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| ==Installation== | | ==Installation== |
| ===Ubuntu===
| | I suggest using conda to install cuda for version control your project. |
| [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit]
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| {{hidden | Details |
| | Note that <code>nvidia-smi</code> lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.<br> |
| See [https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation CUDA Ubuntu Installation]
| | You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver. |
| <syntaxhighlight lang="bash"> | |
| # Set UBUNTU_VERSION to 2004 or 2204
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| UBUNTU_VERSION=$(lsb_release -sr | sed -e 's/\.//g')
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| # Add NVIDIA package repositories
| | ===Conda=== |
| wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-ubuntu${UBUNTU_VERSION}.pin
| | See [https://anaconda.org/nvidia/cuda-toolkit nvidia/cuda-toolkit] and [https://anaconda.org/nvidia/cuda-libraries-dev nvidia/cuda-libraries-dev] |
| sudo mv cuda-ubuntu${UBUNTU_VERSION}.pin /etc/apt/preferences.d/cuda-repository-pin-600
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| sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/3bf863cc.pub
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| sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/ /"
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| # Install NVIDIA driver and cuda. | | For example: |
| sudo apt install nvidia-driver-515 cuda
| | <syntaxhighlight lang="bash"> |
| # Reboot and check that the drivers are working with nvidia-smi | | # Install the runtime only |
| sudo reboot
| | conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit |
| | | # Install the runtime and the development tools |
| # Install cudnn
| | conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-libraries-dev cuda-nvcc |
| sudo apt install libcudnn8 libcudnn8-dev
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| </syntaxhighlight> | | </syntaxhighlight> |
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| ;Notes
| | ===Ubuntu=== |
| * For machine learning, use Anaconda or Docker's CUDA since different versions of TensorFlow and PyTorch require different CUDA versions.
| | [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] |
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| You may need to add <code>LD_LIBRARY_PATH=/usr/local/cuda/lib64</code> to your environment variables.<br>
| | <syntaxhighlight lang="bash"> |
| You can also do this in PyCharm.<br>
| | # Install drivers |
| [[File:Pycharm LD LIBRARY PATH config.png| 200x200px]]
| | sudo apt install nvidia-driver-565-open |
| [[File:Pycharm LD LIBRARY PATH console config.png| 200x200px]]
| | </syntaxhighlight> |
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| }}
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| ===Conda===
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| See [https://anaconda.org/nvidia/cuda-toolkit nvidia/cuda-toolkit] and [https://anaconda.org/nvidia/cuda-libraries-dev nvidia/cuda-libraries-dev]
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| ==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: |