Using s3 backed for Mlflow logger fails
🐛 Bug
MLflow logger support for s3 URI's. It is already supported by MLflow 1.9.0, https://www.mlflow.org/docs/latest/tracking.html#amazon-s3, but passing an s3 URI to the logger's tracking_uri parameter fails.
To Reproduce
Steps to reproduce the behavior:

init a lighting_module and a trainer.
init a mlflow_logger with an s3 URI set for 'tracking_uri'.
run the trainer


mlflow.tracking.registry.UnsupportedModelRegistryStoreURIException:  Model registry functionality is unavailable; got unsupported URI 's3://somebucket/' for model registry data storage. Supported URI schemes are: ['', 'file', 'databricks', 'http', 'https', 'postgresql', 'mysql', 'sqlite', 'mssql']. See https://www.mlflow.org/docs/latest/tracking.html#storage for how to run an MLflow server against one of the supported backend storage locations.
Code sample
model = somedummymodel()  mlflow_logger = MLFlowLogger( experiment_name='some_name', tracking_uri="s3://somebucket/" ) trainer = LT(logger=mlflow_logger) trainer.fit(model, somedata, somedata_val)
Expected behavior
It should post logs into the bucket using the mlfow tracking server feature.
Environment

CUDA:
- GPU:
- GeForce RTX 2080 Ti
- GeForce RTX 2080 Ti
- available:         True
- version:           10.0.130
Packages:
- numpy:             1.17.0
- pyTorch_debug:     False
- pyTorch_version:   1.3.1
- pytorch-lightning: 0.8.1
- tensorboard:       2.2.2
- tqdm:              4.46.1
System:
- OS:                Linux
- architecture:
- 64bit
-
- processor:         x86_64
- python:            3.7.6
- version:           #53~18.04.1-Ubuntu SMP Thu Jun 4 14:58:26 UTC 2020