updated docs

docs/Trainer/Checkpointing.md

@@ -29,11 +29,40 @@ Lightning will restore the session if you pass an experiment with the same versi
 from test_tube import Experiment
 
 exp = Experiment(version=a_previous_version_with_a_saved_checkpoint)
-Trainer(experiment=exp)
+trainer = Trainer(experiment=exp)
 
-trainer = Trainer(checkpoint_callback=checkpoint_callback)
-# the trainer is now restored
+# this fit call loads model weights and trainer state
+# the trainer continues seamlessly from where you left off
+# without having to do anything else.
+trainer.fit(model)
 ```
 
+The trainer restores:  
+- global_step    
+- current_epoch    
+- All optimizers    
+- All lr_schedulers    
+- Model weights
 
+You can even change the logic of your model as long as the weights and "architecture" of 
+the system isn't different. If you add a layer, for instance, it might not work.   
 
+At a rough level, here's [what happens inside Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/model_saving.py#L63):   
+```python
+
+self.global_step = checkpoint['global_step']
+self.current_epoch = checkpoint['epoch']
+
+# restore the optimizers
+optimizer_states = checkpoint['optimizer_states']
+for optimizer, opt_state in zip(self.optimizers, optimizer_states):
+    optimizer.load_state_dict(opt_state)
+
+# restore the lr schedulers
+lr_schedulers = checkpoint['lr_schedulers']
+for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
+    scheduler.load_state_dict(lrs_state)
+
+# uses the model you passed into trainer        
+model.load_state_dict(checkpoint['state_dict'])
+```