Enable any ML experiment tracking framework (#223)

* Implement generic loggers for experiment tracking

* Add tests for loggers

* Get model tests passing

* Test and fix logger pickling

* Expand pickle test and fix bug

* Missed exp -> logger conversion

* Remove commented code

* Add docstrings

* Update logging docs

* Add mlflow to test requirements

* Make linter happy

* Fix mlflow timestamp

* Update Logging.md

* Update test_models.py

* Update test_models.py

* Update test_models.py

* Update properties.md

* Fix tests

* Line length

docs/LightningModule/properties.md

@@ -9,12 +9,20 @@ The current epoch
 Current dtype    
 
 ---
-#### experiment    
-An instance of test-tube Experiment which you can use to log anything for tensorboard (subclass of [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)).   
+#### logger    
+A reference to the logger you passed into trainer. 
+```python
+Trainer(logger=your_logger)
+```    
+
+Call it from anywhere in your LightningModule to add metrics, images, etc... whatever your logger supports.  
+
+Here is an example using the Test-tube logger (which is a wrapper on [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html) with versioned folder structure).     
 ```{.python}
-self.experiment.add_embedding(...)   
-self.experiment.log({'val_loss': 0.9})   
-self.experiment.add_scalars(...)   
+# if logger is a tensorboard logger or test-tube experiment
+self.logger.add_embedding(...)   
+self.logger.log({'val_loss': 0.9})   
+self.logger.add_scalars(...)   
 ```
 
 ---