To expand on @wgpubs a bit: I’m running through the course on a Mac, and the setup is not trivial but also not so much work that it’s not worth it in all cases. Depending on your personal circumstances, it could be worth the hour or so of setup for you – I happen to have a beefy CUDA-capable card and only get to work on the course in short bursts of time; leaving a GPU instance running somewhere so I don’t have to start from scratch each time doesn’t thrill me.
For reference, I’m on a Mac Pro 4,1 (firmware upgraded to 5,1) with a GTX 1080 Ti GPU. My CPUs are only occasionally the bottleneck.
Here are the steps I took to get everything working, in case someone else comes across this forum and needs help:
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Follow the instructions here, using environment-cpu.yml instead of environment.yml in the appropriate place: https://github.com/reshamas/fastai_deeplearn_part1/blob/master/tools/setup_personal_dl_box.md
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Install the CUDA + cuDNN developer tools from Nvidia.
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Download and install Xcode v. 9.2 (anything newer won’t work with nvcc, the Nvidia compiler). Use the xcode-select command line tool to make sure this is the default Xcode installation (needed for step 4, but then can be switched back). You may find this link useful: https://devtalk.nvidia.com/default/topic/1032646/cuda-setup-and-installation/macos-10-13-4-and-xcode-9-3-compatibility-broken-with-cuda-toolkit-9-1/post/5260119/#5260119
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Follow the instructions posted by @wgpubs above to compile pyTorch from source: pyTorch not working with an old NVidia card – if you’ve followed steps 2 and 3 above, it should in fact install pyTorch with GPU support.