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Janardhan Pulivarthi
Janardhan Pulivarthi

Posted on • Edited on

NVIDIA CUDA setup on Colab

The faster way to CUDA programming. Just open it right now here ↗

or

visit colab.research.google.com for Google Colab for online jupyter notebooks. Make sure to set GPU accelerator in the Runtime -> Change runtime type.

set runtime type to GPU

1. Remove existing CUDA installation

This remove all the nvidia and libnvidia named packages and libraries in the system.

!apt-get --purge remove cuda nvidia* libnvidia-* !dpkg -l | grep cuda- | awk '{print $2}' | xargs -n1 dpkg --purge !apt-get remove cuda-* !apt autoremove !apt-get update
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2. Download and install latest CUDA

Visit https://developer.nvidia.com/cuda-downloads

Installer configuration

Latest example,

!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 !wget https://developer.download.nvidia.com/compute/cuda/11.6.0/local_installers/cuda-repo-ubuntu2004-11-6-local_11.6.0-510.39.01-1_amd64.deb !sudo dpkg -i cuda-repo-ubuntu2004-11-6-local_11.6.0-510.39.01-1_amd64.deb !sudo apt-key add /var/cuda-repo-ubuntu2004-11-6-local/7fa2af80.pub !sudo apt-get update !sudo apt-get -y install cuda
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3. Check the installation 

!nvcc --version
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and create simple .cu file

%%writefile /content/main.cu #include <iostream>  int main() { std::cout << "This is from CUDA\n"; return 0; }
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and compile it.

!nvcc main.cu -o main
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Resources

  1. https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html

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