Classification model service deployment¶

Table of contents¶

1 Introduction
2. Serving installation
3. Image Classification Service Deployment
- 3.1 Model conversion
- 3.2 Service deployment and request
  - 3.2.1 Python Serving
  - 3.2.2 C++ Serving

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 Classification Service Deployment¶

The following takes the classic ResNet50_vd model as an example to introduce how to deploy the image classification service.

3.1 Model conversion¶

When using PaddleServing for service deployment, you need to convert the saved inference model into a Serving model.

Go to the working directory:
```
cd deploy/paddleserving
```

Download and unzip the inference model for ResNet50_vd:

# Download ResNet50_vd inference model
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
# Decompress the ResNet50_vd inference model
tar xf ResNet50_vd_infer.tar

Use the paddle_serving_client command to convert the downloaded inference model into a model format for easy server deployment:
```
# Convert ResNet50_vd model
python3.7 -m paddle_serving_client.convert \
--dirname ./ResNet50_vd_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ResNet50_vd_serving/ \
--serving_client ./ResNet50_vd_client/
```
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 | File name where all parameters of the model to be converted are stored. 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 serving_client |

After the ResNet50_vd inference model conversion is completed, there will be additional ResNet50_vd_serving and ResNet50_vd_client folders in the current folder, with the following structure:
```
├── ResNet50_vd_serving/
│   ├── inference.pdiparams
│   ├── inference.pdmodel
│   ├── serving_server_conf.prototxt
│   └── serving_server_conf.stream.prototxt
│
└── ResNet50_vd_client/
    ├── serving_client_conf.prototxt
    └── serving_client_conf.stream.prototxt
```
Serving provides the function of input and output renaming in order to be compatible with the deployment of different models. When different models are deployed in inference, you only need to modify the alias_name of the configuration file, and the inference deployment can be completed without modifying the code. Therefore, after the conversion, you need to modify the alias names in the files serving_server_conf.prototxt under ResNet50_vd_serving and ResNet50_vd_client respectively, and change the alias_name in fetch_var to prediction, the modified serving_server_conf.prototxt is as follows Show:
```
feed_var {
  name: "inputs"
  alias_name: "inputs"
  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: "prediction"
  is_lod_tensor: false
  fetch_type: 1
  shape: 1000
}
```

3.2 Service deployment and request¶

The paddleserving directory contains the code for starting the pipeline service, the C++ serving service and sending the prediction request, mainly including:

__init__.py
classification_web_service.py # Script to start the pipeline server
config.yml # Configuration file to start the 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
readme.md # Classification model service deployment document
run_cpp_serving.sh # Start the C++ Serving departmentscript
test_cpp_serving_client.py # Script for sending C++ serving prediction requests in rpc mode

3.2.1 Python Serving¶

Start the service:

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

send request:

# send service 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': ['label', 'prob'], 'value': ["['daisy']", '[0.9341402053833008]'], 'tensors ': []}

turn off 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.

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.
Modify the client file ResNet50_client/serving_client_conf.prototxt , change the field after feed_type: to 20, change the field after the first shape: to 1 and delete the rest of the shape fields.
```
feed_var {
  name: "inputs"
  alias_name: "inputs"
  is_lod_tensor: false
  feed_type: 20
  shape: 1
}
```
Modify part of the code of test_cpp_serving_client
1. Modify the feed={"inputs": image} part of the code, and change the path after load_client_config to ResNet50_client/serving_client_conf.prototxt .
2. Modify the feed={"inputs": image} part of the code, and change inputs to be the same as the feed_var field in ResNet50_client/serving_client_conf.prototxt nameis the same. Sincenamein some model client files isxinstead ofinputs` , you need to pay attention to this when using these models for C++ Serving deployment.

Start the service:

# Start the service, the service runs in the background, and the running log is saved in nohup.txt
# 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 prediction will be printed in the cmd window, and the results are as follows:
```
prediction: daisy, probability: 0.9341399073600769
```
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