Code giving exceeded job memory limited

for epoch in range(initial_epoch, max_epochs):

	start = time.time()
        total_loss = 0
        for batch, sample_batched in enumerate(dataloader):
	    batch_RGBsT, batch_trimapsT, batch_alphasT, batch_BGsT, batch_FGsT, RGBs_with_meanT = Variable(sample_batched['batch_RGBsT']),Variable(sample_batched['batch_trimapsT']),Variable(sample_batched['batch_alphasT']), Variable(sample_batched['batch_BGsT']), Variable(sample_batched['batch_FGsT']), Variable(sample_batched['RGBs_with_meanT'])
            if USE_CUDA:
                batch_RGBsT, batch_trimapsT, batch_alphasT, batch_BGsT, batch_FGsT, RGBs_with_meanT = [batch_RGBsT.cuda(), batch_trimapsT.cuda(), batch_alphasT.cuda(), batch_BGsT.cuda(), batch_FGsT.cuda(), RGBs_with_meanT.cuda()]

            # initilize gradients
            #print(batch_RGBsT.shape, batch_trimapsT.shape)
            b_input =,batch_trimapsT),1)

            # predictions
            alpha_loss = model(b_input, batch_alphasT, batch_trimapsT)
            alpha_loss = alpha_loss.mean()
            total_loss += alpha_loss
            print_freq = 1000
            if(batch % print_freq == 0 and not batch==0):
                print('Epoch:',epoch,'Batch:', batch, 'Loss:',total_loss/float(print_freq))
                total_loss = 0
                is_best = best_prec1 > total_loss/float(print_freq)
                    'epoch': epoch + 1,
                    'state_dict': model.state_dict(),
                    'best_prec1': total_loss/float(print_freq),
                    'optimizer' : optimizer.state_dict(),
                }, is_best)
        end = time.time()
        print('Time for 1 epoch: ', end-start)

Above code runs over 43100 set of images with a batch size of 16, but once a epoch is completed leads to exceeded job memory limited error. If the whole code could successfully run over a 43100 images in one epoch. what could lead it to job memory limit exceeded error in second epoch?

The line

total_loss += alpha_loss

adds alpha_loss with its entire computation graph to total_loss. Try this instead.

total_loss +=[0]

However total_loss is reset to 0 every print_freq batches which will free up the alpha_loss computation graphs that were attached to it, so that probably isn’t the reason why one epoch succeeds and the second fails.

I don’t see where you set best_prec1, but I’m guessing it carries around a computation graph too.