Fixes #292 (#303)

* early stopping callback is not default

* added a default logger

* added default checkpoint callback

* added default checkpoint/loggers

* added default checkpoint/loggers

* updated docs

* cleaned demos

* cleaned demos

* cleaned demos

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

docs/Trainer/Checkpointing.md

@@ -2,13 +2,20 @@ Lightning can automate saving and loading checkpoints.
 
 ---
 ### Model saving
-To enable checkpointing, define the checkpoint callback and give it to the trainer.
+Checkpointing is enabled by default to the current working directory.
+To change the checkpoint path pass in :
+```python
+Trainer(default_save_path='/your/path/to/save/checkpoints')
+```
+
+To modify the behavior of checkpointing pass in your own callback.
 
 ``` {.python}
 from pytorch_lightning.callbacks import ModelCheckpoint
 
+# DEFAULTS used by the Trainer
 checkpoint_callback = ModelCheckpoint(
-    filepath='/path/to/store/weights/',
+    filepath=os.getcwd(),
     save_best_only=True,
     verbose=True,
     monitor='val_loss',

docs/Trainer/Logging.md

@@ -1,16 +1,33 @@
 Lighting offers options for logging information about model, gpu usage, etc, via several different logging frameworks. It also offers printing options for training monitoring.
 
+---   
+### default_save_path   
+Lightning sets a default TestTubeLogger and CheckpointCallback for you which log to
+```os.getcwd()``` by default. To modify the logging path you can set:
+```python
+Trainer(default_save_path='/your/path/to/save/checkpoints')
+```
+ 
+If you need more custom behavior (different paths for both, different metrics, etc...)
+from the logger and the checkpointCallback, pass in your own instances as explained below.
+
 
 ---
 ### Setting up logging
 
-Initialize your logger, which should inherit from `LightningBaseLogger`, and pass
-it to `Trainer`.
+The trainer inits a default logger for you (TestTubeLogger). All logs will
+go to the current working directory under a folder named ```os.getcwd()/lightning_logs``. 
+
+If you want to modify the default logging behavior even more, pass in a logger
+(which should inherit from `LightningBaseLogger`).   
+
 ```{.python}
 my_logger = MyLightningLogger(...)
 trainer = Trainer(logger=my_logger)
 ```
 
+The path in this logger will overwrite default_save_path.
+
 Lightning supports several common experiment tracking frameworks out of the box
 
 ---

docs/Trainer/Training Loop.md

@@ -21,17 +21,18 @@ trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)
 
 ---
 #### Early stopping
-To enable early-stopping, define the callback and give it to the trainer.
+The trainer already sets up default early stopping for you. 
+To modify this behavior, pass in your own EarlyStopping callback.
 ``` {.python}
 from pytorch_lightning.callbacks import EarlyStopping
 
-# DEFAULTS
+# DEFAULTS used by Trainer
 early_stop_callback = EarlyStopping(
     monitor='val_loss',
     min_delta=0.00,
-    patience=0,
+    patience=3,
     verbose=False,
-    mode='auto'
+    mode='min'
 )
 
 trainer = Trainer(early_stop_callback=early_stop_callback)