diff --git a/README.md b/README.md
index 4e64ebeb80b838241ab2d038532c164a0048b828..468441f45ab5f7b099216012e5fa3abb3edb809b 100644
--- a/README.md
+++ b/README.md
@@ -26,14 +26,6 @@ We consider deploying deep learning inference service online to be a user-facing
-Some Key Features
-
-- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
-- **Industrial serving features** supported, such as models management, online loading, online A/B testing etc.
-- **Distributed Key-Value indexing** supported which is especially useful for large scale sparse features as model inputs.
-- **Highly concurrent and efficient communication** between clients and servers supported.
-- **Multiple programming languages** supported on client side, such as Golang, C++ and python.
-- **Extensible framework design** which can support model serving beyond Paddle.
Installation
@@ -63,6 +55,70 @@ If you need install modules compiled with develop branch, please download packag
Client package support Centos 7 and Ubuntu 18, or you can use HTTP service without install client.
+
+ Pre-built services with Paddle Serving
+
+Chinese Word Segmentation
+
+- **Description**:
+``` shell
+Chinese word segmentation HTTP service that can be deployed with one line command.
+```
+
+- **Demo**:
+``` shell
+> python -m paddle_serving_app.package -get lac
+> tar -xzf lac.tar.gz
+> python lac_web_service.py 9292 &
+> curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9393/lac/prediction
+{"result":[{"word_seg":"我|爱|北京|天安门"}]}
+```
+
+Image Classification
+
+- **Description**:
+``` shell
+Image classification trained with Imagenet dataset. A label and corresponding probability will be returned.
+Note: This demo needs paddle-serving-server-gpu.
+```
+
+- **Download Servable Package**:
+``` shell
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/imagenet_demo.tar.gz
+```
+- **Host web service**:
+``` shell
+tar -xzf imagenet_demo.tar.gz
+python image_classification_service_demo.py resnet50_serving_model
+```
+- **Request sample**:
+
+
+
+
+
+
+
+``` shell
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
+```
+- **Request result**:
+``` shell
+{"label":"daisy","prob":0.9341403245925903}
+```
+
+
+
+
Some Key Features
+
+- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
+- **Industrial serving features** supported, such as models management, online loading, online A/B testing etc.
+- **Distributed Key-Value indexing** supported which is especially useful for large scale sparse features as model inputs.
+- **Highly concurrent and efficient communication** between clients and servers supported.
+- **Multiple programming languages** supported on client side, such as Golang, C++ and python.
+- **Extensible framework design** which can support model serving beyond Paddle.
+
+
Quick Start Example
### Boston House Price Prediction model
@@ -120,66 +176,6 @@ print(fetch_map)
```
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.
- Pre-built services with Paddle Serving
-
-Chinese Word Segmentation
-
-- **Description**:
-``` shell
-Chinese word segmentation HTTP service that can be deployed with one line command.
-```
-
-- **Download Servable Package**:
-``` shell
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/lac/lac_model_jieba_web.tar.gz
-```
-- **Host web service**:
-``` shell
-tar -xzf lac_model_jieba_web.tar.gz
-python lac_web_service.py jieba_server_model/ lac_workdir 9292
-```
-- **Request sample**:
-``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9292/lac/prediction
-```
-- **Request result**:
-``` shell
-{"word_seg":"我|爱|北京|天安门"}
-```
-
-Image Classification
-
-- **Description**:
-``` shell
-Image classification trained with Imagenet dataset. A label and corresponding probability will be returned.
-Note: This demo needs paddle-serving-server-gpu.
-```
-
-- **Download Servable Package**:
-``` shell
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/imagenet_demo.tar.gz
-```
-- **Host web service**:
-``` shell
-tar -xzf imagenet_demo.tar.gz
-python image_classification_service_demo.py resnet50_serving_model
-```
-- **Request sample**:
-
-
-
-
-
-
-
-``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
-```
-- **Request result**:
-``` shell
-{"label":"daisy","prob":0.9341403245925903}
-```
-
More Demos
| Key | Value |