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Service deployment based on PaddleHub Serving

PaddleClas supports rapid service deployment through PaddleHub. Currently, the deployment of image classification is supported. Please look forward to the deployment of image recognition.

Catalogue

1 Introduction

The hubserving service deployment configuration service package clas contains 3 required files, the directories are as follows:

deploy/hubserving/clas/
├── __init__.py # Empty file, required
├── config.json # Configuration file, optional, passed in as a parameter when starting the service with configuration
├── module.py # The main module, required, contains the complete logic of the service
└── params.py # Parameter file, required, including model path, pre- and post-processing parameters and other parameters

2. Prepare the environment

# Install paddlehub, version 2.1.0 is recommended
python3.7 -m pip install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple

3. Download the inference model

Before installing the service module, you need to prepare the inference model and put it in the correct path. The default model path is:

  • Classification inference model structure file: PaddleClas/inference/inference.pdmodel
  • Classification inference model weight file: PaddleClas/inference/inference.pdiparams

Notice:

  • Model file paths can be viewed and modified in PaddleClas/deploy/hubserving/clas/params.py:

    "inference_model_dir": "../inference/"

  • Model files (including .pdmodel and .pdiparams) must be named inference.

  • We provide a large number of pre-trained models based on the ImageNet-1k dataset. For the model list and download address, see Model Library Overview, or you can use your own trained and converted models.

4. Install the service module

  • In the Linux environment, the installation example is as follows:

    cd PaddleClas/deploy
# Install the service module:
hub install hubserving/clas/

  • In the Windows environment (the folder separator is \), the installation example is as follows:

    cd PaddleClas\deploy
# Install the service module:
hub install hubserving\clas\

5. Start service

5.1 Start with command line parameters

This method only supports prediction using CPU. Start command:

hub serving start \
--modules clas_system
--port 8866

This completes the deployment of a serviced API, using the default port number 8866.

Parameter Description:

parameters uses
--modules/-m [required] PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs
When no Version is specified, the latest is selected by default version
--port/-p [OPTIONAL] Service port, default is 8866
--use_multiprocess [Optional] Whether to enable the concurrent mode, the default is single-process mode, it is recommended to use this mode for multi-core CPU machines
Windows operating system only supports single-process mode
--workers [Optional] The number of concurrent tasks specified in concurrent mode, the default is 2*cpu_count-1, where cpu_count is the number of CPU cores
For more deployment details, see PaddleHub Serving Model One-Click Service Deployment

5.2 Start with configuration file

This method only supports prediction using CPU or GPU. Start command:

hub serving start -c config.json

Among them, the format of config.json is as follows:

{
    "modules_info": {
        "clas_system": {
            "init_args": {
                "version": "1.0.0",
                "use_gpu": true,
                "enable_mkldnn": false
            },
            "predict_args": {
            }
        }
    },
    "port": 8866,
    "use_multiprocess": false,
    "workers": 2
}

Parameter Description:

  • The configurable parameters in init_args are consistent with the _initialize function interface in module.py. in,
    • When use_gpu is true, it means to use GPU to start the service.
    • When enable_mkldnn is true, it means to use MKL-DNN acceleration.
  • The configurable parameters in predict_args are consistent with the predict function interface in module.py.

Notice:

  • When using the configuration file to start the service, the parameter settings in the configuration file will be used, and other command line parameters will be ignored;
  • If you use GPU prediction (ie, use_gpu is set to true), you need to set the CUDA_VISIBLE_DEVICES environment variable to specify the GPU card number used before starting the service, such as: export CUDA_VISIBLE_DEVICES=0;
  • use_gpu cannot be true at the same time as use_multiprocess;
  • ** When both use_gpu and enable_mkldnn are true, enable_mkldnn will be ignored and GPU** will be used.

If you use GPU No. 3 card to start the service:

cd PaddleClas/deploy
export CUDA_VISIBLE_DEVICES=3
hub serving start -c hubserving/clas/config.json

6. Send prediction requests

After configuring the server, you can use the following command to send a prediction request to get the prediction result:

cd PaddleClas/deploy
python3.7 hubserving/test_hubserving.py \
--server_url http://127.0.0.1:8866/predict/clas_system \
--image_file ./hubserving/ILSVRC2012_val_00006666.JPEG \
--batch_size 8

Predicted output

The result(s): class_ids: [57, 67, 68, 58, 65], label_names: ['garter snake, grass snake', 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 'sidewinder, horned rattlesnake, Crotalus cerastes' , 'water snake', 'sea snake'], scores: [0.21915, 0.15631, 0.14794, 0.13177, 0.12285]
The average time of prediction cost: 2.970 s/image
The average time cost: 3.014 s/image
The average top-1 score: 0.110

Script parameter description:

  • server_url: Service address, the format is http://[ip_address]:[port]/predict/[module_name].
  • image_path: The test image path, which can be a single image path or an image collection directory path.
  • batch_size: [OPTIONAL] Make predictions in batch_size size, default is 1.
  • resize_short: [optional] When preprocessing, resize by short edge, default is 256.
  • crop_size: [Optional] The size of the center crop during preprocessing, the default is 224.
  • normalize: [Optional] Whether to perform normalize during preprocessing, the default is True.
  • to_chw: [Optional] Whether to adjust to CHW order when preprocessing, the default is True.

Note: If you use Transformer series models, such as DeiT_***_384, ViT_***_384, etc., please pay attention to the input data size of the model, you need to specify --resize_short=384 -- crop_size=384.

Return result format description: The returned result is a list (list), including the top-k classification results, the corresponding scores, and the time-consuming prediction of this image, as follows:

list: return result
└──list: first image result
   ├── list: the top k classification results, sorted in descending order of score
   ├── list: the scores corresponding to the first k classification results, sorted in descending order of score
   └── float: The image classification time, in seconds

7. User defined service module modification

If you need to modify the service logic, you need to do the following:

  1. Stop the service

    hub serving stop --port/-p XXXX

  2. Go to the corresponding module.py and params.py and other files to modify the code according to actual needs. module.py needs to be reinstalled after modification (hub install hubserving/clas/) and deployed. Before deploying, you can use the python3.7 hubserving/clas/module.py command to quickly test the code ready for deployment.

  3. Uninstall the old service pack

    hub uninstall clas_system

  4. Install the new modified service pack

    hub install hubserving/clas/

  5. Restart the service

    hub serving start -m clas_system

Notice: Common parameters can be modified in PaddleClas/deploy/hubserving/clas/params.py:

  • To replace the model, you need to modify the model file path parameters: 
    "inference_model_dir":
  • Change the number of top-k results returned when postprocessing: 
    'topk':
  • The mapping file corresponding to the lable and class id when changing the post-processing: 
    'class_id_map_file':

In order to avoid unnecessary delay and be able to predict with batch_size, data preprocessing logic (including resize, crop and other operations) is completed on the client side, so it needs to modify data preprocessing logic related code in PaddleClas/deploy/hubserving/test_hubserving.py# L41-L47 and PaddleClas/deploy/hubserving/test_hubserving.py#L51-L76.