ptrblck:

Basically, even if you set torch.backends.cudnn.deterministic = True , torch.backends.cudnn.benchmark = True might select different deterministic algorithms, which would yield different results.
There is a current PR  working on this.
Could you disable benchmarking and try it again?


Hi, I am also facing a similar issue
 torch.backends.cudnn.deterministic=True
 torch.backends.cudnn.benchmark=False


I do just after the imports, but I am still getting non deterministic behaviour. I have also tried torch.backends.cudnn.enabled=False as I read that cunn modules also provide deterministic behaviour.
I have shuffle turned off, model on eval mode, and backpropogating for the gradients of input image (for performing adversarial attacks), so I believe protocol is fulfilled ?
Link to thread I created 5 (which further contain links to questions I created)