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Recognition model service deployment

Table of contents

1 Introduction

Paddle Serving aims to help deep learning developers easily deploy online prediction services, support one-click deployment of industrial-grade service capabilities, high concurrency between client and server Efficient communication and support for developing clients in multiple programming languages.

This section takes the HTTP prediction service deployment as an example to introduce how to use PaddleServing to deploy the model service in PaddleClas. Currently, only Linux platform deployment is supported, and Windows platform is not currently supported.

2. Serving installation

The Serving official website recommends using docker to install and deploy the Serving environment. First, you need to pull the docker environment and create a Serving-based docker.

# start GPU docker
docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel bash
nvidia-docker exec -it test bash

# start CPU docker
docker pull paddlepaddle/serving:0.7.0-devel
docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-devel bash
docker exec -it test bash

After entering docker, you need to install Serving-related python packages.

python3.7 -m pip install paddle-serving-client==0.7.0
python3.7 -m pip install paddle-serving-app==0.7.0
python3.7 -m pip install faiss-cpu==1.7.1post2

#If it is a CPU deployment environment:
python3.7 -m pip install paddle-serving-server==0.7.0 #CPU
python3.7 -m pip install paddlepaddle==2.2.0 # CPU

#If it is a GPU deployment environment
python3.7 -m pip install paddle-serving-server-gpu==0.7.0.post102 # GPU with CUDA10.2 + TensorRT6
python3.7 -m pip install paddlepaddle-gpu==2.2.0 # GPU with CUDA10.2

#Other GPU environments need to confirm the environment and then choose which one to execute
python3.7 -m pip install paddle-serving-server-gpu==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
python3.7 -m pip install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
  • If the installation speed is too slow, you can change the source through -i https://pypi.tuna.tsinghua.edu.cn/simple to speed up the installation process.
  • For other environment configuration installation, please refer to: Install Paddle Serving with Docker

3. Image recognition service deployment

When using PaddleServing for image recognition service deployment, need to convert multiple saved inference models to Serving models. The following takes the ultra-lightweight image recognition model in PP-ShiTu as an example to introduce the deployment of image recognition services.

3.1 Model conversion

  • Go to the working directory:

    cd deploy/

  • Download generic detection inference model and generic recognition inference model

    # Create and enter the models folder
mkdir models
cd models
# Download and unzip the generic recognition model
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
# Download and unzip the generic detection model
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar

  • Convert the generic recognition inference model to the Serving model:

    # Convert the generic recognition model
python3.7 -m paddle_serving_client.convert \
--dirname ./general_PPLCNet_x2_5_lite_v1.0_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ \
--serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/

    The meaning of the parameters of the above command is the same as #3.1 Model conversion

    After the recognition inference model is converted, there will be additional folders general_PPLCNet_x2_5_lite_v1.0_serving/ and general_PPLCNet_x2_5_lite_v1.0_client/ in the current folder. Modify the name of alias in serving_server_conf.prototxt in general_PPLCNet_x2_5_lite_v1.0_serving/ and general_PPLCNet_x2_5_lite_v1.0_client/ directories respectively: Change alias_name in fetch_var to features. The content of the modified serving_server_conf.prototxt is as follows

    feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 3
shape: 224
shape: 224
}
fetch_var {
  name: "save_infer_model/scale_0.tmp_1"
  alias_name: "features"
  is_lod_tensor: false
  fetch_type: 1
  shape: 512
}

    After the conversion of the general recognition inference model is completed, there will be additional general_PPLCNet_x2_5_lite_v1.0_serving/ and general_PPLCNet_x2_5_lite_v1.0_client/ folders in the current folder, with the following structure:

    ├── general_PPLCNet_x2_5_lite_v1.0_serving/
│   ├── inference.pdiparams
│   ├── inference.pdmodel
│   ├── serving_server_conf.prototxt
│   └── serving_server_conf.stream.prototxt
│
└── general_PPLCNet_x2_5_lite_v1.0_client/
    ├── serving_client_conf.prototxt
    └── serving_client_conf.stream.prototxt

  • Convert general detection inference model to Serving model:

    # Convert generic detection model
python3.7 -m paddle_serving_client.convert --dirname ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/ \
--serving_client ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/

    The meaning of the parameters of the above command is the same as #3.1 Model conversion

    After the conversion of the general detection inference model is completed, there will be additional folders picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/ and picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/ in the current folder, with the following structure:

    ├── picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/
│   ├── inference.pdiparams
│   ├── inference.pdmodel
│   ├── serving_server_conf.prototxt
│   └── serving_server_conf.stream.prototxt
│
└── picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
    ├── serving_client_conf.prototxt
    └── serving_client_conf.stream.prototxt

    The specific meaning of the parameters in the above command is shown in the following table

    parameter type default value description
    dirname str - The storage path of the model file to be converted. The program structure file and parameter file are saved in this directory.
    model_filename str None The name of the file storing the model Inference Program structure that needs to be converted. If set to None, use __model__ as the default filename
    params_filename str None The name of the file that stores all parameters of the model that need to be transformed. It needs to be specified if and only if all model parameters are stored in a single binary file. If the model parameters are stored in separate files, set it to None
    serving_server str "serving_server" The storage path of the converted model files and configuration files. Default is serving_server
    serving_client str "serving_client" The converted client configuration file storage path. Default is

  • Download and unzip the index of the retrieval library that has been built

    # Go back to the deploy directory
cd ../
# Download the built retrieval library index
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
# Decompress the built retrieval library index
tar -xf drink_dataset_v1.0.tar

3.2 Service deployment and request

Note: The identification service involves multiple models, and the PipeLine deployment method is used for performance reasons. The Pipeline deployment method currently does not support the windows platform.

  • go to the working directory 
    cd ./deploy/paddleserving/recognition
    The paddleserving directory contains code to start the Python Pipeline service, the C++ Serving service, and send prediction requests, including: 
    __init__.py
config.yml # The configuration file to start the python pipeline service
pipeline_http_client.py # Script for sending pipeline prediction requests in http mode
pipeline_rpc_client.py # Script for sending pipeline prediction requests in rpc mode
recognition_web_service.py # Script to start the pipeline server
readme.md # Recognition model service deployment documents
run_cpp_serving.sh # Script to start C++ Pipeline Serving deployment
test_cpp_serving_client.py # Script for sending C++ Pipeline serving prediction requests by rpc

3.2.1 Python Serving

  • Start the service:

    # Start the service and save the running log in log.txt
python3.7 recognition_web_service.py &>log.txt &

  • send request:

    python3.7 pipeline_http_client.py

    After a successful run, the results of the model prediction will be printed in the cmd window, and the results are as follows:

    {'err_no': 0, 'err_msg': '', 'key': ['result'], 'value': ["[{'bbox': [345, 95, 524, 576], 'rec_docs': 'Red Bull-Enhanced', 'rec_scores': 0.79903316}]"], 'tensors': []}

3.2.2 C++ Serving

Different from Python Serving, the C++ Serving client calls C++ OP to predict, so before starting the service, you need to compile and install the serving server package, and set SERVING_BIN.

  • Compile and install the Serving server package

    # Enter the working directory
cd PaddleClas/deploy/paddleserving
# One-click compile and install Serving server, set SERVING_BIN
source ./build_server.sh python3.7

    Note: The path set by build_server.sh may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled; If you encounter a non-network error during the execution of build_server.sh, you can manually copy the commands in the script to the terminal for execution.

  • The input and output format used by C++ Serving is different from that of Python, so you need to execute the following command to overwrite the files below [3.1] (#31-model conversion) by copying the 4 files to get the corresponding 4 prototxt files in the folder.

    # Enter PaddleClas/deploy directory
cd PaddleClas/deploy/

# Overwrite prototxt file
\cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ./models/general_PPLCNet_x2_5_lite_v1.0_serving/
\cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ./models/general_PPLCNet_x2_5_lite_v1.0_client/
\cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
\cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/

  • Start the service:

    # Enter the working directory
cd PaddleClas/deploy/paddleserving/recognition

# The default port number is 9400; the running log is saved in log_PPShiTu.txt by default
# CPU deployment
sh run_cpp_serving.sh
# GPU deployment, and specify card 0
sh run_cpp_serving.sh 0

  • send request:

    # send service request
python3.7 test_cpp_serving_client.py

    After a successful run, the results of the model predictions are printed in the client's terminal window as follows:

    WARNING: Logging before InitGoogleLogging() is written to STDERR
I0614 03:01:36.273097 6084 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9400"): added 1
I0614 03:01:37.393564 6084 general_model.cpp:490] [client]logid=0,client_cost=1107.82ms,server_cost=1101.75ms.
[{'bbox': [345, 95, 524, 585], 'rec_docs': 'Red Bull-Enhanced', 'rec_scores': 0.8073724}]

  • close the service: If the service program is running in the foreground, you can press Ctrl+C to terminate the server program; if it is running in the background, you can use the kill command to close related processes, or you can execute the following command in the path where the service program is started to terminate the server program:

    python3.7 -m paddle_serving_server.serve stop

    After the execution is completed, the Process stopped message appears, indicating that the service was successfully shut down.

4. FAQ

Q1: No result is returned after the request is sent or an output decoding error is prompted

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

unset https_proxy
unset http_proxy

Q2: nothing happens after starting the service

A2: You can check whether the path corresponding to model_config in config.yml exists, and whether the folder name is correct

For more service deployment types, such as RPC prediction service, you can refer to Serving's github official website