百度智能云智能边缘 - 制作GPU版本Paddle Serving推理镜像
文档简介:
前言:
前文重点介绍了通过BIE将Paddle Serving部署至边缘,为了通用起见,使用了Paddle Serving的CPU镜像。本文重点介绍如何制作Paddle Serving的GPU镜像,并同样以yolov3模型进行验证。
前言
前文重点介绍了通过BIE将Paddle Serving部署至边缘,为了通用起见,使用了Paddle Serving的CPU镜像。本文重点介绍如何制作Paddle Serving的GPU镜像,并同样以yolov3模型进行验证。
实验设备
本文所使用的实验设备是一台GPU服务器,参数如下:
- 操作系统:ubuntu 18.04 desktop
- CPU:intel i9
- GPU:NVIDIA 3090
- cuda 11.4
镜像准备
# 下载镜像 docker pull registry.baidubce.com/paddlepaddle/serving:0.7.0
-cuda11.2-cudnn8-devel # 运行镜像 docker run --rm -dit --name pipeline_
serving_demo registry.baidubce.com/paddlepaddle/serving:0.7.0-cuda11.2-cu
dnn8-devel bash # copy Paddle Serving代码包至容器镜像 docker cp Serving-
0.7.0.zip pipeline_serving_demo:/home/ # 进入容器镜像 docker exec -it pip
eline_serving_demo bash # 解压代码包 unzip Serving-0.7.0.zip # 进入代码目
录 cd Serving-0.7.0 # 安装paddle-serving-gpu pip3 install paddle-serving-client==0.7.0 pip3 install paddle-serving-server-gpu==0.7.0.post112 pip3 install paddle-serving-app==0.7.0 # 因为本地cuda版本并非10.2,
需要现在指定whl包来安装paddlepaddle-gpu docker cp paddlepaddle_gpu-2.2.1
.post112-cp36-cp36m-linux_x86_64.whl pipeline_serving_demo:/home/
pip3 install paddlepaddle_gpu-2.2.1.post112-cp36-cp36m-linux_x86_64.whl
# 保存镜像 docker commit pipeline_serving_demo paddle_serving:0.7.0-cuda11.2-cudnn8-py36
该镜像也同步推送至百度CCR,镜像地址为:registry.baidubce.com/pp/paddle-serving:0.7.0-gpu-py36,可直接使用。
模型准备
模型依然使用上文制作的模型paddle_serving_yolov3_darknet53_270e_coco_gpu.zip,可直接下载。
针对GPU设备,需要单独修改config.yml文件,主要修改如下:
- device_type: 1
- devices: "0"
dag: #op资源类型, True, 为线程模型;False,为进程模型 is_thread_op: false #使用性能分析,
True,生成Timeline性能数据,对性能有一定影响;False为不使用 tracer: interval_s: 30
#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时,
不自动生成http_port http_port: 18082 op: yolov3: #并发数,is_thread_op=True时,
为线程并发;否则为进程并发 concurrency: 10 local_service_conf: #client类型,
包括brpc, grpc和local_predictor.local_predictor不启动Serving服务,进程内预测
client_type: local_predictor # device_type, 0=cpu, 1=gpu, 2=tensorRT, 3=arm
cpu, 4=kunlun xpu device_type: 1 #计算硬件ID,当devices为""或不写时为CPU预测;
当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡 devices: "0" #Fetch结果列表,
以bert_seq128_model中fetch_var的alias_name为准, 如果没有设置则全部返回 fetch_list:
- save_infer_model/scale_0.tmp_1 #模型路径 model_config: serving_server/ #rpc端口
, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时,会自动将rp
c_port设置为http_port+1 rpc_port: 9998 #worker_num, 最大并发数。当build_dag_each
_worker=True时, 框架会创建worker_num个进程,每个进程内构建grpcSever和DAG #当buil
d_dag_each_worker=False时,框架会设置主线程grpc线程池的max_workers=worker_num worker_num: 20
测试验证
导入测试应用
打开BIE云端控制台,按顺序导入如下配置与应用:
- 配置项-paddle-yolov3-model-gpu.json
- 配置项-paddle-yolov3-run-script.json
- 应用-paddle-serving-gpu-070.json
远程调用
打开远程调用,使用通用模式调用部署在边缘的yolov3模型,填写响应参数,最后点击测试,得到返回结果。
- 方法:POST
- 端口:18082
- 接口路径:/yolov3/prediction
- params:空
- header:空
- body:拷贝如下内容粘贴只前端页面
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查看gpu使用
在GPU服务器上执行nvidia-smi,查看GPU资源使用
nvidia-smi Fri Nov 18 13:16:35 2022 +------------------------------------------------------------
-----------------+ | NVIDIA-SMI 470.42.01 Driver Version: 470.42.01 CUDA Version
: 11.4 | |-------------------------------+----------------------+---------------------
-+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | F
an Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=
==============================+======================+======================| | 0 NVID
IA GeForce ... Off | 00000000:17:00.0 On | N/A | | 30% 39C P8 20W / 350W | 92
94MiB / 24259MiB | 0% Default | | | | N/A | +-------------------------------+----
------------------+----------------------+
+-----------------------------------------------------------------------------+ | P
rocesses: | | GPU GI CI PID Type Process name
GPU Memory | | ID ID
Usage | |==================================================
===========================| | 0 N/A N/A 1328 G /usr/lib/xorg/Xorg
35MiB | | 0 N/A N/A 1881 G /usr/lib/xorg/Xorg
80MiB | | 0 N/A N/A 2081 G /usr/bin/gnome-shell
32MiB | | 0 N/A N/A 219909 C python3
791MiB | | 0 N/A N/A 219917 C python3
791MiB | | 0 N/A N/A 219928 C python3
791MiB | | 0 N/A N/A 219941 C python3
1987MiB | | 0 N/A N/A 219955 C python3
791MiB | | 0 N/A N/A 219967 C python3
791MiB | | 0 N/A N/A 219979 C python3
791MiB | | 0 N/A N/A 219993 C python3
791MiB | | 0 N/A N/A 220006 C python3
791MiB | | 0 N/A N/A 220016 C python3
791MiB | | 0 N/A N/A 2142849 G gnome-control-center
4MiB | +-----------------------------------------------------------------------------+