PaddlePaddle/Serving PaddlePaddle/Serving

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    C++
  • License
    Apache License 2.0
  • Created over 5 years ago
  • Updated 7 months ago
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PaddlePaddle/Serving
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PaddlePaddle

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Repository Details

A flexible, high-performance carrier for machine learning models（『飞桨』服务化部署框架）

(简体中文|English)




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Paddle Serving 依托深度学习框架 PaddlePaddle 旨在帮助深度学习开发者和企业提供高性能、灵活易用的工业级在线推理服务。Paddle Serving 支持 RESTful、gRPC、bRPC 等多种协议，提供多种异构硬件和多种操作系统环境下推理解决方案，和多种经典预训练模型示例。核心特性如下：

  • 集成高性能服务端推理引擎 Paddle Inference 和端侧引擎 Paddle Lite，其他机器学习平台（Caffe/TensorFlow/ONNX/PyTorch）可通过 x2paddle 工具迁移模型
  • 具有高性能 C++ Serving 和高易用 Python Pipeline 2套框架。C++ Serving 基于高性能 bRPC 网络框架打造高吞吐、低延迟的推理服务，性能领先竞品。Python Pipeline 基于 gRPC/gRPC-Gateway 网络框架和 Python 语言构建高易用、高吞吐推理服务框架。技术选型参考技术选型
  • 支持 HTTP、gRPC、bRPC 等多种协议；提供 C++、Python、Java 语言 SDK
  • 设计并实现基于有向无环图(DAG) 的异步流水线高性能推理框架，具有多模型组合、异步调度、并发推理、动态批量、多卡多流推理、请求缓存等特性
  • 适配 x86(Intel) CPU、ARM CPU、Nvidia GPU、昆仑 XPU、华为昇腾310/910、海光 DCU、Nvidia Jetson 等多种硬件
  • 集成 Intel MKLDNN、Nvidia TensorRT 加速库，以及低精度量化推理
  • 提供一套模型安全部署解决方案，包括加密模型部署、鉴权校验、HTTPs 安全网关，并在实际项目中应用
  • 支持云端部署，提供百度云智能云 kubernetes 集群部署 Paddle Serving 案例
  • 提供丰富的经典模型部署示例，如 PaddleOCR、PaddleClas、PaddleDetection、PaddleSeg、PaddleNLP、PaddleRec 等套件，共计40+个预训练精品模型
  • 支持大规模稀疏参数索引模型分布式部署，具有多表、多分片、多副本、本地高频 cache 等特性、可单机或云端部署
  • 支持服务监控，提供基于普罗米修斯的性能数据统计及端口访问

教程与案例

论文

文档

部署

此章节引导您完成安装和部署步骤，强烈推荐使用Docker部署Paddle Serving，如您不使用docker，省略docker相关步骤。在云服务器上可以使用Kubernetes部署Paddle Serving。在异构硬件如ARM CPU、昆仑XPU上编译或使用Paddle Serving可阅读以下文档。每天编译生成develop分支的最新开发包供开发者使用。

使用

安装Paddle Serving后，使用快速开始将引导您运行Serving。具体步骤如下：

第一步，调用模型保存接口，生成模型参数配置文件(.prototxt)用以在客户端和服务端使用；

第二步，阅读配置和启动参数并启动服务；

第三步，根据API和您的使用场景，基于SDK编写客户端请求，并测试推理服务。您想了解跟多特性的使用场景和方法，请详细阅读以下文档。

开发者

为Paddle Serving开发者，提供自定义OP，变长数据处理。

模型库

Paddle Serving与Paddle模型套件紧密配合，实现大量服务化部署，包括图像分类、物体检测、语言文本识别、中文词性、情感分析、内容推荐等多种类型示例，以及Paddle全链条项目，共计46个模型。

图像分类与识别 NLP 推荐系统 人脸识别 目标检测 文字识别 图像分割 关键点检测 视频理解
14 6 3 1 10 8 2 1 1

更多模型示例进入模型库

社区

您想要同开发者和其他用户沟通吗？欢迎加入我们，通过如下方式加入社群

微信

  • 微信用户请扫码

QQ

  • 飞桨推理部署交流群(Group No.：697765514)

贡献代码

如果您想为Paddle Serving贡献代码，请参考 Contribution Guidelines(English)

  • 感谢 @w5688414 提供 NLP Ernie Indexing 案例
  • 感谢 @loveululu 提供 Cube python API
  • 感谢 @EtachGu 更新 docker 使用命令
  • 感谢 @BeyondYourself 提供grpc教程，更新FAQ教程，整理文件目录。
  • 感谢 @mcl-stone 提供faster rcnn benchmark脚本
  • 感谢 @cg82616424 提供unet benchmark脚本和修改部分注释错误
  • 感谢 @cuicheng01 提供PaddleClas的11个模型
  • 感谢 @Jiaqi Liu 新增list[str]类型输入的预测支持
  • 感谢 @Bin Lu 提供PP-Shitu C++模型示例

反馈

如有任何反馈或是bug，请在 GitHub Issue提交

License

Apache 2.0 License

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