Merge pull request #422 from wangjiawei04/doc_night

Add ABTEST doc test

doc/ABTEST_IN_PADDLE_SERVING.md

@@ -19,6 +19,7 @@ sh get_data.sh
 
 The following Python code will process the data `test_data/part-0` and write to the `processed.data` file.
 
+[//file]:#process.py
 ``` python
 from imdb_reader import IMDBDataset
 imdb_dataset = IMDBDataset()
@@ -59,7 +60,8 @@ exit
 
 Run the following Python code on the host computer to start client. Make sure that the host computer is installed with the `paddle-serving-client` package.
 
-``` go
+[//file]:#ab_client.py
+``` python
 from paddle_serving_client import Client
 
 client = Client()
@@ -94,3 +96,24 @@ When making prediction on the client side, if the parameter `need_variant_tag=Tr
 [lstm](total: 1867) acc: 0.490091055169
 [bow](total: 217) acc: 0.73732718894
 ```
+
+<!--
+cp ../Serving/python/examples/imdb/get_data.sh .
+cp ../Serving/python/examples/imdb/imdb_reader.py .
+pip install -U paddle_serving_server
+pip install -U paddle_serving_client
+pip install -U paddlepaddle
+sh get_data.sh
+python process.py
+python -m paddle_serving_server.serve --model imdb_bow_model --port 8000 --workdir workdir1 &
+sleep 5
+python -m paddle_serving_server.serve --model imdb_lstm_model --port 9000  --workdir workdir2 &
+sleep 5
+python ab_client.py >log.txt
+if [[ $? -eq 0 ]]; then
+    echo "test success"
+else
+    echo "test fail"
+fi
+ps -ef | grep "paddle_serving_server" | grep -v grep | awk '{print $2}' | xargs kill
+-->

doc/BERT_10_MINS.md

@@ -102,4 +102,5 @@ if [[ $? -eq 0 ]]; then
 else
     echo "test fail"
 fi
+ps -ef | grep "paddle_serving_server" | grep -v grep | awk '{print $2}' | xargs kill
 -->

doc/TRAIN_TO_SERVICE.md

@@ -288,7 +288,7 @@ 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
-cat test_data/part-0 | python test_client.py imdb_lstm_client_conf / serving_client_conf.prototxt imdb.vocab
+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%.
@@ -350,7 +350,7 @@ In the above command, the first parameter is the saved server-side model and con
 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
+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.
 

doc/doc_test_list

 BERT_10_MINS.md
+ABTEST_IN_PADDLE_SERVING.md