Hi,
I want to parallelize the inner loop of MAML.
Each inner loop of the MAML will produce individual loss along with individual gradient graphs,
and after the iteration, I have to aggregate the losses followed by backpropagation.
My naive idea is replacing the loop to map.
To do this, I guess I need to aggregate the loss from multiple threads.
(e.g. torch.mean(torch.stack(list_of_loss_from_multiple_threads))
Is it possible to aggregate graphs from worker threads and then do the backprop at once?
Thanks