In this document, we mainly focus on how to develop a new server side operator for PaddleServing. Before we start to write a new operator, let's look at some sample code to get the basic idea of writing a new operator for server. We assume you have known the basic computation logic on server side of PaddleServing, please reference to []() if you do not know much about it. The following code can be visited at `core/general-server/op` of Serving repo.
In this document, we mainly focus on how to develop a new server side operator for PaddleServing. Before we start to write a new operator, let's look at some sample code to get the basic idea of writing a new operator for server. We assume you have known the basic computation logic on server side of PaddleServing, please reference to []() if you do not know much about it. The following code can be visited at `core/general-server/op` of Serving repo.
Paddle Serving is PaddlePaddle's online estimation service framework, which can help developers easily implement remote prediction services that call deep learning models from mobile and server ends. At present, Paddle Serving is mainly based on models that support PaddlePaddle training. It can be used in conjunction with the Paddle training framework to quickly deploy inference services. Paddle Serving is designed around common industrial-level deep learning model deployment scenarios. Some common functions include multi-model management, model hot loading, [Baidu-rpc](https://github.com/apache/incubator-brpc)-based high-concurrency low-latency response capabilities, and online model A/B tests. The API that cooperates with the Paddle training framework can enable users to seamlessly transition between training and remote deployment, improving the landing efficiency of deep learning models.
Paddle Serving is PaddlePaddle's online estimation service framework, which can help developers easily implement remote prediction services that call deep learning models from mobile and server ends. At present, Paddle Serving is mainly based on models that support PaddlePaddle training. It can be used in conjunction with the Paddle training framework to quickly deploy inference services. Paddle Serving is designed around common industrial-level deep learning model deployment scenarios. Some common functions include multi-model management, model hot loading, [Baidu-rpc](https://github.com/apache/incubator-brpc)-based high-concurrency low-latency response capabilities, and online model A/B tests. The API that cooperates with the Paddle training framework can enable users to seamlessly transition between training and remote deployment, improving the landing efficiency of deep learning models.
This document shows the concept of computation graph on server. How to define computation graph with PaddleServing built-in operators. Examples for some sequential execution logics are shown as well.
This document shows the concept of computation graph on server. How to define computation graph with PaddleServing built-in operators. Examples for some sequential execution logics are shown as well.
