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: ./inference/cls_infer.pdmodel
Model parameters file: ./inference/cls_infer.pdiparams
```
**The model path can be found and modified in `params.py`.** More models provided by PaddleClas can be obtained from the [model library](../../docs/en/models/models_intro_en.md). You can also use models trained by yourself.
### 3. Install Service Module
* On Linux platform, the examples are as follows.
```shell
hub install deploy/hubserving/clas/
```
* On Windows platform, the examples are as follows.
```shell
hub install deploy\hubserving\clas\
```
### 4. Start service
#### Way 1. Start with command line parameters (CPU only)
**start command：**
```shell
$ hub serving start --modules Module1==Version1 \
--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<br>*`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<br>*`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 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：**
```shell
hub serving start --config/-c config.json
```
Wherein, the format of `config.json` is as follows:
- 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.**
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 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:
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 `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.
After modifying and installing (`hub install deploy/hubserving/clas/`) and before deploying, you can use `python deploy/hubserving/clas/test.py` to test the installed service module.
