How To Run CUDA C or C++ on Google Colab or Azure Notebook

Harshit Yadav

3 min readAug 23, 2019

CUDA code doesn’t run on AMD CPU or Intel HD graphics unless you have a NVIDIA Hardware inside you Machine

Step 1: Go to https://colab.research.google.com in Browser and Click on New Python 3 Notebook

Step 2: Click to Runtime > Change > Hardware Accelerator GPU .

Step 3: Refresh the Cloud Instance of CUDA On Server [write code in a Seprate code Block and Run that]

!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

Step 4: Install CUDA Version 9 [write code in a Seprate code Block and Run that]

!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
!apt-get update
!apt-get install cuda-9.2

Step 5: Check the Version of CUDA by : running the command below to get the following output :

!nvcc --version

Output :

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation

Step 6 : Execute the given command to install a small extension to run nvcc from Notebook cells [write code in a Seprate code Block and Run that]

!pip install git+git://github.com/andreinechaev/nvcc4jupyter.git

Step 7: Load the extension using this code:[write code in a Seprate code Block and Run that]

%load_ext nvcc_plugin

Important : To check the Code run the following snippet in [write code in a Seprate code Block and Run that only again when changing the code and re running it]

%%cu
#include <stdio.h>
#include <stdlib.h>
__global__ void add(int *a, int *b, int *c) {
*c = *a + *b;
}
int main() {
int a, b, c;
// host copies of variables a, b & c
int *d_a, *d_b, *d_c;
// device copies of variables a, b & c
int size = sizeof(int);
// Allocate space for device copies of a, b, c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
// Setup input values  
c = 0;
a = 3;
b = 5;
// Copy inputs to device
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
  cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
// Launch add() kernel on GPU
add<<<1,1>>>(d_a, d_b, d_c);
// Copy result back to host
cudaError err = cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
  if(err!=cudaSuccess) {
      printf("CUDA error copying to Host: %s\n", cudaGetErrorString(err));
  }
printf("result is %d\n",c);
// Cleanup
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}

The Ouptut should be 8

For Next time just run to following script to setup the environment [LINK]

PS : Working on how to Pass Command Line Argument

[Azure notebook do not support GPU yet and code works on CPU but is slower]

Refernces :

How To Run CUDA C or C++ on Google Colab or Azure Notebook

Written by Harshit Yadav