Hello. I generally run my experiments with
num_workers set to 1 in the
DataLoader. Recently I got a slightly more demanding dataset to generate and I thought of speeding things up by setting
num_workers to 8. Strangely, this stopped my model to learn completely. Why is this the case? My datasets are generated on the fly, if that’s relevant.
And, more generally, how does
num_workers work exactly? Could you point me to some docs? Each different worker works on a different batch and then they are collated together?
And, maybe related: since I write code on windows, all my code is wrapped around
if __name__ == '__main__': freeze_support() ....
would this afftect performance when ran on a non-windows machine?