基于Paddle Serving&百度智能边缘BIE的边缘AI解决方案

2022-01-17 16:50:22 浏览数 (1)

Paddle Serving作为飞桨(PaddlePaddle)开源的服务化部署服务化方案,提供了C Serving和Python Pipeline两套框架,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,助力人工智能落地应用。在最新的Paddle Serving v0.7.0中,提供了丰富的模型示例,总计有42个,具体模型信息可查看Model_Zoo:

https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Model_Zoo_CN.md。

百度智能边缘(Baidu Intelligent Edge,BIE)由 云端管理平台和BAETYL 开源边缘计算框架两部分组成,实现将云计算能力拓展至用户现场,可以提供临时离线、低延时的计算服务,包括消息规则、函数计算、AI 推断。智能边缘配合百度智能云,形成“云管理,端计算”的端云一体解决方案。

通过Paddle Serving赋能BIE,可以实现产业级的边缘AI服务发布解决方案,达到如下的云边端能力:

  • 管理边缘节点:纳管多种类型的边缘节点,包括服务器、边缘计算盒子。如果边缘侧是一个多机集群,也支持通过BIE统一管理。
  • 状态检查:支持监控边缘节点运行状态、资源使用(CPU、内存、GPU、磁盘、网络流量等)。
  • 下发Serving:支持云端将Paddle Serving下发至边缘侧,作为边缘侧服务化推理 Serving版本升级。
  • 下发模型:支持云端动态下发PaddlePaddle模型至边缘侧,模型版本升级。

以下教程详细描述使用Paddle Serving和BIE实现云边端服务发布的能力。主要包括实验准备、模型准备、Paddle Serving镜像准备、模型应用创建、模型应用部署、测试验证、测试效果展示。

1 试验设备

一台x86架构的ubuntu 18.04虚拟机,不依赖GPU

2 模型文件准备

1.在宿主机上下载Paddle Serving代码

代码语言:javascript复制
git clone https://github.com/PaddlePaddle/Serving.git

2.下载模型,参考文档:

https://github.com/PaddlePaddle/Serving/tree/v0.7.0/examples/Pipeline/PaddleDetection/yolov3

代码语言:javascript复制
# 进入到yolov3实例模型目录
cd Serving/examples/Pipeline/PaddleDetection/yolov3/
# 下载模型
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/yolov3_darknet53_270e_coco.tar
# 解压模型
tar xf yolov3_darknet53_270e_coco.tar
# 解压以后删除模型压缩包
rm -r yolov3_darknet53_270e_coco.tar

3.制作模型压缩包

代码语言:javascript复制
cd Serving/examples/Pipeline/PaddleDetection/yolov3/
压缩当前目录下的文件
zip -r paddle_serving_yolov3_darknet53_270e_coco.zip ./*
# 查看md5
md5sum paddle_serving_yolov3_darknet53_270e_coco.zip 
7a2ca27f2f444c6ac169d19922ff89ab  paddle_serving_yolov3_darknet53_270e_coco.zip

4.将模型上传到bos

3 Paddle Serving镜像准备

1.下载Paddle Serving开发镜像

代码语言:javascript复制
docker pull registry.baidubce.com/paddlepaddle/serving:0.7.0-devel

2.运行Paddle Serving开发镜像

代码语言:javascript复制
docker run --rm -dit --name pipeline_serving_demo registry.baidubce.com/paddlepaddle/serving:0.7.0-devel bash

3.Paddle Serving开发镜像当中安装依赖程序

代码语言:javascript复制
# 进入容器
docker exec -it pipeline_serving_demo bash
# 下载代码
git clone https://github.com/PaddlePaddle/Serving.git
# 进入Paddle Serving代码目录
cd Serving
# 安装依赖
pip3 install -r python/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

# CPU环境安装内容如下

# 安装Paddle Serving
pip3 install paddle-serving-client==0.7.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install paddle-serving-server==0.7.0 -i https://pypi.tuna.tsinghua.edu.cn/simple 
pip3 install paddle-serving-app==0.7.0 -i https://pypi.tuna.tsinghua.edu.cn/simple 

# 安装Paddle相关Python库
pip3 install paddlepaddle==2.2.0

加上 -i https://pypi.tuna.tsinghua.edu.cn/simple 表示使用国内源,提升下载速度,非必须,可以不加。

4.提交镜像,固化上面的安装内容

代码语言:javascript复制
docker commit pipeline_serving_demo paddle_serving:0.7.0-cpu-py36

这里将我制作的镜像推送到了百度公有云CCR,可以直接下载使用

代码语言:javascript复制
docker pull registry.baidubce.com/pp/paddle-serving:0.7.0-cpu-py36

4 模型应用创建

4.1创建模型文件配置项

①创建配置项paddle-yolov3-model

②点击引入文件

  • 类型:HTTP
  • URL:https://bie-document.gz.bcebos.com/paddlepaddle/paddle_serving_yolov3_darknet53_270e_coco.zip
  • 文件名称:paddle_serving_yolov3_darknet53_270e_coco.zip
  • 是否解压:是

4.2创建启动脚本配置项

①创建配置项paddle-yolov3-run-script

②添加配置数据如下

  • 变量名:run.sh
  • 变量值:如下述代码
代码语言:javascript复制
#! /usr/bin/env bash
cd /home/work/yolov3
python3 web_service.py

4.3创建paddle-serving应用并挂载

①创建应用paddle-serving

②配置服务

  • 基础信息:

名称:paddle-serving

镜像:paddle-serving:0.7.0-cpu-py36

  • 卷配置:

/home/work/script:运行脚本位置,与启动参数一致

/home/work/yolov3:模型位置,与运行脚本一致

  • 启动参数

/bin/bash

/home/work/script/run.sh,与前面的卷配置一致

5 模型应用部署

1.进入到paddle-serving

2.定位到目标节点,点击单节点匹配,选择目标节点paddle-serving-test。等待几分钟,部署状态将变为已部署。

3.进入边缘节点,可以查看服务在边缘测的运行状态,如下图所示:

6 测试验证

6.1使用paddle-serving-client验证

①ssh登录边缘节点

②查看边缘节点BIE应用状态

代码语言:javascript复制
kubectl get pod -n baetyl-edge
NAME                             READY   STATUS    RESTARTS   AGE
paddle-serving-dd6d8986c-d89k7   1/1     Running   0          3m7s

③进入边缘容器

代码语言:javascript复制
kubectl exec -it paddle-serving-dd6d8986c-d89k7 -n baetyl-edge /bin/bash
# 进去以后,工作目录为/home
λ paddle-serving-dd6d8986c-d89k7 /home 
# 查看/home/work目录,检查云端模型是否下发成功
λ paddle-serving-dd6d8986c-d89k7 /home/work cd /home/work/
λ paddle-serving-dd6d8986c-d89k7 /home/work ls
script/  yolov3/

④执行测试命令

代码语言:javascript复制
# 进入yolov3目录
λ paddle-serving-dd6d8986c-d89k7 /home/work/yolov3 cd /home/work/yolov3/
# 查看内容
λ paddle-serving-dd6d8986c-d89k7 /home/work/yolov3 ls -l
total 221M
-rw-rw-r-- 1 root root 136K Dec 17 09:21 000000570688.jpg
-rw-rw-r-- 1 root root  509 Dec 17 09:21 benchmark_config.yaml
-rw-rw-r-- 1 root root 4.2K Dec 17 09:21 benchmark.py
-rw-rw-r-- 1 root root 2.1K Dec 17 09:21 benchmark.sh
-rw-rw-r-- 1 root root 1.5K Dec 17 09:21 config.yml
-rw-rw-r-- 1 root root  621 Dec 17 09:21 label_list.txt
-rwxr-xr-x 1 root root 220M Dec 17 09:21 paddle_serving_yolov3_darknet53_270e_coco.zip
-rw-rw-r-- 1 root root 1.2K Dec 17 09:21 pipeline_http_client.py
drwxr-xr-x 2 root root 4.0K Dec 17 09:26 PipelineServingLogs/
-rw-r--r-- 1 root root   89 Dec 17 09:26 ProcessInfo.json
-rw-rw-r-- 1 root root  368 Dec 17 09:21 README_CN.md
-rw-rw-r-- 1 root root  374 Dec 17 09:21 README.md
drwxr-xr-x 2 root root 4.0K Dec 17 09:21 serving_client/
drwxr-xr-x 2 root root 4.0K Dec 17 09:21 serving_server/
-rw-rw-r-- 1 root root 2.8K Dec 17 09:21 web_service.py
λ paddle-serving-dd6d8986c-d89k7 /home/work/yolov3 python3 
# 执行客户端测试脚本
pipeline_http_client.py

返回结果如下:

代码语言:javascript复制
{
    'err_no': 0,
    'err_msg': '',
    'key': ['bbox_result'],
/*
* 提示:该行代码过长,系统自动注释不进行高亮。一键复制会移除系统注释 
* 'value': ["[{'category_id': 0, 'bbox': [215.16099548339844, 438.1199951171875, 43.29920959472656, 186.94189453125], 'score': 0.9860591292381287}, {'category_id': 0, 'bbox': [404.882568359375, 463.1432800292969, 50.00750732421875, 174.96109008789062], 'score': 0.972165584564209}, {'category_id': 0, 'bbox': [259.8436279296875, 458.67169189453125, 47.04876708984375, 154.5758056640625], 'score': 0.9670743346214294}, {'category_id': 0, 'bbox': [438.247314453125, 491.875, 68.9227294921875, 145.5482177734375], 'score': 0.9092186689376831}, {'category_id': 0, 'bbox': [156.2906951904297, 505.449951171875, 57.74359130859375, 55.9661865234375], 'score': 0.7775811553001404}, {'category_id': 0, 'bbox': [28.40601921081543, 451.0614318847656, 27.93486213684082, 113.30917358398438], 'score': 0.768792986869812}, {'category_id': 0, 'bbox': [297.4198303222656, 511.6090087890625, 59.18255615234375, 76.463134765625], 'score': 0.7284609079360962}, {'category_id': 0, 'bbox': [498.0811767578125, 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*/
    'tensors': []
}

6.2使用postman验证

①我们知道上述yolov3模型服务的容器内端口是18082,当前我们需要在单独的一台测试机器上使用postman去调用yolov3服务接口,那么就需要将容器内的18082端口映射到宿主机上,我们在云端BIE控制台配置paddle-serving这个服务,添加端口映射,如下图所示:

②下载测试图片dog.jpeg

图片链接:https://bce.bdstatic.com/doc/bce-doc/BIE/dog_831f56a.jpeg

③执行一下命令,将这张图片的base64编码输出到dog.base64文件当中

代码语言:javascript复制
base64 -i dog.jpeg -o dog.base64

④组装postman输出参数,如下所示:

代码语言:javascript复制
{
    "key":[
        "image"
    ],
    "value":[
/*
* 提示:该行代码过长,系统自动注释不进行高亮。一键复制会移除系统注释 
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*/
    ]
}

⑤postman调用url为http://[ip]:18082/yolov3/prediction,如下图所示:

⑥查看postman返回结果如下,如下图所示:

代码语言:javascript复制
{
    "err_no":0,
    "err_msg":"",
    "key":[
        "bbox_result"
    ],
    "value":[
        "[{'category_id': 16, 'bbox': [138.06399536132812, 54.169952392578125, 464.1486511230469, 552.6064147949219], 'score': 0.9826956987380981}, {'category_id': 57, 'bbox': [142.67298889160156, 29.47320556640625, 401.58738708496094, 564.1134033203125], 'score': 0.02150958590209484}]"
    ],
    "tensors":[

    ]
}

⑦我们看到category_id为16,查看该模型的label_list.txt,我们看到16刚好对应dog。

模型链接地址:

https://github.com/PaddlePaddle/Serving/blob/v0.7.0/examples/Pipeline/PaddleDetection/yolov3/label_list.txt

*说明:label_list当中的id,从0开始。

基于Paddle Serving BIE的

迁移扩展能力

通过BIE部署Paddle Serving的主要思路:

1.文中所用到的paddle-serving应用和运行脚本是通用的,若想运行不同模型,只需替换下发模型文件即可。

2.GPU镜像构建逻辑与CPU镜像一致,可参考官网文档,本文统一使用CPU镜像。

Paddle Serving不断拓展异构硬件和边缘端部署能力,将与百度智能云智能边缘框架BIE深度合作。智能边缘框架BIE凭借其核心技术优势已在各领域落地部署中提供解决方案,未来,BIE将携手更多开发者共创智能边缘发展新机遇,推动边缘计算平台稳步向前,助力众多行业实现智慧化转型。

Paddle Serving即将发布v0.8.0版本将提供更多硬件上AI服务化部署,如华为昇腾310、昇腾910、海光DCU、以及英伟达Jetson。对服务化部署感兴趣的小伙伴欢迎来Paddle Serving的github了解:https://github.com/PaddlePaddle/Serving

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