added docs page

README.md

@@ -28,31 +28,16 @@ This is a research tool I built for myself internally while doing my PhD. The AP
 ## What is it?  
 Keras is too abstract for researchers. Lightning abstracts the full training loop but gives you control in the critical points.   
 
+
+## Why do I want to use lightning?
+Because you want to use best practices and get gpu training, multi-node training, checkpointing, mixed-precision, etc... for free.
+
 To use lightning do 2 things:
 1. Define a model with the lightning interface.
 2. Feed this model to the lightning trainer.
 
 *Example model definition*
 ```python
-from pytorch_lightning import RootModule
-
-class MyModel(RootModule):
-
-    def init(self): # define model
-    def training_step(self, data_batch, batch_nb): # what to do with a training batch
-    def validation_step(self, data_batch, batch_nb): # what to do with a val/test batch
-    def validation_end(self, data_batch, batch_nb): # collate all val batch outputs
-    def get_save_dict(self): # return what to save in a checkpoint
-    def load_model_specific(self, checkpoint): # use the checkpoint to reset your model state
-    def configure_optimizers(self): # return a list of optimizers
-    def tng_dataloader(self): # return a pytorch dataloader for each split
-    def val_dataloader(self):
-    def test_dataloader(self):
-    def add_model_specific_args(parent_parser): # add args for this model to your argparse
-```   
-
-*Example trainer*
-```python
 from pytorch_lightning import Trainer
 from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
 
@@ -63,16 +48,10 @@ trainer = Trainer(
     early_stop_callback=EarlyStopping(...),
     gpus=[0,1]
 )
+
+trainer.fit(model)
 ```
 
-Pytorch   
-<-- Lightning   
-Your model.   
-
-## Why do I want to use lightning?
-Because you want to use best practices and get gpu training, multi-node training, checkpointing, mixed-precision, etc... for free.
-
-
 ## What are some key lightning features?
 
 - Automatic training loop
@@ -86,11 +65,6 @@ def training_step(self, data_batch, batch_nb):
 # define what happens for validation here
 def validation_step(self, data_batch, batch_nb):
 ```
-- Automatic early stopping   
-```python
-callback = EarlyStopping(...)
-Trainer(early_stopping=callback)
-```
 
 - Automatic early stopping   
 ```python
@@ -105,7 +79,9 @@ Trainer(lr_scheduler_milestones=[100, 200])
 ```
 
 - 16 bit precision training   
-```--use_amp```
+```python
+Trainer(use_amp=True, amp_level='O2')
+```
 
 - multi-gpu training
 ```python