I wonder do I need the latest CUDA and cuDNN libraries installed to follow fast.ai notebooks? I am working on 1 part now (lesson 3) and I’ve installed required packages onto my desktop machine a couple of months ago. And, I’m using CUDA 8.0 with cuDNN 5.3.
Therefore, my question is, do I need to update to the most recent versions of these tools or can continue to follow lessons by updating only fast.ai package itself?
Also, probably a bit off-topic, is it possible to keep several CUDA versions installed?
Agree, Docker is a good idea. I guess there should be a few pre-build Docker containers for PyTorch and TensorFlow libs. Or just minimalistic one with CUDA only installed. By the way, could you please share a link to some good container to start with? (If you use any, of course).
Now I am using this setup on my machine, but I would say having a few “clean” containers with CUDA 8/9 only would be great.
I guess that the simplest approach to have several CUDA versions is to tweak CUDA symlink (e. g. by swapping destination from /usr/local/cuda -> /usr/local/cuda-8.0 to /usr/local/cuda -> /usr/local/cuda-9.0).
The approach with activate script is just a cleaner way of kinda doing exactly that.