Weird performance differences with RandomSampler/torch.randperm

Not sure what I am doing wrong but I have had some weird issues where if I use with (i.e. DataLoader(shuffle=True)) I get dramatically different F1 scores when I load identical data from different locations. One location is a local drive and the other is a NFS drive. Both locations contain exactly the same files when checked with md5sum over each one. I found that if I create my own version of RandomSampler (below) I no longer see the difference in performance. I think the issue might be with randperm if my code is correct (the issue exists on both CPU and GPU).

class RandomSampler(
    def __iter__(self):
        n = len(self.data_source)
        if self.replacement:
            return iter(np.random.randomint(0, n, size=n).tolist())
        arr = np.arange(n)
        return iter(arr.tolist())

I would post a minimal working example here but not sure how to reduce my working code (dataset is >1GB as well) to reproduce.

Is this effect reproducible, i.e. how many times have you trained your model from the local drive and the NFS drive?

I’ve been able to reproduce it dozens of times now (took me a long time to figure out this was the difference). Very puzzled as to why.