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Service deployment based on PaddleHub Serving

HubServing service pack contains 3 files, the directory is as follows:

hubserving/clas/
  └─  __init__.py    Empty file, required
  └─  config.json    Configuration file, optional, passed in as a parameter when using configuration to start the service
  └─  module.py      Main module file, required, contains the complete logic of the service
  └─  params.py      Parameter file, required, including parameters such as model path, pre- and post-processing parameters

Quick start service

1. Prepare the environment

# Install version 2.0 of PaddleHub  
pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple

2. Download 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:

Model structure file: PaddleClas/inference/inference.pdmodel
Model parameters file: PaddleClas/inference/inference.pdiparams

• The model file path can be viewed and modified in PaddleClas/deploy/hubserving/clas/params.py.

  It should be noted that the prefix of model structure file and model parameters file must be inference.

• More models provided by PaddleClas can be obtained from the model library. You can also use models trained by yourself.

3. Install Service Module

• On Linux platform, the examples are as follows.

cd PaddleClas/deploy
hub install hubserving/clas/

• On Windows platform, the examples are as follows.

cd PaddleClas\deploy
hub install hubserving\clas\

4. Start service

Way 1. Start with command line parameters (CPU only)

start command：

$ hub serving start --modules Module1==Version1 \
                    --port XXXX \
                    --use_multiprocess \
                    --workers \

parameters：

For example, start the 2-stage series service:

hub serving start -m clas_system

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

Way 2. Start with configuration file（CPU、GPU）

start command：

hub serving start --config/-c config.json

Wherein, 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
}

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

Note:

• When using the configuration file to start the service, other parameters will be ignored.
• If you use GPU prediction (that is, use_gpu is set to true), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as: export CUDA_VISIBLE_DEVICES=0, otherwise you do not need to set it.
• use_gpu and use_multiprocess cannot be true at the same time.
• When both use_gpu and enable_mkldnn are set to true at the same time, GPU is used to run and enable_mkldnn will be ignored.

For example, use GPU card No. 3 to start the 2-stage series service:

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

Send prediction requests

After the service starts, you can use the following command to send a prediction request to obtain the prediction result:

cd PaddleClas/deploy
python hubserving/test_hubserving.py server_url image_path

Two required parameters need to be passed to the script:

• server_url: service address，format of which is http://[ip_address]:[port]/predict/[module_name]
• image_path: Test image path, can be a single image path or an image directory path
• batch_size: [Optional] batch_size. Default by 1.
• resize_short: [Optional] In preprocessing, resize according to short size. Default by 256。
• crop_size: [Optional] In preprocessing, centor crop size. Default by 224。
• normalize: [Optional] In preprocessing, whether to do normalize. Default by True。
• to_chw: [Optional] In preprocessing, whether to transpose to CHW. Default by True。

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

Eg.

python 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

Returned result format

The returned result is a list, including the top_k's classification results, corresponding scores and the time cost of prediction, details as follows.

list: The returned results
└─ list: The result of first picture
   └─ list: The top-k classification results, sorted in descending order of score
   └─ list: The scores corresponding to the top-k classification results, sorted in descending order of score
   └─ float: The time cost of predicting the picture, unit second

Note： If you need to add, delete or modify the returned fields, you can modify the corresponding module. For the details, refer to the user-defined modification service module in the next section.

User defined service module modification

If you need to modify the service logic, the following steps are generally required:

1. Stop service

hub serving stop --port/-p XXXX

2. Modify the code in the corresponding files, like module.py and params.py, according to the actual needs. You need re-install(hub install hubserving/clas/) and re-deploy after modifing module.py. After modifying and installing and before deploying, you can use python hubserving/clas/module.py to test the installed service module.

For example, if you need to replace the model used by the deployed service, you need to modify model path parameters cfg.model_file and cfg.params_file in params.py. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation.

3. Uninstall old service module

hub uninstall clas_system

4. Install modified service module

hub install hubserving/clas/

5. Restart service

hub serving start -m clas_system

Note:

Common parameters can be modified in params.py:

• Directory of model files(include model structure file and model parameters file):

  "inference_model_dir":
• The number of Top-k results returned during post-processing:

  'topk':
• Mapping file corresponding to label and class ID during post-processing:

  'class_id_map_file':

In order to avoid unnecessary delay and be able to predict in batch, the preprocessing (include resize, crop and other) is completed in the client, so modify test_hubserving.py if necessary.