Yes, Jacob, I read that thread and I tried to run the notebook you kindly shared and it fails early on in the very first training call. I thought perhaps there were some changes in the main version of fastai, so I forked the current version on github, applied the changes you suggested to remove do_gpu and it fails just the same.
That’s why I’m asking for help 
running your branch:
!pip uninstall -y fastai
!pip install git+https://github.com/jfpettit/fastai
this section fails:
arch=resnet34
#data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=32)
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=16)
learn = ConvLearner.pretrained(arch, data, precompute=True)
learn.fit(0.01, 3)
Downloading: "https://download.pytorch.org/models/resnet34-333f7ec4.pth" to /content/.torch/models/resnet34-333f7ec4.pth
100%|██████████| 87306240/87306240 [00:03<00:00, 25361894.91it/s]
0%| | 0/1438 [00:00<?, ?it/s]
RuntimeError Traceback (most recent call last)
in ()
2 #data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=32)
3 data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=16)
----> 4 learn = ConvLearner.pretrained(arch, data, precompute=True)
5 learn.fit(0.01, 3)
/usr/local/lib/python3.6/dist-packages/fastai/conv_learner.py in pretrained(cls, f, data, ps, xtra_fc, xtra_cut, **kwargs)
97 def pretrained(cls, f, data, ps=None, xtra_fc=None, xtra_cut=0, **kwargs):
98 models = ConvnetBuilder(f, data.c, data.is_multi, data.is_reg, ps=ps, xtra_fc=xtra_fc, xtra_cut=xtra_cut)
—> 99 return cls(data, models, **kwargs)
100
101 @property
/usr/local/lib/python3.6/dist-packages/fastai/conv_learner.py in init(self, data, models, precompute, **kwargs)
90 elif self.metrics is None:
91 self.metrics = [accuracy_thresh(0.5)] if self.data.is_multi else [accuracy]
—> 92 if precompute: self.save_fc1()
93 self.freeze()
94 self.precompute = precompute
/usr/local/lib/python3.6/dist-packages/fastai/conv_learner.py in save_fc1(self)
142 m=self.models.top_model
143 if len(self.activations[0])!=len(self.data.trn_ds):
–> 144 predict_to_bcolz(m, self.data.fix_dl, act)
145 if len(self.activations[1])!=len(self.data.val_ds):
146 predict_to_bcolz(m, self.data.val_dl, val_act)
/usr/local/lib/python3.6/dist-packages/fastai/model.py in predict_to_bcolz(m, gen, arr, workers)
12 m.eval()
13 for x,*_ in tqdm(gen):
—> 14 y = to_np(m(VV(x)).data)
15 with lock:
16 arr.append(y)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
323 for hook in self._forward_pre_hooks.values():
324 hook(self, input)
–> 325 result = self.forward(*input, **kwargs)
326 for hook in self._forward_hooks.values():
327 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py in forward(self, input)
65 def forward(self, input):
66 for module in self._modules.values():
—> 67 input = module(input)
68 return input
69
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
323 for hook in self._forward_pre_hooks.values():
324 hook(self, input)
–> 325 result = self.forward(*input, **kwargs)
326 for hook in self._forward_hooks.values():
327 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self, input)
275 def forward(self, input):
276 return F.conv2d(input, self.weight, self.bias, self.stride,
–> 277 self.padding, self.dilation, self.groups)
278
279
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in conv2d(input, weight, bias, stride, padding, dilation, groups)
88 _pair(0), groups, torch.backends.cudnn.benchmark,
89 torch.backends.cudnn.deterministic, torch.backends.cudnn.enabled)
—> 90 return f(input, weight, bias)
91
92
RuntimeError: Input type (CUDAFloatTensor) and weight type (CPUFloatTensor) should be the same
since I am still getting the same error!