I have a GPU server running on an Ubuntu 16.04 distribution and an Nvidia 1080 TI. I bought it in 2018 and to this day, it’s still using Cuda 9.0 with a driver version of 384.130.

My setup

I’ve decided to upgrade it to Cuda 10.2 and use the latest version of the driver (440.33.01). I wanted to do this for a long time for a couple of reasons:

• I occasionally noticed an unstable training and GPU failures with my current setup
• Some of the latest versions of common deep learning frameworks (PyTorch mainly) were running on Cuda 10+ only

So it was the perfect time to do it. I documented the different steps in this post. Let’s go!

1. head over https://developer.nvidia.com/cuda-downloads
2. select your
3. • operating system (Linux)
4. • architecture (x86_64)
5. • distribution (Ubuntu)
6. • version (16.04)
7. • installer type (runfile (local))
8. Once you’ve selected the elements, you’ll be prompted with the two following commands. Execute them

wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run

sudo sh cuda_10.2.89_440.33.01_linux.run

4. Reboot your machine

**sudo reboot now**

You should be okay now. However, if you encounter this error message when running nvidia-smi

Failed to initialize NVML: Driver/library version mismatch

This means that your previous driver was too old to be detected and removed by the**cuda_10.2.89_440.33.01_linux.run**script.

That’s why you’ll need to purge Nvidia old dependencies:

sudo apt-get update
sudo apt-get --purge remove cuda
sudo apt-get autoremove
dpkg --list |grep "^rc" | cut -d " " -f 3 | xargs sudo dpkg --purge
sudo apt-get purge nvidia*
sudo apt-get update
sudo reboot now

#nvidia #hardware #machine-learning #deep-learning #deep learning