update readme

README.md

 # tensorpack
 Neural Network Toolbox on TensorFlow
 
-In development but usable. API might change a bit.
+Still in development, but usable.
 
 See some interesting [examples](https://github.com/ppwwyyxx/tensorpack/tree/master/examples) to learn about the framework:
 
@@ -14,10 +14,14 @@ See some interesting [examples](https://github.com/ppwwyyxx/tensorpack/tree/mast
 
 Focused on modularity. Just have to define the three components in training:
 
-1. The model, or the graph. Define its input and output. `models/` has some scoped abstraction of common models.
+1. The model, or the graph. Define the graph as well as its inputs and outputs. `models/` has some scoped abstraction of common models.
 
-2. The data. All data producer has a unified `DataFlow` interface, and this interface can be chained
-	 to perform complex preprocessing. It uses multiprocess to avoid performance bottleneck.
+2. The data. All data producer has an unified `DataFlow` interface, and this interface can be chained
+	 to perform complex preprocessing. It uses multiprocess to avoid performance bottleneck on data
+	 loading.
 
-3. The callbacks. They include everything you want to do besides the training iterations:
-	change hyperparameters, save model, print logs, run validation, and more.
+3. The callbacks. They include everything you want to do apart from the training iterations:
+	change hyperparameters, save models, print logs, run validation, and more.
+
+With the above components defined, tensorpack trainer will run the training iterations for you.
+Multi-GPU training is ready to use by simply changing the trainer.