-[Model Configuration](#model-configuration): Auto generated when converting model. Specify model input/output.
-[C++ Serving](#c-serving): High-performance scenarios. Specify how to start quickly and start with user-defined configuration.
-[Python Pipeline](#python-pipeline): Multiple model combined scenarios.
```JSON
port: 8010
services {
name: "ImageClassifyService"
workflows: "workflow1"
## Model Configuration
The model configuration is generated by converting PaddleServing model and named serving_client_conf.prototxt/serving_server_conf.prototxt. It specifies the info of input/output so that users can fill parameters easily. The model configuration file should not be modified. See the [Saving guide](SAVE.md) for model converting. The model configuration file provided must be a `core/configure/proto/general_model_config.proto`.
Example：
```
feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 13
}
fetch_var {
name: "concat_1.tmp_0"
alias_name: "concat_1.tmp_0"
is_lod_tensor: false
fetch_type: 1
shape: 3
shape: 640
shape: 640
}
```
其中
- feed_var：model input
- fetch_var：model output
- name：node name
- alias_name：alias name
- is_lod_tensor：lod tensor, ref to [Lod Introduction](LOD.md)
Mostly, the flags can meet the demand. However, the model configuration files can be modified by user that include service.prototxt、workflow.prototxt、resource.prototxt、model_toolkit.prototxt、proj.conf.
Example starting with self-defined config:
```BASH
/bin/serving --flagfile=proj.conf
```
#### 2.1 proj.conf
You can provide proj.conf with lots of flags:
```
# for paddle inference
--precision=fp32
--use_calib=False
--reload_interval_s=10
# for brpc
--max_concurrency=0
--num_threads=10
--bthread_concurrency=10
--max_body_size=536870912
# default path
--inferservice_path=conf
--inferservice_file=infer_service.prototxt
--resource_path=conf
--resource_file=resource.prototxt
--workflow_path=conf
--workflow_file=workflow.prototxt
```
The table below sets out the detailed description:
| name | Default | Description |
|------|--------|------|
|precision|"fp32"|Precision Mode, support FP32, FP16, INT8|
|use_calib|False|Only for deployment with TensorRT|
|reload_interval_s|10|Reload interval|
|max_concurrency|0|Limit of request processing in parallel, 0: unlimited|
|num_threads|10|Number of brpc service thread|
|bthread_concurrency|10|Number of bthread|
|max_body_size|536870912|Max size of brpc message|
|inferservice_path|"conf"|Path of inferservice conf|
|inferservice_file|"infer_service.prototxt"|Filename of inferservice conf|
|resource_path|"conf"|Path of resource conf|
|resource_file|"resource.prototxt"|Filename of resource conf|
|workflow_path|"conf"|Path of workflow conf|
|workflow_file|"workflow.prototxt"|Filename of workflow conf|
#### 2.2 service.prototxt
To set listening port, modify service.prototxt. You can set the `--inferservice_path` and `--inferservice_file` to instruct the server to check for service.prototxt. The `service.prototxt` file provided must be a `core/configure/server_configure.protobuf:InferServiceConf`.
To server user-defined OP, modify workflow.prototxt. You can set the `--workflow_path` and `--inferservice_file` to instruct the server to check for workflow.prototxt. The `workflow.prototxt` provided must be a `core/configure/server_configure.protobuf:Workflow`.
In the blow example, you are serving model with 3 OPs. The GeneralReaderOp converts the input data to tensor. The GeneralInferOp which depends the output of GeneralReaderOp predicts the tensor. The GeneralResponseOp return the output data.
You may modify resource.prototxt to set the path of model files. You can set the `--resource_path` and `--resource_file` to instruct the server to check for resource.prototxt. The `resource.prototxt` provided must be a `core/configure/server_configure.proto:Workflow`.
- model_toolkit_path: The diectory path of model_toolkil.prototxt.
- model_toolkit_file: The file name of model_toolkil.prototxt.
- general_model_path: The diectory path of general_model.prototxt.
- general_model_file: The file name of general_model.prototxt.
Cube是Paddle Serving中用于大规模稀疏参数的组件。
#### 2.5 model_toolkit.prototxt
# 4. model_toolkit.prototxt
The model_toolkit.prototxt specifies the parameters of predictor engines. The `model_toolkit.prototxt` provided must be a `core/configure/server_configure.proto:ModelToolkitConf`.
#RPC port. The RPC port and HTTP port cannot be empyt at the same time. If the RPC port is empty and the HTTP port is not empty, the RPC port is automatically set to HTTP port+1.
#HTTP port. The RPC port and the HTTP port cannot be empty at the same time. If the RPC port is available and the HTTP port is empty, the HTTP port is not automatically generated
Single-machine multi-card inference can be abstracted into M OP processes bound to N GPU cards. It is related to the configuration of three parameters in config.yml. First, select the process mode, the number of concurrent processes is the number of processes, and devices is the GPU card ID.The binding method is to traverse the GPU card ID when the process starts, for example, start 7 OP processes, set devices:0,1,2 in config.yml, then the first, fourth, and seventh started processes are bound to the 0 card, and the second , 4 started processes are bound to 1 card, 3 and 6 processes are bound to card 2.
Reference config.yaml:
```YAML
#True, thread model；False，process model
is_thread_op: False
#concurrency，is_thread_op=True，thread otherwise process
In addition to supporting CPU and GPU, Pipeline also supports the deployment of a variety of heterogeneous hardware. It consists of device_type and devices in config.yml. Use device_type to specify the type first, and judge according to devices when it is vacant. The device_type is described as follows:
