mlflow checkpoints in the wrong location 
I'm not sure if I'm doing something wrong, I'm using mlflow instead of tensorboard as a logger. I've used the defaults i.e.
mlflow = loggers.MLFlowLogger()
trainer = pl.Trainer.from_argparse_args(args, logger=mlflow)

I'm ending up with the following folder structure
\mlflow
\mlflow\1
\mlflow\1\{guid}\artifacts
\mlflow\1\{guid}\metrics
\mlflow\1\{guid}\params
\mlflow\1\{guid}\meta.yaml
\1\{guid}\checkpoints
i.e. the checkpoints are in the wrong location, they should be in the \mlflow folder.
Perhaps this is an mlflow rather than pytorch-lightning issue?
I'm using pytorch-lightning 0.8.5 on macos running in python 3.7.6