# Cuda no longer work when installing new ubuntu update

## post by binhvanpham on Sep 21, 2018

Hi I just recently make update to latest ubuntu version. However, cuda no longer work.

When I compile using nvcc I got this msg

nvcc warning : The ‘compute\_20’, ‘sm\_20’, and ‘sm\_21’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).

In file included from /usr/local/cuda/bin/…//include/cuda\_runtime.h:78:0,
from :0:
/usr/local/cuda/bin/…//include/host\_config.h:119:2: error: #error – unsupported GNU version! gcc versions later than 5 are not supported!
#error – unsupported GNU version! gcc versions later than 5 are not supported!
^~~~~

Would you please help reinstall CUDA?

Thank you,

–Binh Pham

## post by sabalaba on Sep 24, 2018

Hey Binh,

You can try to use Lambda Stack. [Lambda Stack: an AI software stack that's always up-to-date](/content/lambda-stack-deep-learning-software/index.html)

That will install TensorFlow with GPU support / CUDA / Drivers / etc. Let us know if that one-liner works:

```bash

LAMBDA_REPO=$(mktemp) && 
wget -O${LAMBDA_REPO} https://lambdalabs.com/static/misc/lambda-stack-repo.deb && 
sudo dpkg -i ${LAMBDA_REPO} && rm -f ${LAMBDA_REPO} && 
sudo apt-get update && sudo apt-get install -y lambda-stack-cuda
```

## post by binhvanpham on Sep 24, 2018

Hi,

The command works after I added --allow-downgrades. However, when I run a test cuda code, it doesn’t work. It gives me wrong result. This code works before. Below is my test code and output.

CODE:

```c
#include <stdio.h>

**global** void cube(float * d\_out, float * d\_in){

// Todo: Fill in this function

int idx = threadIdx.x;

float f = d\_in[idx];

d\_out[idx] = f _f_ f;
}

int main(int argc, char ** argv) {

const int ARRAY\_SIZE = 96;

const int ARRAY\_BYTES = ARRAY\_SIZE * sizeof(float);

// generate the input array on the host

float h\_in[ARRAY\_SIZE];

for (int i = 0; i < ARRAY\_SIZE; i++) {

h\_in[i] = float(i);
}

float h\_out[ARRAY\_SIZE];

// declare GPU memory pointers

float * d\_in;

float * d\_out;

// allocate GPU memory

cudaMalloc((void**) &d\_in, ARRAY\_BYTES);

cudaMalloc((void**) &d\_out, ARRAY\_BYTES);

// transfer the array to the GPU

cudaMemcpy(d\_in, h\_in, ARRAY\_BYTES, cudaMemcpyHostToDevice);

// launch the kernel

cube<<<1, ARRAY\_SIZE>>>(d\_out, d\_in);

// copy back the result array to the CPU

cudaMemcpy(h\_out, d\_out, ARRAY\_BYTES, cudaMemcpyDeviceToHost);

// print out the resulting array

for (int i =0; i < ARRAY\_SIZE; i++) {

printf(“%f”, h\_out[i]);

printf(((i % 4) != 3) ? “\t” : “\n”);
}

cudaFree(d\_in);

cudaFree(d\_out);

return 0;
}
```

OUTPUT:

64956.226562 -3442308913561600.000000 -0.040856 -8858266431913984.000000

0.000000 0.000000 0.000000 0.000000

0.000000 0.000000 -0.000000 0.000000

0.000000 0.000000 -nan -nan

0.000000 0.000000 0.000000 0.000000

0.000000 0.000000 0.000000 0.000000

0.000000 0.000000 0.000000 0.000000

0.000000 0.000000 -49057123615670591891177472.000000 0.000005

-0.000000 0.000000 -344859.468750 0.000000

0.000000 0.000000 0.000000 0.000000

0.000000 0.000000 -49057123615670591891177472.000000 0.000005

-0.000000 0.000000 -702221.000000 0.000000

-6630776053649813224172895258552565760.000000 0.000000 -702323.312500 0.000000

-1446412.000000 0.000000 0.000000 0.000000

-0.000000 0.000000 -702231.437500 0.000000

-0.000000 0.000000 -0.000000 0.000000

-1446958.000000 0.000000 -0.000000 0.000000

-0.000000 0.000000 -427953.250000 0.000000

-0.000000 0.000000 -307492.281250 0.000000

0.000000 0.000000 0.000000 0.000000

-0.000000 0.000000 -0.000000 0.000000

0.000000 0.000000 -0.000000 0.000000

-1172276.000000 0.000000 0.000000 0.000000

-0.000000 0.000000 -0.000000 0.000000

Please help,

Thanks Sabalaba,

–Binh Pham

## post by sabalaba on Oct 3, 2018

What is the expected output? What did you used to get on the old version? Seems odd that it would be different.