logger is NoneType hence doesn't have any experiment or other functionality in a lightning module
🐛 Bug
When trying to use the logging abilities of lightning, I hit a wall, the default and tensorboard loggers both seem to stay uninitialized when calling trainer.fit(model), resulting in crashes everytime I try to log something.
To Reproduce
Create a lightning module as such
class SimpleRegressor(pl.LightningModule):
    ...

Use the logger anywhere to get this kind of stacktrace:
d:\Documents\projects\MetaWatch\MetaWatch\notebooks\audio-video-interest\simple_regressor.py in configure_optimizers(self)
    105         #see https://pytorch-lightning.readthedocs.io/en/latest/pytorch_lightning.core.lightning.html#pytorch_lightning.core.lightning.LightningModule.configure_optimizers
    106         # REQUIRED
--> 107         self.logger.experiment.add_hparams({'hidden_layer_size':self.hidden_layer_size,
    108                                             'linear_layer_size':self.linear_layer_size,
    109                                             'lstm_layers':self.lstm_layers})

AttributeError: 'NoneType' object has no attribute 'experiment'

Code sample
import pytorch_lightning as pl

class SimpleRegressor(pl.LightningModule):
    def __init__(self, cuda=False):
        super(SimpleRegressor, self).__init__()
        self.logger.experiment.add_hparams({'hidden_layer_size':1})

Expected behavior
To log as described in the documentation.
Environment
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Microsoft Windows 10 Pro    
GCC version: Could not collect  
CMake version: Could not collect

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce GTX 970
Nvidia driver version: 441.12
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.18.1
[pip3] pytorch-lightning==0.6.0
[pip3] tinynumpy==1.2.1
[pip3] torch==1.4.0
[pip3] torchvision==0.4.1
[conda] Could not collect

Additional context