I have a question for the PyTorch development team.
How is the memory consumed by queues in PyTorch implementation of multi-processing libraries managed?
If you can point me to the relevant piece of code (if available) and/or provide a textual description, I would appreciate it.
@VitalyFedyunin Could you help out here since its a torch.multiprocessing question?
as methods are different for CPU/GPU
generally speaking we are passing storage descriptors and do usage ref counting.