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173 lines
6.2 KiB
Markdown
173 lines
6.2 KiB
Markdown
# 基于PaddleServing的服务部署
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(简体中文|[English](./README.md))
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本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署 Arcface 动态图模型的pipeline在线服务。
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PaddleServing具备以下优点：
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- 支持客户端和服务端之间高并发和高效通信
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- 支持 工业级的服务能力 例如模型管理，在线加载，在线A/B测试等
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- 支持 多种编程语言 开发客户端，例如C++, Python和Java
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更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)。
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## 目录
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- [环境准备](#环境准备)
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- [模型转换](#模型转换)
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- [Paddle Serving pipeline部署](#部署)
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- [FAQ](#FAQ)
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<a name="环境准备"></a>
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## 环境准备
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需要准备 Arcface 的运行环境和Paddle Serving的运行环境。
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- 准备 Arcface 的运行环境[链接](../../README_cn.md)
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根据环境下载对应的paddle whl包，推荐安装2.2+版本
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- 准备PaddleServing的运行环境，步骤如下
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1. 安装serving，用于启动服务
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```
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pip3 install paddle-serving-server==0.6.3 # for CPU
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pip3 install paddle-serving-server-gpu==0.6.3 # for GPU
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# 其他GPU环境需要确认环境再选择执行如下命令
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pip3 install paddle-serving-server-gpu==0.6.3.post101 # GPU with CUDA10.1 + TensorRT6
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pip3 install paddle-serving-server-gpu==0.6.3.post11 # GPU with CUDA11 + TensorRT7
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```
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2. 安装client，用于向服务发送请求
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```
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pip3 install paddle_serving_client==0.6.3
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```
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3. 安装serving-app
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```
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pip3 install paddle-serving-app==0.6.3
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```
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**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
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<a name="模型转换"></a>
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## 模型转换
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使用PaddleServing做服务化部署时，需要将保存的inference模型转换为serving易于部署的模型。
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首先，下载Arcface的inference模型
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```
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# 下载并解压 Arcface 模型
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wget -nc -P ./inference https://paddle-model-ecology.bj.bcebos.com/model/insight-face/mobileface_v1.0_infer.tar
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tar xf inference/mobileface_v1.0_infer.tar --strip-components 1 -C inference
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```
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接下来，用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
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```
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python3 -m paddle_serving_client.convert --dirname ./inference/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./MobileFaceNet_128_serving/ \
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--serving_client ./MobileFaceNet_128_client/
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```
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检测模型转换完成后，会在当前文件夹多出`MobileFaceNet_128_serving/` 和`MobileFaceNet_128_client`的文件夹，具备如下格式：
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```
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MobileFaceNet_128_serving
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├── __model__
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├── __params__
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├── serving_server_conf.prototxt
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└── serving_server_conf.stream.prototxt
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MobileFaceNet_128_client/
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├── serving_client_conf.prototxt
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└── serving_client_conf.stream.prototxt
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```
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<a name="部署"></a>
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## Paddle Serving pipeline部署
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1. 下载insightface代码，若已下载可跳过此步骤
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```
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git clone https://github.com/deepinsight/insightface
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# 进入到工作目录
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cd recognition/arcface_paddle/deploy/pdserving
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```
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pdserving目录包含启动pipeline服务和发送预测请求的代码，包括：
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```
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__init__.py
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config.yml # 启动服务的配置文件
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pipeline_http_client.py # web方式发送pipeline预测请求的脚本
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pipeline_rpc_client.py # rpc方式发送pipeline预测请求的脚本
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web_service.py # 启动pipeline服务端的脚本
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```
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2. 启动服务可运行如下命令：
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```
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# 启动服务，运行日志保存在log.txt
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python3 web_service.py &>log.txt &
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```
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成功启动服务后，log.txt中会打印类似如下日志
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3. 发送服务请求：
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```
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python3 pipeline_http_client.py
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```
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成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
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调整 config.yml 中的并发个数获得最大的QPS, 一般检测和识别的并发数为2：1
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```
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ArcFace:
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#并发数，is_thread_op=True时，为线程并发；否则为进程并发
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concurrency: 8
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...
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```
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有需要的话可以同时发送多个服务请求
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预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
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在700张真实图片上测试，V100 GPU 上 QPS 均值可达到57左右：
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```
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2021-11-04 13:38:52,507 Op(ArcFace):
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2021-11-04 13:38:52,507 in[135.4579597902098 ms]
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2021-11-04 13:38:52,507 prep[0.9921311188811189 ms]
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2021-11-04 13:38:52,507 midp[3.9232132867132865 ms]
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2021-11-04 13:38:52,507 postp[0.12166258741258741 ms]
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2021-11-04 13:38:52,507 out[0.9898286713286714 ms]
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2021-11-04 13:38:52,508 idle[0.9643989520087675]
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2021-11-04 13:38:52,508 DAGExecutor:
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2021-11-04 13:38:52,508 Query count[573]
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2021-11-04 13:38:52,508 QPS[57.3 q/s]
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2021-11-04 13:38:52,509 Succ[0.9982547993019197]
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2021-11-04 13:38:52,509 Error req[394]
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2021-11-04 13:38:52,509 Latency:
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2021-11-04 13:38:52,509 ave[11.52941186736475 ms]
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2021-11-04 13:38:52,509 .50[11.492 ms]
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2021-11-04 13:38:52,509 .60[11.658 ms]
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2021-11-04 13:38:52,509 .70[11.95 ms]
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2021-11-04 13:38:52,509 .80[12.251 ms]
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2021-11-04 13:38:52,509 .90[12.736 ms]
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2021-11-04 13:38:52,509 .95[13.21 ms]
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2021-11-04 13:38:52,509 .99[13.987 ms]
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2021-11-04 13:38:52,510 Channel (server worker num[10]):
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2021-11-04 13:38:52,510 chl0(In: ['@DAGExecutor'], Out: ['ArcFace']) size[0/0]
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2021-11-04 13:38:52,510 chl1(In: ['ArcFace'], Out: ['@DAGExecutor']) size[0/0]
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```
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<a name="FAQ"></a>
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## FAQ
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**Q1**： 发送请求后没有结果返回或者提示输出解码报错
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**A1**： 启动服务和发送请求时不要设置代理，可以在启动服务前和发送请求前关闭代理，关闭代理的命令是：
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```
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unset https_proxy
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unset http_proxy
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```
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