Fixing the seed for torch random_split()
Is it possible to fix the seed for torch.utils.data.random_split() when splitting a dataset so that it is possible to reproduce the test results?

You can use torch.manual_seed function to seed the script globally:

import torch
torch.manual_seed(0)


See reproducibility documentation for more information.

If you want to specifically seed torch.utils.data.random_split you could "reset" the seed to it's initial value afterwards. Simply use torch.initial_seed() like this:

torch.manual_seed(torch.initial_seed())


AFAIK pytorch does not provide arguments like seed or random_state (which could be seen in sklearn for example).
