For those of you who have been building a new box. I just finished installing nvidia cuda and cudnn drivers last night. Had some issues getting the drivers my Ubuntu box, but found a very useful tutorial is actually found on the opencv website. If someone is just setting up their desktop build for the first time, hope these notes are helpful.
If anyone else has any feedback or installation notes, or a better guide, I would be interested in their experiences as well. I know some of my fellow USF master’s students have been re-configuring old computers to use as DL boxes.
Installation Guide covers:
- installation of Nvidia drivers on Ubuntu, specifically CUDA, CUDNN drivers
- setup of python environments for deep learning frameworks (ignore if you want to use conda for package installations)
A couple of caveats:
Know your Framework / Driver Version Compatibility: Before you start installing any of the software, note compatibility issues with Torch. From the website the only links available are for CUDA 7.5 or 8.0, which are older versions. To make Torch run on CUDA 9, you have to clone a repo + install (a bit more complicated)
Restart your comp after drivers are installed: Once the CUDA is installed, make sure to reboot your machine to make sure the drivers are installed.
Check versions between CUDA + CUDNN Make sure the CUDNN + CUDA versions are matched correctly with the framework you want to use
Note Python version + Framework Compatibility: If ever interested in tensorflow, make sure your python version matches (sometimes TF is looking for 3.5 instead of the current 3.6)
Recommend the .deb installation method : There’s two ways of installing the nvidia CUDA drivers, the .deb / local run file option. IMO, the .deb(local) approach is much cleaner and easier to manage. (see img below)
Installing Deep Learning Frameworks on Ubuntu with Cuda Support
Intel i5 (from 2011)
32 GB RAM
1 x 500GB SSD ubuntu
Some other HD’s for storage and a Windows Boot