PaddleServing在框架中具有一些预定义的计算节点。 一种非常常用的计算图是简单的reader-infer-response模式，可以涵盖大多数单一模型推理方案。 示例图和通过`Python API 启动Server`相应的DAG定义代码如下（`python/paddle_serving_server/serve.py`）。
@@ -16,12 +16,13 @@ Deep neural nets often have some preprocessing steps on input data, and postproc
### Simple series structure
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 definition code is as follows.
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. Here is a example of DAG graph.
If you use `the command line + configuration file method to start C++ server`, you only need to modify [the configuration file](./Serving_Configure_CN.md), don`t need to change any line of 👆 code.
For simple series logic, we simplify it and build it with `OpSeqMaker`. You can determine the successor by default according to the order of joining `OpSeqMaker` without specifying the successor of each node.
Since the code will be commonly used and users do not have to change the code, PaddleServing releases a easy-to-use launching command for service startup. An example is as follows:
