diff --git a/doc/TRAIN_TO_SERVICE.md b/doc/TRAIN_TO_SERVICE.md
index 4219e66948a9bc3b0ae43e5cda61aad8ae35b3a0..30c180f9361e48d38459ceeb2e85561d1a83e168 100644
--- a/doc/TRAIN_TO_SERVICE.md
+++ b/doc/TRAIN_TO_SERVICE.md
@@ -228,7 +228,7 @@ if __name__ == "__main__":
 
 </details>
 
-! [Training process](./ imdb_loss.png) As can be seen from the above figure, the loss of the model starts to converge after the 65th round. We save the model and configuration file after the 65th round of training is completed. The saved files are divided into imdb_cnn_client_conf and imdb_cnn_model folders. The former contains client-side configuration files, and the latter contains server-side configuration files and saved model files.
+![Training process](./imdb_loss.png) As can be seen from the above figure, the loss of the model starts to converge after the 65th round. We save the model and configuration file after the 65th round of training is completed. The saved files are divided into imdb_cnn_client_conf and imdb_cnn_model folders. The former contains client-side configuration files, and the latter contains server-side configuration files and saved model files.
 The parameter list of the save_model function is as follows:
 
 | Parameter            | Meaning                                                        |
@@ -243,10 +243,10 @@ The parameter list of the save_model function is as follows:
 
 The Paddle Serving framework supports two types of prediction service methods. One is to communicate through RPC and the other is to communicate through HTTP. The deployment and use of RPC prediction service will be introduced first. The deployment and use of HTTP prediction service will be introduced at Step 8. .
 
-`` `shell
+```shell
 python -m paddle_serving_server.serve --model imdb_cnn_model / --port 9292 #cpu prediction service
 python -m paddle_serving_server_gpu.serve --model imdb_cnn_model / --port 9292 --gpu_ids 0 #gpu prediction service
-`` `
+```
 
 The parameter --model in the command specifies the server-side model and configuration file directory previously saved, --port specifies the port of the prediction service. When deploying the gpu prediction service using the gpu version, you can use --gpu_ids to specify the gpu used.
 
@@ -287,13 +287,13 @@ The script receives data from standard input and prints out the probability that
 
 The client implemented in the previous step runs the prediction service as an example. The usage method is as follows:
 
-`` `shell
+```shell
 cat test_data/part-0 | python test_client.py imdb_lstm_client_conf / serving_client_conf.prototxt imdb.vocab
-`` `
+```
 
 Using 2084 samples in the test_data/part-0 file for test testing, the model prediction accuracy is 88.19%.
 
-** Note **: The effect of each model training may be slightly different, and the accuracy of predictions using the trained model will be close to the examples but may not be exactly the same.
+**Note**: The effect of each model training may be slightly different, and the accuracy of predictions using the trained model will be close to the examples but may not be exactly the same.
 
 ## Step8: Deploy HTTP Prediction Service
 
@@ -349,13 +349,13 @@ In the above command, the first parameter is the saved server-side model and con
 ## Step9: Call the prediction service with plaintext data
 After starting the HTTP prediction service, you can make prediction with a single command:
 
-`` `
+```
 curl -H "Content-Type: application / json" -X POST -d '{"words": "i am very sad | 0", "fetch": ["prediction"]}' http://127.0.0.1:9292/imdb/prediction
-`` `
+```
 When the inference process is normal, the prediction probability is returned, as shown below.
 
-`` `
+```
 {"prediction": [0.5592559576034546,0.44074398279190063]}
-`` `
+```
 
-** Note **: The effect of each model training may be slightly different, and the inferred probability value using the trained model may not be consistent with the example.
+**Note**: The effect of each model training may be slightly different, and the inferred probability value using the trained model may not be consistent with the example.
diff --git a/python/examples/criteo_ctr/README.md b/python/examples/criteo_ctr/README.md
index e59947ba377160fe0dc1d1105f35c467e50d32ad..4780fb667dfe2a0bc4bc4497dc24a495b59aa3ac 100644
--- a/python/examples/criteo_ctr/README.md
+++ b/python/examples/criteo_ctr/README.md
@@ -1,26 +1,31 @@
-## CTR预测服务
+## CTR Prediction Service
 
-### 获取样例数据
+([简体中文](./README_CN.md)|English)
+
+### download criteo dataset
 ```
 sh get_data.sh
 ```
 
-### 保存模型和配置文件
+### download inference model
 ```
-python local_train.py
+wget https://paddle-serving.bj.bcebos.com/criteo_ctr_example/criteo_ctr_demo_model.tar.gz
+tar xf criteo_ctr_demo_model.tar.gz
+mv models/ctr_client_conf .
+mv models/ctr_serving_model .
 ```
-执行脚本后会在当前目录生成serving_server_model和serving_client_config文件夹。
+the directories like serving_server_model and serving_client_config will appear.
 
-### 启动RPC预测服务
+### Start RPC Inference Service
 
 ```
-python -m paddle_serving_server.serve --model ctr_serving_model/ --port 9292 #启动CPU预测服务
-python -m paddle_serving_server_gpu.serve --model ctr_serving_model/ --port 9292 --gpu_ids 0 #在GPU 0上启动预测服务
+python -m paddle_serving_server.serve --model ctr_serving_model/ --port 9292 #CPU RPC Service
+python -m paddle_serving_server_gpu.serve --model ctr_serving_model/ --port 9292 --gpu_ids 0 #RPC Service on GPU 0
 ```
 
-### 执行预测
+### RPC Infer
 
 ```
 python test_client.py ctr_client_conf/serving_client_conf.prototxt raw_data/
 ```
-预测完毕会输出预测过程的耗时。
+the latency will display in the end.
diff --git a/python/examples/criteo_ctr/README_CN.md b/python/examples/criteo_ctr/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..bee946aac8eca4293811057baff061bceb9508af
--- /dev/null
+++ b/python/examples/criteo_ctr/README_CN.md
@@ -0,0 +1,31 @@
+## CTR预测服务
+
+(简体中文|[English](./README.md))
+
+### 获取样例数据
+```
+sh get_data.sh
+```
+
+### 下载模型
+```
+wget https://paddle-serving.bj.bcebos.com/criteo_ctr_example/criteo_ctr_demo_model.tar.gz
+tar xf criteo_ctr_demo_model.tar.gz
+mv models/ctr_client_conf .
+mv models/ctr_serving_model .
+```
+会在当前目录出现serving_server_model和serving_client_config文件夹。
+
+### 启动RPC预测服务
+
+```
+python -m paddle_serving_server.serve --model ctr_serving_model/ --port 9292 #启动CPU预测服务
+python -m paddle_serving_server_gpu.serve --model ctr_serving_model/ --port 9292 --gpu_ids 0 #在GPU 0上启动预测服务
+```
+
+### 执行预测
+
+```
+python test_client.py ctr_client_conf/serving_client_conf.prototxt raw_data/
+```
+预测完毕会输出预测过程的耗时。
diff --git a/python/examples/criteo_ctr_with_cube/README.md b/python/examples/criteo_ctr_with_cube/README.md
index 4a69fd528a3d25f4010d6fdc724146027a90e896..25f171f5b07a6c58de68809ae6092c85f92b8116 100755
--- a/python/examples/criteo_ctr_with_cube/README.md
+++ b/python/examples/criteo_ctr_with_cube/README.md
@@ -7,7 +7,7 @@ in the root directory of this git project
 ```
 mkdir build_server
 cd build_server
-cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/  -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so  -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python  -DCLIENT_ONLY=OFF ..
+cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib64/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DSERVER=ON ..
 make -j10
 make install -j10
 ```
diff --git a/python/examples/criteo_ctr_with_cube/README_CN.md b/python/examples/criteo_ctr_with_cube/README_CN.md
index 5bc36869abe0c1f3fc010c2893731a0e86a1c270..47279cc8c2dd781324fe4c73d98f58cbd69319c9 100644
--- a/python/examples/criteo_ctr_with_cube/README_CN.md
+++ b/python/examples/criteo_ctr_with_cube/README_CN.md
@@ -6,7 +6,7 @@
 ```
 mkdir build_server
 cd build_server
-cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/  -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so  -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python  -DCLIENT_ONLY=OFF ..
+cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib64/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DSERVER=ON ..
 make -j10
 make install -j10
 ```