143 lines
5.3 KiB
Markdown
143 lines
5.3 KiB
Markdown
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# PPOCR 服务化部署
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([English](./README.md)|简体中文)
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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PPOCR
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动态图模型的pipeline在线服务。
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相比较于hubserving部署，PaddleServing支持客户端和服务端之间 高并发和高效通信，更多有关Paddle Serving服务化部署框架介绍和使用教程参考[文档](https://aistudio.baidu.com/aistudio/projectdetail/1550674)。
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## 目录
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- 环境准备
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- 模型转换
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- Paddle Serving pipeline部署
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- FAQ
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## 环境准备
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需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
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1. 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
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2. 准备PaddleServing的运行环境，步骤如下
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安装serving，用于启动服务
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```
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pip3 install paddle-serving-server==0.5.0 # for CPU
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pip3 install paddle-serving-server-gpu==0.5.0 # 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==0.5.0.post9 # GPU with CUDA9.0
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pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
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pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
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pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + 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==0.5.0 # for CPU
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pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
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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==0.3.0
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```
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**note:** 安装0.3.0版本的serving-app后，为了能加载动态图模型，需要修改serving_app的源码，具体为：
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```
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# 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件
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vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
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# 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为：
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if os.path.exists(os.path.join(model_path, "__params__")):
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config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
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else:
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config = AnalysisConfig(model_path)
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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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## 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PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
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```
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# 下载并解压 OCR 文本检测模型
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
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# 下载并解压 OCR 文本识别模型
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
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# 转换检测模型
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python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ppocr_det_server_2.0_serving/ \
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--serving_client ./ppocr_det_server_2.0_client/
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# 转换识别模型
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python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ppocr_rec_server_2.0_serving/ \
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--serving_client ./ppocr_rec_server_2.0_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`ppocr_det_server_2.0_serving` 和`ppocr_det_server_2.0_client`的文件夹，具备如下格式：
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```
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|- ppocr_det_server_2.0_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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|- ppocr_det_server_2.0_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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识别模型同理。
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## Paddle Serving pipeline部署
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```
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# 下载PaddleOCR代码，若已下载可跳过此步骤
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git clone https://github.com/PaddlePaddle/PaddleOCR
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# 进入到工作目录
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cd PaddleOCR/deploy/pdserver/
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```
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pdserver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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ocr_reader.py # OCR模型预处理和后处理的代码实现
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pipeline_http_client.py # 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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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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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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## FAQ
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** Q1**： 发送请求后得不到结果
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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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