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(简体中文|English)
The goal of Paddle Serving is to provide high-performance, flexible and easy-to-use industrial-grade online inference services for machine learning developers and enterprises.Paddle Serving supports multiple protocols such as RESTful, gRPC, bRPC, and provides inference solutions under a variety of hardware and multiple operating system environments, and many famous pre-trained model examples. The core features are as follows:
AIStudio tutorial(Chinese) : Paddle Serving服务化部署框架
Video tutorial(Chinese) : 深度学习服务化部署-以互联网应用为例
Set up
This chapter guides you through the installation and deployment steps. It is strongly recommended to use Docker to deploy Paddle Serving. If you do not use docker, ignore the docker-related steps. Paddle Serving can be deployed on cloud servers using Kubernetes, running on many commonly hardwares such as ARM CPU, Intel CPU, Nvidia GPU, Kunlun XPU. The latest development kit of the develop branch is compiled and generated every day for developers to use.
Use
The first step is to call the model save interface to generate a model parameter configuration file (.prototxt), which will be used on the client and server. The second step, read the configuration and startup parameters and start the service. According to API documents and your case, the third step is to write client requests based on the SDK, and test the inference service.
Developers
For Paddle Serving developers, we provide extended documents such as custom OP, level of detail(LOD) processing.
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 42 models.
PaddleOCR | PaddleDetection | PaddleClas | PaddleSeg | PaddleRec | Paddle NLP |
---|---|---|---|---|---|
8 | 12 | 13 | 2 | 3 | 4 |
For more model examples, read Model zoo
If you want to communicate with developers and other users? Welcome to join us, join the community through the following methods below.
Contribution
If you want to contribute code to Paddle Serving, please reference Contribution Guidelines
Feedback
For any feedback or to report a bug, please propose a GitHub Issue.
License
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