[Torch Geometric] Load multiple files using multiprocessing in Dataset get() method

I read here that we can use get() method in Dataset to load a single file before passing the Data type object as a list to DataLoader to be fed into model training. In my case, I want to load multiple files with multiprocessing instead of 1 file at a time to speed up the process. How can I do that? Thanks

To be clear, are you asking how to call get() in multiple processes simultaneously and gather each of their resulting Data objects in the main process? And do you want each subprocess to load multiple files? Here’s a simple example of using Pool to load two files, data0.pt and data1.pt, with two subprocesses:

import torch
from torch.multiprocessing import Pool

def load(idx):
    return torch.load(f'data{idx}.pt')

if __name__ == '__main__':
    with Pool(2) as p:
        data_list = p.map(load, [0,1])

but beware of the overhead of spawning subprocesses.

Hey @ArchieGertsman, thanks for the answer!
Basically, I want to do something like num_parallel_calls (for multiprocessing) and .prefetch() (to process the next batch of data in CPU while training is done in GPU) like in tf.data.Dataset here using torch geometric Dataset. How can we do that? What I understand so far __getitem__ or get() only loads 1 file at a time.