Does PyTorch seed affect dropout layers?

Question

I came across the idea of seeding my neural network for reproducible results, and was wondering if pytorch seeding affects dropout layers and what is the proper way to seed my training/testing?

I'm reading the documentation here, and wondering if just placing these lines will be enough?

torch.manual_seed(1)
torch.cuda.manual_seed(1)

Answer 1

You can easily answer your question with some lines of code:

import torch
from torch import nn

dropout = nn.Dropout(0.5)
torch.manual_seed(9999)
a = dropout(torch.ones(1000))
torch.manual_seed(9999)
b = dropout(torch.ones(1000))
print(sum(abs(a - b)))
# > tensor(0.)

Yes, using manual_seed is enough.