docs about inference

docs/tutorial/index.rst

@@ -21,6 +21,7 @@ User Tutorials
   callback
   save-load
   summary
+  inference
   faq
 
 

docs/tutorial/inference.md

+
+# Inference
+
+## Inference During Training
+
+There are two ways to do inference during training.
+
+1. The easiest way is to write a callback, and use
+`  self.trainer.get_predictor()` to get a callable under inference mode.
+	See [Write a Callback](extend/callback.html)
+
+2. If your inference follows the paradigm of:
+	"fetch some tensors for each input, and aggregate the results".
+	You can use the `InferenceRunner` interface with some `Inferencer`.
+	This will further support prefetch & data-parallel inference.
+	More details to come.
+
+
+## Inference After Training
+
+Tensorpack doesn't care what happened after training.
+It saves models to standard checkpoint format, plus a metagraph protobuf file.
+They are sufficient to use with whatever deployment methods TensorFlow supports.
+But you'll need to read the docs and do it on your own.
+
+The only thing tensorpack has is `OfflinePredictor`,
+a simple function to build the graph and a callable for you.
+It only runs inference on Python data, therefore may not be the best way.
+It is mainly for quick demo purpose.
+Check out some examples for the usage.