This quick start example is only for users who already have a model to deploy and we prepare a ready-to-deploy model here. If you want to know how to use paddle serving from offline training to online serving, please reference to [Train_To_Service](https://github.com/PaddlePaddle/Serving/blob/develop/doc/TRAIN_TO_SERVICE.md)
Here, `client.predict` function has two arguments. `feed` is a `python dict` with model input variable alias name and values. `fetch` assigns the prediction variables to be returned from servers. In the example, the name of `"x"` and `"price"` are assigned when the servable model is saved during training.
Here, `client.predict` function has two arguments. `feed` is a `python dict` with model input variable alias name and values. `fetch` assigns the prediction variables to be returned from servers. In the example, the name of `"x"` and `"price"` are assigned when the servable model is saved during training.
<h2align="center"> Pre-built services with Paddle Serving</h2>
<h2align="center">Some Key Features of Paddle Serving</h2>
<h3align="center">Chinese Word Segmentation</h4>
-**Description**:
``` shell
Chinese word segmentation HTTP service that can be deployed with one line command.
In the default centos7 image we provide, the Python path is `/usr/bin/python`. If you want to use our centos6 image, you need to set it to `export PYTHONROOT=/usr/local/python2.7/`.
## Compile Server
## Compile Server
### Integrated CPU version paddle inference library
### Integrated CPU version paddle inference library
In the Chinese sentiment classification task, the Chinese word segmentation needs to be done through [LAC task] (../lac). Set model path by ```lac_model_path``` and dictionary path by ```lac_dict_path```.
In the Chinese sentiment classification task, the Chinese word segmentation needs to be done through [LAC task] (../lac).
In this demo, the LAC task is placed in the preprocessing part of the HTTP prediction service of the sentiment classification task. The LAC prediction service is deployed on the CPU, and the sentiment classification task is deployed on the GPU, which can be changed according to the actual situation.
In this demo, the LAC task is placed in the preprocessing part of the HTTP prediction service of the sentiment classification task.
## Client prediction
## Client prediction
```
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "天气不错"}], "fetch":["class_probs"]}' http://127.0.0.1:9292/senta/prediction
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "天气不错"}], "fetch":["class_probs"]}' http://127.0.0.1:9292/senta/prediction
@@ -76,7 +76,7 @@ Preprocessing for Chinese word segmentation task.
...
@@ -76,7 +76,7 @@ Preprocessing for Chinese word segmentation task.
[example](../examples/senta/senta_web_service.py)
[example](../examples/senta/senta_web_service.py)
- The image preprocessing method is more flexible than the above method, and can be combined by the following multiple classes，[example](../examples/imagenet/image_rpc_client.py)
- The image preprocessing method is more flexible than the above method, and can be combined by the following multiple classes，[example](../examples/imagenet/resnet50_rpc_client.py)
