insightface/recognition/arcface_paddle/deploy/pdserving

Service deployment based on PaddleServing

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This document will introduce how to use the PaddleServing to deploy the Arcface dynamic graph model as a pipeline online service.

Some Key Features of Paddle Serving:

• Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed with one line command.
• Industrial serving features supported, such as models management, online loading, online A/B testing etc.
• Highly concurrent and efficient communication between clients and servers supported.

The introduction and tutorial of Paddle Serving service deployment framework reference document.

Contents

• Environmental preparation
• Model conversion
• Paddle Serving pipeline deployment
• FAQ

Environmental preparation

Arcface operating environment and Paddle Serving operating environment are needed.

1. Please prepare Arcface operating environment reference link. Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.

2. The steps of PaddleServing operating environment prepare are as follows:

   Install serving which used to start the service

   pip3 install paddle-serving-server==0.6.3 # for CPU
   pip3 install paddle-serving-server-gpu==0.6.3 # for GPU
   # Other GPU environments need to confirm the environment and then choose to execute the following commands
   pip3 install paddle-serving-server-gpu==0.6.3.post101 # GPU with CUDA10.1 + TensorRT6
   pip3 install paddle-serving-server-gpu==0.6.3.post11 # GPU with CUDA11 + TensorRT7
   

3. Install the client to send requests to the service In download link find the client installation package corresponding to the python version. The python3.7 version is recommended here:

   pip3 install paddle-serving-client==0.6.3
   

4. Install serving-app

   pip3 install paddle-serving-app==0.6.3
   

   note: If you want to install the latest version of PaddleServing, refer to link.

Model conversion

When using PaddleServing for service deployment, you need to convert the saved inference model into a serving model that is easy to deploy.

Firstly, download the inference model of Arcface

wget -nc -P ./inference https://paddle-model-ecology.bj.bcebos.com/model/insight-face/mobileface_v1.0_infer.tar
tar xf inference/mobileface_v1.0_infer.tar --strip-components 1 -C inference 

Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.

python3 -m paddle_serving_client.convert --dirname ./inference/ \
                                         --model_filename inference.pdmodel \
                                         --params_filename inference.pdiparams \
                                         --serving_server ./MobileFaceNet_128_serving/ \
                                         --serving_client ./MobileFaceNet_128_client/

After the detection model is converted, there will be additional folders of MobileFaceNet_128_serving and MobileFaceNet_128_client in the current folder, with the following format:

MobileFaceNet_128_serving
├── __model__
├── __params__
├── serving_server_conf.prototxt
└── serving_server_conf.stream.prototxt

MobileFaceNet_128_client/
├── serving_client_conf.prototxt
└── serving_client_conf.stream.prototxt

The recognition model is the same.

Paddle Serving pipeline deployment

1. Download the PaddleOCR code, if you have already downloaded it, you can skip this step.

   git clone https://github.com/deepinsight/insightface
   
   # Enter the working directory  
   cd recognition/arcface_paddle/deploy/pdserving
   

   The pdserver directory contains the code to start the pipeline service and send prediction requests, including:

   __init__.py
   config.yml # Start the service configuration file
   ocr_reader.py # pre-processing and post-processing code implementation
   pipeline_http_client.py # Script to send pipeline prediction request
   web_service.py # Start the script of the pipeline server
   

2. Run the following command to start the service.

   # Start the service and save the running log in log.txt
   python3 web_service.py &>log.txt &
   

   After the service is successfully started, a log similar to the following will be printed in log.txt

3. Send service request

   python3 pipeline_http_client.py
   

   After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:

   Adjust the number of concurrency in config.yml to get the largest QPS. Generally, the number of concurrent detection and recognition is 2:1

   det:
       concurrency: 8
       ...
   rec:
       concurrency: 4
       ...
   

   Multiple service requests can be sent at the same time if necessary.

   The predicted performance data will be automatically written into the PipelineServingLogs/pipeline.tracer file.

   Tested on 700 real picture. The average QPS on V100 GPU can reach around 57:

   
   2021-05-13 03:42:36,895 ==================== TRACER ======================
   2021-05-13 03:42:36,975 Op(rec):
   2021-05-13 03:42:36,976         in[14.472382882882883 ms]
   2021-05-13 03:42:36,976         prep[9.556855855855856 ms]
   2021-05-13 03:42:36,976         midp[59.921905405405404 ms]
   2021-05-13 03:42:36,976         postp[15.345945945945946 ms]
   2021-05-13 03:42:36,976         out[1.9921216216216215 ms]
   2021-05-13 03:42:36,976         idle[0.16254943864471572]
   2021-05-13 03:42:36,976 Op(det):
   2021-05-13 03:42:36,976         in[315.4468035714286 ms]
   2021-05-13 03:42:36,976         prep[69.5980625 ms]
   2021-05-13 03:42:36,976         midp[18.989535714285715 ms]
   2021-05-13 03:42:36,976         postp[18.857803571428573 ms]
   2021-05-13 03:42:36,977         out[3.1337544642857145 ms]
   2021-05-13 03:42:36,977         idle[0.7477961159203756]
   2021-05-13 03:42:36,977 DAGExecutor:
   2021-05-13 03:42:36,977         Query count[224]
   2021-05-13 03:42:36,977         QPS[22.4 q/s]
   2021-05-13 03:42:36,977         Succ[0.9910714285714286]
   2021-05-13 03:42:36,977         Error req[169, 170]
   2021-05-13 03:42:36,977         Latency:
   2021-05-13 03:42:36,977                 ave[535.1678348214285 ms]
   2021-05-13 03:42:36,977                 .50[172.651 ms]
   2021-05-13 03:42:36,977                 .60[187.904 ms]
   2021-05-13 03:42:36,977                 .70[245.675 ms]
   2021-05-13 03:42:36,977                 .80[526.684 ms]
   2021-05-13 03:42:36,977                 .90[854.596 ms]
   2021-05-13 03:42:36,977                 .95[1722.728 ms]
   2021-05-13 03:42:36,977                 .99[3990.292 ms]
   2021-05-13 03:42:36,978 Channel (server worker num[10]):
   2021-05-13 03:42:36,978         chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
   2021-05-13 03:42:36,979         chl1(In: ['det'], Out: ['rec']) size[6/0]
   2021-05-13 03:42:36,979         chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
   

FAQ

Q1: No result return after sending the request.

A1: Do not set the proxy when starting the service and sending the request. You can close the proxy before starting the service and before sending the request. The command to close the proxy is:

unset https_proxy
unset http_proxy