CUDA: Difference between revisions
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==Installation== | ==Installation== | ||
===Linux=== | ===Linux=== | ||
[https://developer.nvidia.com/cuda-toolkit CUDA Toolkit] | |||
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] | ||
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# Install development and runtime libraries | # Install development and runtime libraries | ||
sudo apt install libcudnn8 libcudnn8-dev | sudo apt install libcudnn8 libcudnn8-dev | ||
</syntaxhighlight> | </syntaxhighlight> | ||
For | |||
;Notes | |||
* For machine learning, I just have Anaconda install a compatible CUDA since different versions of TF and PyTorch require different CUDA versions. | |||
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]] |
Revision as of 15:03, 18 April 2021
Installation
Linux
# 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 update
# Install NVIDIA driver
sudo apt install nvidia-driver-465
# Reboot
# Check that GPUs are visible using nvidia-smi
sudo apt install cuda
# Install development and runtime libraries
sudo apt install libcudnn8 libcudnn8-dev
- Notes
- For machine learning, I just have Anaconda install a compatible CUDA since different versions of TF 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/gcc
sudo 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