RuntimeError: cuda runtime error (710) : device-side assert triggered at

  Traning the image classification with pytorch  got following
  error messageK





  RuntimeError                              Traceback (most recent call
  last)  in 
       29         print(len(train_loader.dataset),len(valid_loader.dataset))
       30         #break
  ---> 31         train_loss, train_acc ,model= train(model, device, train_loader, optimizer, criterion)
       32         valid_loss, valid_acc,model = evaluate(model, device, valid_loader, criterion)
       33 
  
   in train(model, device, iterator,
  optimizer, criterion)
       21         acc = calculate_accuracy(fx, y)
       22         #print("5.")
  ---> 23         loss.backward()
       24 
       25         optimizer.step()
  
  ~/venv/lib/python3.7/site-packages/torch/tensor.py in backward(self,
  gradient, retain_graph, create_graph)
      164                 products. Defaults to False.
      165         """
  --> 166         torch.autograd.backward(self, gradient, retain_graph, create_graph)
      167 
      168     def register_hook(self, hook):
  
  ~/venv/lib/python3.7/site-packages/torch/autograd/init.py in
  backward(tensors, grad_tensors, retain_graph, create_graph,
  grad_variables)
       97     Variable._execution_engine.run_backward(
       98         tensors, grad_tensors, retain_graph, create_graph,
  ---> 99         allow_unreachable=True)  # allow_unreachable flag
      100 
      101 
  
  RuntimeError: cuda runtime error (710) : device-side assert triggered
  at /pytorch/aten/src/THC/generic/THCTensorMath.cu:26


Related code block is here 

def train(model, device, iterator, optimizer, criterion):

print('train')
epoch_loss = 0
epoch_acc = 0

model.train()


for (x, y) in iterator:
    #print(x,y)
    x,y = x.cuda(), y.cuda()
    #x = x.to(device)
    #y = y.to(device)
    #print('1')
    optimizer.zero_grad()
    #print('2')
    fx = model(x)
    #print('3')
    loss = criterion(fx, y)
    #print("4.loss-&gt;",loss)
    acc = calculate_accuracy(fx, y)
    #print("5.")
    loss.backward()

    optimizer.step()

    epoch_loss += loss.item()
    epoch_acc += acc.item()

return epoch_loss / len(iterator), epoch_acc / len(iterator),model


    EPOCHS = 5
    SAVE_DIR = 'models'
    MODEL_SAVE_PATH = os.path.join(SAVE_DIR, 'please.pt')
    from torch.utils.data import DataLoader
    best_valid_loss = float('inf')

    if not os.path.isdir(f'{SAVE_DIR}'):
        os.makedirs(f'{SAVE_DIR}')
    print("start")
    for epoch in range(EPOCHS):
        print('================================',epoch ,'================================')
        for i , (train_idx, valid_idx) in enumerate(zip(train_indexes, valid_indexes)):
            print(i,train_idx,valid_idx,len(train_idx),len(valid_idx))

            traindf = df_train.iloc[train_index, :].reset_index()
            validdf = df_train.iloc[valid_index, :].reset_index()

            #traindf = df_train
            #validdf = df_train

            train_dataset = TrainDataset(traindf, mode='train', transforms=data_transforms)
            valid_dataset = TrainDataset(validdf, mode='valid', transforms=data_transforms)

            train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
            valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)



            print(len(train_loader.dataset),len(valid_loader.dataset))
            #break
            train_loss, train_acc ,model= train(model, device, train_loader, optimizer, criterion)
            valid_loss, valid_acc,model = evaluate(model, device, valid_loader, criterion)

            if valid_loss &lt; best_valid_loss:
                best_valid_loss = valid_loss
                torch.save(model,MODEL_SAVE_PATH)

            print(f'| Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:05.2f}% | Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:05.2f}% |')



  splits = zip(train_indexes, valid_indexes)
  [ 3692  3696  3703 ... 30733 30734 30735] [   0    1    2 ... 4028
  4041 4046] [    0     1     2 ... 30733 30734 30735] [3692 3696 3703
  ... 7986 7991 8005] [    0     1     2 ... 30733 30734 30735] [ 7499 
  7500  7502 ... 11856 11858 11860] [    0     1     2 ... 30733 30734
  30735] [11239 11274 11280 ... 15711 15716 15720] [    0     1     2
  ... 30733 30734 30735] [15045 15051 15053 ... 19448 19460 19474] [
  0     1     2 ... 30733 30734 30735] [18919 18920 18926 ... 23392
  23400 23402] [    0     1     2 ... 30733 30734 30735] [22831 22835
  22846 ... 27118 27120 27124] [    0     1     2 ... 27118 27120 27124]
  [26718 26721 26728 ... 30733 30734 30735]


What was your loss function?
I got this error too. My problem was a multi-class classification and I was using a crossEntropy loss.
As it say in the documentations, labels should be in the range [0, C-1] where C is number of classes.
But my labels were not in the range and when I used proper values for labels, Everything was ok.
