From 947de3058c70c4f5fac118a63e0f94e3e359442f Mon Sep 17 00:00:00 2001
From: Dong Daxiang <35550832+guru4elephant@users.noreply.github.com>
Date: Sat, 22 Feb 2020 14:56:53 +0800
Subject: [PATCH] Update SERVER_DAG.md
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## 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)
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+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:
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## 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.
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``` python
import paddle_serving_server as serving
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