From d2474313007e71a736ba0e646b702151e110b0dc Mon Sep 17 00:00:00 2001
From: William Falcon <waf2107@columbia.edu>
Date: Thu, 16 Jan 2020 07:45:36 -0500
Subject: [PATCH] finished rebase

---
 pytorch_lightning/trainer/__init__.py | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/pytorch_lightning/trainer/__init__.py b/pytorch_lightning/trainer/__init__.py
index 9ce668ea..893b5cbe 100644
--- a/pytorch_lightning/trainer/__init__.py
+++ b/pytorch_lightning/trainer/__init__.py
@@ -1,6 +1,17 @@
 """
- It calls parts of your model when it wants to hand over full control and otherwise makes
- training assumptions which are now standard practice in AI research.
+
+The trainer de-couples the engineering code (16-bit, early stopping, GPU distribution, etc...) from the
+science code (GAN, BERT, your project, etc...). It uses many assumptions which are best practices in 
+AI research today.
+
+The trainer automates all parts of training except:
+
+- what happens in training , test, val loop
+- where the data come from
+- which optimizers to use
+- how to do the computations 
+
+The Trainer delegates those calls to your LightningModule which defines how to do those parts.  
 
 This is the basic use of the trainer:
 
@@ -8,15 +19,10 @@ This is the basic use of the trainer:
 
     from pytorch_lightning import Trainer
 
-    model = LightningTemplate()
+    model = MyLightningModule()
 
     trainer = Trainer()
     trainer.fit(model)
-
-The Trainer holds all the engineering code you might need such as distributing over GPUs or early stopping.
-The LightningTemplate holds the core computations, train, val, test loop, optimizer and dataloaders.
-
-This pattern de-couples the engineering from the science which makes your code reusable and free to run on any hardware.
 """
 
 from .trainer import Trainer