I found that even if I set all the seed and cudnn, I still can not get deterministic result. The seed and cudnn settings are:
	random.seed(args.seed)
	np.random.seed(args.seed)
	torch.manual_seed(args.seed)
	if torch.cuda.is_available():
		torch.cuda.manual_seed(args.seed)
		torch.cuda.manual_seed_all(args.seed)
	torch.backends.cudnn.benchmark = False
	torch.backends.cudnn.deterministic = True

I use following settings:
FloatTensor as default Tensor.
CUDA version is 9.0.176.
cudnn version is 7102.
Pytorch version is 0.4.1.post2.
GPU is GTX1080.
Python version is 3.5.6.
I ran a nlp task and the loss between two runs:


The loss of these two runs between Iter 0~17 are the same, however, in Iter 18, one is 0.8873478174 and another is 0.8873476982.
Since this is a nlp task, I believe that there is no randomness in the data preprocessing porcess. I have checked the dataloader and set ‘num_workers’ as default setting. I also check the data of these two runs and they are the same.
I really want to know the reason why this happen. I’ve been dealing with the problem for few weeks. It totally drives me crazy.
I also searched for some related discussion, and I found that a topic discussed that this issue is caused by FloatTensor and need to switch to DoubleTensor. If it is true, is there any way to get deterministic result using FloatTensor?
I really appreciate it if someone could discuss with me.