added docs page

README.md

@@ -26,7 +26,7 @@ pip install pytorch-lightning
 This is a research tool I built for myself internally while doing my PhD. The API is not 100% production quality, but my hope is that by open-sourcing, we can all get it there (I don't have too much time nowadays to write production-level code).
 
 ## What is it?  
-Keras is too abstract for researchers. Lightning makes it so you only have to define your model but still control all details of training if you need to.   
+Keras is too abstract for researchers. Lightning abstracts the full training loop but gives you control in the critical points.   
 
 To use lightning do 2 things:
 1. Define a model with the lightning interface.
@@ -69,12 +69,68 @@ Pytorch
 <-- Lightning   
 Your model.   
 
-**Lightning will do the following for you:**   
+## 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
+```python
+# define what happens for training here
+def training_step(self, data_batch, batch_nb):  
+```
+- Automatic validation loop   
+
+```python
+# 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
+callback = EarlyStopping(...)
+Trainer(early_stopping=callback)
+```
+
+- Learning rate annealing     
+```python
+# anneal at 100 and 200 epochs
+Trainer(lr_scheduler_milestones=[100, 200])
+```
+
+- 16 bit precision training   
+```--use_amp```
+
+- multi-gpu training
+```python
+# train on 4 gpus
+Trainer(gpus=[0, 1, 2, 3])
+```
+
+- Automatic checkpointing
+```python
+# do 3 things:
+# 1
+Trainer(checkpoint_callback=ModelCheckpoint)
+
+# 2 return what to save in a checkpoint
+def get_save_dict(self):
+    return {'state_dict': self.state_dict()}
+
+
+# 3 use the checkpoint to reset your model state
+def load_model_specific(self, checkpoint):
+    self.load_state_dict(checkpoint['state_dict'])
+```
+
+
 
-1. Run the training loop.   
-2. Run the validation loop.   
-3. Run the testing loop.   
-4. Early stopping.   
 5. Learning rate annealing.
 6. Can train complex models like GANs or anything with multiple optimizers.
 7. Weight checkpointing.