DDP training creates multiple directories when ModelCheckpoint filepath contains timestamp
🐛 Bug

When running single-node, multi-GPU training, the combination of ModelCheckpoint with timestamps in the filepath and the default Trainer creates multiple directories with slightly different timestamps. This happens when the fit() method is called from the Trainer. For single-node, 4 GPU training, this results in 4 directories being created, 3 of which remain empty during training.
To Reproduce
Steps to reproduce the behavior:

Run code sample supplied below.


Code sample

import os
from argparse import ArgumentParser
from datetime import datetime

from pytorch_lightning import LightningModule
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint


class MyModel(LightningModule):
    def __init__(self):
        super().__init__()

    def training_step(self, idx, batch_idx):
        return 0

    def train_dataloader(self):
        return None

    def configure_optimizers(self):
        return None


def main(args):

    checkpoint_callback = ModelCheckpoint(filepath=os.path.join(args.save_dir, "{epoch}"))
    trainer = Trainer.from_argparse_args(args, checkpoint_callback=checkpoint_callback)
    model = MyModel()
    trainer.fit(model)


if __name__ == "__main__":

    parser = ArgumentParser(add_help=False)
    parser = Trainer.add_argparse_args(parser)
    args = parser.parse_args()
    args.save_dir = os.path.join(".", datetime.now().strftime("%Y%m%d_%H%M%S"))

    main(args)

Expected behavior

Ideally, only one directory would be made when instantiating the ModelCheckpoint callback (which is the case now) and when the Trainer calls fit(), other processes would inherit that directory.
Environment
* CUDA:
        - GPU:
                - Tesla V100-SXM2-16GB
                - Tesla V100-SXM2-16GB
                - Tesla V100-SXM2-16GB
                - Tesla V100-SXM2-16GB
        - available:         True
        - version:           10.2
* Packages:
        - numpy:             1.18.1
        - pyTorch_debug:     False
        - pyTorch_version:   1.5.0
        - pytorch-lightning: 0.9.0rc12
        - tensorboard:       2.2.1
        - tqdm:              4.46.0
* System:
        - OS:                Linux
        - architecture:
                - 64bit
                - 
        - processor:         x86_64
        - python:            3.7.7
        - version:           #1 SMP Mon Jul 29 17:46:05 UTC 2019

Additional context

I am aware that the supplied code sample results in errors, but they are unrelated to the issue at hand. Empty directories are still being created.