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CUDA: Difference between revisions

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==Installation==
==Installation==
I suggest using conda to install cuda for version control your project.
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===
===Conda===
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For example:
For example:
<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
# Install the runtime
# Install the runtime only
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
# Install development tools
# Install the runtime and the development tools
conda install -c "nvidia/label/cuda-11.8.0" cuda-libraries-dev
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-libraries-dev cuda-nvcc
</syntaxhighlight>
</syntaxhighlight>


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[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]
<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
# Set UBUNTU_VERSION to 2004 or 2204
# Install drivers
UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g')
sudo apt install nvidia-driver-565-open
 
# Install nvidia driver
sudo apt install nvidia-driver-530
 
# 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
sudo apt install libcudnn8 libcudnn8-dev
</syntaxhighlight>
</syntaxhighlight>
;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 <code>LD_LIBRARY_PATH&#x3D;/usr/local/cuda/lib64</code> to your environment variables.<br>
You can also do this in PyCharm.<br>
[[File:Pycharm LD LIBRARY PATH config.png| 200x200px]]
[[File:Pycharm LD LIBRARY PATH console config.png| 200x200px]]
}}


===GCC Versions===
===GCC Versions===
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