Hi, I implemented my own collate_fn as I need to pad data up to a variable length. The length up to which the batch has to be padded is also determined in this function.
When I set the num_workers > 0 in the DataLoader, it runs fine on my cpu. However, und gpu I get the following error:
RuntimeError: cuda runtime error (3) : initialization error at /pytorch/aten/src/THC/THCCachingAllocator.cpp:507
I read several similar threads, where it is recommended to set the start method for multiprocesing like this:
torch.multiprocessing.set_start_method('forkserver')
Now if I do that, I get the following error on both my cpu and gpu:
AttributeError: Can't pickle local object 'MyDataset.get_collate_fn.<locals>.collate_fn'
Maybe important to know is that I pass the collate_fn to the Dataloader like this:
class MyDataset(Dataset)
    ...
    @static_method
    def get_collate(device):
        def collate_fn(batch):
            ...
            batch = batch.to(device)
        return collate_fn

data_loader = DataLoader(MyDataset(), collate_fn=MyDataset.get_collate(device))