lang="bash"> | ||
# Set UBUNTU_VERSION to 2004 or 2204 | # Set UBUNTU_VERSION to 2004 or 2204 | ||
UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g') | UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g') | ||
# Install nvidia driver | |||
sudo apt install nvidia-driver-545 | |||
# Add NVIDIA package repositories | # Add NVIDIA package repositories | ||
| Line 15: | Line 35: | ||
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/ /" | sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/ /" | ||
# Install | # Install cuda. | ||
sudo apt install | sudo apt install cuda | ||
# Reboot and check that the drivers are working with nvidia-smi | # Reboot and check that the drivers are working with nvidia-smi | ||
sudo reboot | sudo reboot | ||
# Install cudnn | # Install cudnn if needed | ||
sudo apt install libcudnn8 libcudnn8-dev | sudo apt install libcudnn8 libcudnn8-dev | ||
</syntaxhighlight> | </syntaxhighlight> | ||
| Line 27: | Line 47: | ||
* For machine learning, use Anaconda or Docker's CUDA since different versions of TensorFlow and PyTorch require different CUDA versions. | * For machine learning, use Anaconda or Docker's CUDA since different versions of TensorFlow and PyTorch require different CUDA versions. | ||
You may need to add <code>LD_LIBRARY_PATH | You may need to add <code>LD_LIBRARY_PATH=/usr/local/cuda/lib64</code> to your environment variables.<br> | ||
You can also do this in PyCharm.<br> | You can also do this in PyCharm.<br> | ||
[[File:Pycharm LD LIBRARY PATH config.png| 200x200px]] | [[File:Pycharm LD LIBRARY PATH config.png| 200x200px]] | ||
[[File:Pycharm LD LIBRARY PATH console config.png| 200x200px]] | [[File:Pycharm LD LIBRARY PATH console config.png| 200x200px]] | ||
==GCC Versions== | }} | ||
===GCC Versions=== | |||
<code>nvcc</code> sometimes only supports older gcc/g++ versions. | <code>nvcc</code> sometimes only supports older gcc/g++ versions. | ||
To make it use those by default, create the following symlinks: | To make it use those by default, create the following symlinks: | ||
Latest revision as of 16:15, 23 April 2024
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
Details
# Set UBUNTU_VERSION to 2004 or 2204
UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g')
# Install nvidia driver
sudo apt install nvidia-driver-545
# Add NVIDIA package repositories
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-ubuntu${UBUNTU_VERSION}.pin
sudo mv cuda-ubuntu${UBUNTU_VERSION}.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/ /"
# Install cuda.
sudo apt install cuda
# Reboot and check that the drivers are working with nvidia-smi
sudo reboot
# Install cudnn if needed
sudo apt install libcudnn8 libcudnn8-dev
- Notes
- For machine learning, use Anaconda or Docker's CUDA since different versions of TensorFlow and PyTorch require different CUDA versions.
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/gccsudo 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