CUDA: Difference between revisions

Latest revision as of 08:46, 12 December 2024

Installation

I suggest using conda to install cuda for version control your project.

Note that nvidia-smi lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.
You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver.

Conda

See nvidia/cuda-toolkit and nvidia/cuda-libraries-dev

For example:

# 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

Ubuntu

CUDA Toolkit

# Install drivers
sudo apt install nvidia-driver-565-open

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

References

An Even Easier Introduction To Cuda

@@ Line 11: / Line 11: @@
 For example:
 <syntaxhighlight lang="bash">
-# Install the runtime
+# Install the runtime only
 conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
-# Install runtime and the development tools
+# Install the runtime and the development tools
-conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-libraries-dev
+conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-libraries-dev cuda-nvcc
 </syntaxhighlight>
@@ Line 20: / Line 20: @@
 [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">
-# 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>
-;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===