After spending more than a week trying to set up my Windows 7 machine on AWS, I want to offer some advice for prepping yourself for the course:
I don’t recommend installing Cygwin to use as a Linux terminal as Jeremy does in the (rather dated) setup video, particularly if you already use Git Bash. The installation alone created conflicts that rendered Git Bash unusable; a nightmare within my nightmare. I backed out of the problem by restoring my Win 7 system to an earlier point.
The Github repository that someone kindly posted, https://github.com/reshamas/fastai_deeplearn_part1/blob/master/tools/aws_ami_gpu_setup.md, was the most helpful resource I found, although it is a bit sketchy for those unfamiliar with working in Linux shell. For instance, one step calls for editing the jupyter_configuration.py file after generating it. I don’t know how to do this (not everyone taking the course majored in CS, did they?). Wasted a lot of time trying to figure out how, and I’m not sure if I successfully did it. Things seem to be working, in any case, but I’m still just at the beginning.
Difficulties aside, it’s probably worth the pain to familiarize oneself with using a service like AWS, as it’s likely to be something you use in the future. I’m easily frustrated, but I learned a lot by calling the billing department and asking many questions even though they don’t really have much technical knowledge. Incidentally, paying for “premium” support might not be a bad way to go because once you’re up and running you can cancel the contract and only be charged for it on a prorated basis ($29/month). I probably would have learned more and faster if I’d done that.