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I think I managed to fix the problem I was running into. The approach I had before didn't seem to take into account how Pytorch handled wrapping the Model class and distributing the necessary information to each forward function to perform the operations. I believe the trick to getting Pytorch to work was creating a custom function class inheriting from torch.autograd.
What I have, if I can look into it some more and fix some bugs, should be able to run with any number of GPUs while evenly distributing the image data, scored and classified arrays across them.