CUDA: Difference between revisions

CUDA (view source)

Revision as of 15:03, 18 April 2021

469 bytes removed , 18 April 2021

→‎Linux

David

Bureaucrats, Interface administrators, Administrators

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 ==Installation==
 ===Linux===
-[https://www.pugetsystems.com/labs/hpc/How-To-Install-CUDA-10-1-on-Ubuntu-19-04-1405/#Step3)InstallCUDA\ Reference]
+[https://developer.nvidia.com/cuda-toolkit CUDA Toolkit]
-* Install the latest nvidia drivers from the standard repo, e.g. <code>nvidia-drivers-465</code><br>
-* Install [https://developer.nvidia.com/cuda-toolkit Cuda Toolkit] separately without the drivers.<br>
-** Use one of the deb install options.
-* For machine learning, you may also want to install the following:
-** [https://developer.nvidia.com/rdp/cudnn-download cuDNN]
-** [https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing TensorRT]
 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
 sudo apt install libcudnn8 libcudnn8-dev
-#sudo apt-get install -y libnvinfer6 libnvinfer-dev libnvinfer-plugin6
 </syntaxhighlight>
-For TensorFlow and PyTorch, you may need to add <code>LD_LIBRARY_PATH=/usr/local/cuda/lib64</code> to your environment variables.<br>
+;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>
 [[File:Pycharm LD LIBRARY PATH config.png| 200x200px]]