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| ==Installation== | | ==Installation== |
| ===Ubuntu 20.04===
| | I suggest using conda to install cuda for version control your project. |
| [https://developer.nvidia.com/cuda-toolkit CUDA Toolkit]
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| | |
| See [https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation CUDA Ubuntu Installation]
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| <syntaxhighlight lang="bash">
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| # Add NVIDIA package repositories
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| wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
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| sudo mv cuda-ubuntu2004.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/ubuntu2004/x86_64/7fa2af80.pub
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| sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
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| # Add the ML Repo (Optional)
| | Note that <code>nvidia-smi</code> lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.<br> |
| wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb
| | You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver. |
| sudo apt install ./nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb
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| sudo apt update
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| # Install NVIDIA driver and cuda.
| | ===Conda=== |
| sudo apt install nvidia-driver-515 cuda
| | See [https://anaconda.org/nvidia/cuda-toolkit nvidia/cuda-toolkit] and [https://anaconda.org/nvidia/cuda-libraries-dev nvidia/cuda-libraries-dev] |
| # Reboot and check that the drivers are working with nvidia-smi
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| sudo reboot
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| # Install cudnn (Optional) | | For example: |
| sudo apt install libcudnn8 libcudnn8-dev
| | <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> | | </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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| ==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: |