Same error no change I guess multiprocessing is not supported …
import torch

def main():
    for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels,img_name = data
            inputs = inputs.to(device) 
            labels = labels.to(device)
            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            total=0
            correct=0
            if i % 2000 == 1999: 
                # print every 2000 mini-batches

                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))

    print('Finished Training')

if __name__ == '__main__':
    main()

I guess I should give up running on Windows and just go for linux …
Error I get  is for above code is …
BrokenPipeError                           Traceback (most recent call last)
<ipython-input-103-4ec33088ead4> in <module>
     29 
     30 if __name__ == '__main__':
---> 31     main()

<ipython-input-103-4ec33088ead4> in main()
      2 
      3 def main():
----> 4     for i, data in enumerate(trainloader, 0):
      5             # get the inputs; data is a list of [inputs, labels]
      6             inputs, labels,img_name = data

~\Documents\Python\lib\site-packages\torch\utils\data\dataloader.py in __iter__(self)
    191 
    192     def __iter__(self):
--> 193         return _DataLoaderIter(self)
    194 
    195     def __len__(self):

~\Documents\Python\lib\site-packages\torch\utils\data\dataloader.py in __init__(self, loader)
    467                 #     before it starts, and __del__ tries to join but will get:
    468                 #     AssertionError: can only join a started process.
--> 469                 w.start()
    470                 self.index_queues.append(index_queue)
    471                 self.workers.append(w)

~\Documents\Python\lib\multiprocessing\process.py in start(self)
    110                'daemonic processes are not allowed to have children'
    111         _cleanup()
--> 112         self._popen = self._Popen(self)
    113         self._sentinel = self._popen.sentinel
    114         # Avoid a refcycle if the target function holds an indirect

~\Documents\Python\lib\multiprocessing\context.py in _Popen(process_obj)
    221     @staticmethod
    222     def _Popen(process_obj):
--> 223         return _default_context.get_context().Process._Popen(process_obj)
    224 
    225 class DefaultContext(BaseContext):

~\Documents\Python\lib\multiprocessing\context.py in _Popen(process_obj)
    320         def _Popen(process_obj):
    321             from .popen_spawn_win32 import Popen
--> 322             return Popen(process_obj)
    323 
    324     class SpawnContext(BaseContext):

~\Documents\Python\lib\multiprocessing\popen_spawn_win32.py in __init__(self, process_obj)
     63             try:
     64                 reduction.dump(prep_data, to_child)
---> 65                 reduction.dump(process_obj, to_child)
     66             finally:
     67                 set_spawning_popen(None)

~\Documents\Python\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
     58 def dump(obj, file, protocol=None):
     59     '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60     ForkingPickler(file, protocol).dump(obj)
     61 
     62 #

BrokenPipeError: [Errno 32] Broken pipe


Fastai code for small dataset I remmber using it which worked  on gpu
import torch
print("cuda Available",torch.cuda.is_available())
# torch.cuda.current_device()
s=time.time()
print("time started at:",s)
tfms = get_transforms(do_flip=False)
#path = '/content/drive/My Drive/Colab Notebooks/fastai/'
path = './'
data = ImageDataBunch.from_folder(path,ds_tfms=tfms, size=224)
# data.show_batch(rows=3, figsize=(10,10))
my_trained_mod = create_cnn(data, models.resnet50, metrics=error_rate)
my_trained_mod.unfreeze()
my_trained_mod.fit_one_cycle(6,max_lr=slice(1e-5,1e-3))

my_trained_mod.save("trained_model")

my_trained_mod.export()
print(time.time()-s,'seconds')