Logging with Distributed Data Parallel with PyTorch

I am trying to setup a training workflow with PyTorch DistributedDataParallel (DDP). Generally when I train I pass a logger through to track outputs and record useful information. However, I am having trouble using the logger I have with the DDP method. Right now my code is as follows:

import torch
import torch.multiprocessing as mp


class BaseModel:
    def __init__(self, *args, **kwargs):
        ...
        "does things"

    def fit(self, *args, **kwargs):
        ...
        'set up stuff'
        mp.spawn(self.distributed_training, nprocs=self.num_gpus, args=(self.params, training_input, self.logger))

    def distributed_training(params, training_input, logger):
        ...
        for e in epochs:
            'trains for an epoch'
            logger.info(print_line)

I know I am supposed to use the QueueHandler and QueueListener tools from logging with the import, but I have been scouring the internet and still do not have a clear understanding as to how. Any help would be greatly appreciated.