# Service deployment PaddleOCR provides 2 service deployment methods:: - Based on **HubServing**:Has been integrated into PaddleOCR ([code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)). Please follow this tutorial. - Based on **PaddleServing**:See PaddleServing official website for details ([demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)). Follow-up will also be integrated into PaddleOCR. The service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Select the corresponding service package to install and start service according to your needs. The directory is as follows: ``` deploy/hubserving/ └─ ocr_det detection module service package └─ ocr_rec recognition module service package └─ ocr_system two-stage series connection service package ``` Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows: ``` deploy/hubserving/ocr_system/ └─ __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 The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path. ### 1. Prepare the environment ```shell # Install paddlehub pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple # Set environment variables on Linux export PYTHONPATH=. # Set environment variables on Windows SET PYTHONPATH=. ``` ### 2. Install Service Module PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs. * On Linux platform, the examples are as follows. ```shell # Install the detection service module: hub install deploy/hubserving/ocr_det/ # Or, install the recognition service module: hub install deploy/hubserving/ocr_rec/ # Or, install the 2-stage series service module: hub install deploy/hubserving/ocr_system/ ``` * On Windows platform, the examples are as follows. ```shell # Install the detection service module: hub install deploy\hubserving\ocr_det\ # Or, install the recognition service module: hub install deploy\hubserving\ocr_rec\ # Or, install the 2-stage series service module: hub install deploy\hubserving\ocr_system\ ``` ### 3. Start service #### Way 1. Start with command line parameters (CPU only) **start command:** ```shell $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \ --port XXXX \ --use_multiprocess \ --workers \ ``` **parameters:** |parameters|usage| |-|-| |--modules/-m|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs
*`When Version is not specified, the latest version is selected by default`*| |--port/-p|Service port, default is 8866| |--use_multiprocess|Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines
*`Windows operating system only supports single-process mode`*| |--workers|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 example, start the 2-stage series service: ```shell hub serving start -m ocr_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:** ```shell hub serving start --config/-c config.json ``` Wherein, the format of `config.json` is as follows: ```python { "modules_info": { "ocr_system": { "init_args": { "version": "1.0.0", "use_gpu": true }, "predict_args": { } } }, "port": 8868, "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**. - 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.** For example, use GPU card No. 3 to start the 2-stage series service: ```shell export CUDA_VISIBLE_DEVICES=3 hub serving start -c deploy/hubserving/ocr_system/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: ```shell python tools/test_hubserving.py server_url image_path ``` Two parameters need to be passed to the script: - **server_url**:service address,format of which is `http://[ip_address]:[port]/predict/[module_name]` For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective `server_url` would be: `http://127.0.0.1:8866/predict/ocr_det` `http://127.0.0.1:8867/predict/ocr_rec` `http://127.0.0.1:8868/predict/ocr_system` - **image_path**:Test image path, can be a single image path or an image directory path **Eg.** ```shell python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/ ``` ## Returned result format The returned result is a list. Each item in the list is a dict. The dict may contain three fields. The information is as follows: |field name|data type|description| |-|-|-| |text|str|text content| |confidence|float|text recognition confidence| |text_region|list|text location coordinates| The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain `text_region`. The details are as follows: |field name/module name|ocr_det|ocr_rec|ocr_system| |-|-|-|-| |text||✔|✔| |confidence||✔|✔| |text_region|✔||✔| **Note:** If you need to add, delete or modify the returned fields, you can modify the file `module.py` of the corresponding module. For the complete process, 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 (take the modification of `ocr_system` for example): - 1. Stop service ```shell 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. For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` 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. It is suggested to run `module.py` directly for debugging after modification before starting the service test. - 3. Uninstall old service module ```shell hub uninstall ocr_system ``` - 4. Install modified service module ```shell hub install deploy/hubserving/ocr_system/ ``` - 5. Restart service ```shell hub serving start -m ocr_system ```