question: How to set Checkpoints to be used in the automatically generated `version_N` directories?
context: If the TensorBoard logger is set up as shown
  logger = TensorBoardLogger(name="MyModel")
  checkpoint_callback = ModelCheckpoint(
      filename="{epoch}-{step}-{val_loss:.2f}",
      monitor="val_loss",
      save_top_k=5,
  )
  trainer = pl.Trainer(
      default_root_dir=ROOT_DIR,
      callbacks=[checkpoint_callback],
      logger=[logger],
  )
  how do we configure the checkpoints to be written to directories that are automatically named version_0, version_1, the way it is if you do not pass a logger to Trainer?
  trainer = pl.Trainer(
      default_root_dir=ROOT_DIR,
      callbacks=[checkpoint_callback],
  )
  If we pass in a logger to Trainer, the checkpoints are written to
  <root_path>/<experiment_name>/<integer>/checkpoints
  
  while the tensorboard logs and hparams.yaml are written to
  <root_path>/<experiment_name>/version_<integer>/
  
  If we do not pass in a logger to Trainer, then checkpoint files, Tensorboard files and hparams.yaml are all written to the same directory
  <root_path>/<experiment_name>/version_<integer>/
  
  How can both checkpoints and tensorboard files we written to the same version_<integer> directory?
answer: Hi
  For this you need to set the "default_root_dir" in the Trainer, and set the save_dir of the Logger to the same.
  This works for me (latest PL version):
  from argparse import ArgumentParser
  
  import torch
  from torch.nn import functional as F
  
  import pytorch_lightning as pl
  from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.loggers import TensorBoardLogger
  
  
  class LitClassifier(pl.LightningModule):
  
      def __init__(self, hidden_dim=128, learning_rate=1e-3):
          super().__init__()
          self.save_hyperparameters()
  
          self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
          self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
  
      def forward(self, x):
          x = x.view(x.size(0), -1)
          x = torch.relu(self.l1(x))
          x = torch.relu(self.l2(x))
          return x
  
      def training_step(self, batch, batch_idx):
          x, y = batch
          y_hat = self(x)
          loss = F.cross_entropy(y_hat, y)
          return loss
  
      def validation_step(self, batch, batch_idx):
          x, y = batch
          y_hat = self(x)
          loss = F.cross_entropy(y_hat, y)
          self.log('valid_loss', loss)
  
      def configure_optimizers(self):
          return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
  
      @staticmethod
      def add_model_specific_args(parent_parser):
          parser = parent_parser.add_argument_group("LitClassifier")
          parser.add_argument('--hidden_dim', type=int, default=128)
          parser.add_argument('--learning_rate', type=float, default=0.0001)
          return parent_parser
  
  
  def cli_main():
      pl.seed_everything(1234)
  
      parser = ArgumentParser()
      parser = pl.Trainer.add_argparse_args(parser)
      parser = LitClassifier.add_model_specific_args(parser)
      parser = MNISTDataModule.add_argparse_args(parser)
      args = parser.parse_args()
  
      dm = MNISTDataModule.from_argparse_args(args, num_workers=2)
      model = LitClassifier(args.hidden_dim, args.learning_rate)
  
      ROOT_DIR = "here"
      mylogger = TensorBoardLogger(name="MyModel", save_dir=ROOT_DIR)
      ckpt_callback = ModelCheckpoint(monitor="valid_loss", filename="{epoch}-{step}-{valid_loss:.2f}")
      trainer = pl.Trainer.from_argparse_args(args, default_root_dir=ROOT_DIR, logger=mylogger, callbacks=[ckpt_callback], limit_train_batches=2, limit_val_batches=2)
      trainer.fit(model, datamodule=dm)
  
  
  if __name__ == '__main__':
      cli_main()