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 1: / Line 1: @@
 ==Installation==
-===Linux===
+I suggest using conda to install cuda for version control your project.
-[https://www.pugetsystems.com/labs/hpc/How-To-Install-CUDA-10-1-on-Ubuntu-19-04-1405/#Step3)InstallCUDA\ Reference]
-* Install the latest nvidia drivers from the standard repo, e.g. <code>nvidia-drivers-450</code><br>
+Note that <code>nvidia-smi</code> lists the maximum CUDA version supported by the GPU driver, not the installed version of CUDA.<br>
-* Install [https://developer.nvidia.com/cuda-toolkit Cuda Toolkit] separately without the drivers.<br>
+You can have a different version of CUDA installed in each conda environment, independently of the version supported by the GPU driver.
-** Use one of the deb install options.
-* You may also want to install the following:
-** [https://developer.nvidia.com/rdp/cudnn-download cuDnn]<br>
-** TensorRT
-;Adapted from tensorflow
+===Conda===
-<pre>
+See [https://anaconda.org/nvidia/cuda-toolkit nvidia/cuda-toolkit] and [https://anaconda.org/nvidia/cuda-libraries-dev nvidia/cuda-libraries-dev]
-# Add NVIDIA package repositories
-wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
-sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
-sudo dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
-sudo apt-get update
-wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
-sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
-sudo apt-get update
-# Install NVIDIA driver
-sudo apt-get install --no-install-recommends nvidia-driver-450
-# Reboot. Check that GPUs are visible using the command: nvidia-smi
-# Install development and runtime libraries (~4GB)
-sudo apt-get install --no-install-recommends \
-    cuda-10-1 \
-    libcudnn7=7.6.5.32-1+cuda10.1  \
-    libcudnn7-dev=7.6.5.32-1+cuda10.1
+For example:
+<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>
-# Install TensorRT. Requires that libcudnn7 is installed above.
+===Ubuntu===
-sudo apt-get install -y --no-install-recommends libnvinfer6=6.0.1-1+cuda10.1 \
+[https://developer.nvidia.com/cuda-toolkit CUDA Toolkit]
-    libnvinfer-dev=6.0.1-1+cuda10.1 \
-    libnvinfer-plugin6=6.0.1-1+cuda10.1
-</pre>
-For tensorflow and pytorch, 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>
-==GCC Versions==
+===GCC Versions===
 <code>nvcc</code> sometimes only supports older gcc/g++ versions.
 To make it use those by default, create the following symlinks:
@@ Line 50: / Line 31: @@
 * <code>sudo ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc</code>
 * <code>sudo ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++</code>
+Alternatively, you can use <code>-ccbin</code> and point to your gcc:
+<pre>
+-ccbin /usr/local/cuda/bin/gcc
+</pre>
 ==References==
 * [https://devblogs.nvidia.com/even-easier-introduction-cuda/ An Even Easier Introduction To Cuda]
+[[Category:Programming languages]]
+[[Category:GPU Programming languages]]