diff --git a/doc/SERVER_DAG.md b/doc/SERVER_DAG.md
index da840f525f1156f6723f64ac8f640e74ced0845b..12566d612bf2f0e391283ec58df12a53752333ba 100644
--- a/doc/SERVER_DAG.md
+++ b/doc/SERVER_DAG.md
@@ -4,13 +4,18 @@ This document shows the concept of computation graph on server. How to define co
 
 ## Computation Graph on Server
 
-Deep neural nets often have some preprocessing steps on input data, and postprocessing steps on model inference scores. Since deep learning frameworks are now very flexible, it is possible to do preprocessing and postprocessing outside the training computation graph. If we want to do input data preprocessing and inference result postprocess on server side, we have to add the corresponding computation logics on server. Moreover, if a user wants to do inference with the same inputs on more than one model, the best way is to do the inference concurrently on server side given only one client request so that we can save some network computation overhead. For the above two reasons, it is naturally to think of a Directed Acyclic Graph(DAG) as the main computation method for server inference. One example of DAG is as follows. [one possible DAG on Serving](server_dag.png)
-
+Deep neural nets often have some preprocessing steps on input data, and postprocessing steps on model inference scores. Since deep learning frameworks are now very flexible, it is possible to do preprocessing and postprocessing outside the training computation graph. If we want to do input data preprocessing and inference result postprocess on server side, we have to add the corresponding computation logics on server. Moreover, if a user wants to do inference with the same inputs on more than one model, the best way is to do the inference concurrently on server side given only one client request so that we can save some network computation overhead. For the above two reasons, it is naturally to think of a Directed Acyclic Graph(DAG) as the main computation method for server inference. One example of DAG is as follows:
 
+<center>
+<img src='server_dag.png' width = "450" height = "500" align="middle"/>
+</center>
 
 ## How to define Node
 
 PaddleServing has some predefined Computation Node in the framework. A very commonly used Computation Graph is the simple reader-inference-response mode that can cover most of the single model inference scenarios. A example graph and the corresponding DAG defination code is as follows.
+<center>
+<img src='simple_dag.png' width = "260" height = "370" align="middle"/>
+</center>
 
 ``` python
 import paddle_serving_server as serving