Logging prints nothing in the following code:
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals

import os, logging
#logging.basicConfig(level=logging.DEBUG)

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as mp

from torch.nn.parallel import DistributedDataParallel as DDP


def setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'

    # Initialize the process group.
    dist.init_process_group('NCCL', rank=rank, world_size=world_size)


def cleanup():
    dist.destroy_process_group()


class ToyModel(nn.Module):
    def __init__(self):
        super(ToyModel, self).__init__()
        self.net1 = nn.Linear(10, 10)
        self.relu = nn.ReLU()
        self.net2 = nn.Linear(10, 5)

    def forward(self, x):
        return self.net2(self.relu(self.net1(x)))


def demo_basic(rank, world_size):
    setup(rank, world_size)

    logger = logging.getLogger('train')
    logger.setLevel(logging.DEBUG)
    logger.info(f'Running DPP on rank={rank}.')

    # Create model and move it to GPU.
    model = ToyModel().to(rank)
    ddp_model = DDP(model, device_ids=[rank])

    loss_fn = nn.MSELoss()
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)  # optimizer takes DDP model.

    optimizer.zero_grad()
    inputs = torch.randn(20, 10)  # .to(rank)

    outputs = ddp_model(inputs)

    labels = torch.randn(20, 5).to(rank)
    loss_fn(outputs, labels).backward()

    optimizer.step()

    cleanup()


def run_demo(demo_func, world_size):
    mp.spawn(
        demo_func,
        args=(world_size,),
        nprocs=world_size,
        join=True
    )


def main():
    run_demo(demo_basic, 4)


if __name__ == "__main__":
    main()

However, when we uncomment the 6th line, the logging works. May I know the reason and how to fix the bug please?