add doc

doc/ABTEST_IN_PADDLE_SERVING.md

+# ABTEST in Paddle Serving
+
+This document will use an example of text classification task based on IMDB dataset to show how to build a A/B Test framework using Paddle Serving. The structure relationship between the client and servers in the example is shown in the figure below.
+
+<img src="/abtest.png" style="zoom:33%;" />
+
+Note that:  A/B Test is only applicable to RPC mode, not web mode.
+
+### Download Data and Models
+
+```shell
+cd Serving/python/examples/imdb
+sh get_data.sh
+```
+
+### Processing Data
+
+The following Python code will process the data `test_data/part-0` and write to the `processed.data` file.
+
+``` python
+from imdb_reader import IMDBDataset
+imdb_dataset = IMDBDataset()
+imdb_dataset.load_resource('imdb.vocab')
+
+with open('test_data/part-0') as fin:
+    with open('processed.data', 'w') as fout:
+        for line in fin:
+            word_ids, label = imdb_dataset.get_words_and_label(line)
+            fout.write("{};{}\n".format(','.join([str(x) for x in word_ids]), label[0]))
+```
+
+### Start Server
+
+Here, we [use docker](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER.md) to start the server-side service. 
+
+First, start the BOW server, which enables the `8000` port:
+
+``` shell
+docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/ctr/paddleserving:0.1.3
+docker exec -it bow-server bash
+pip install paddle-serving-server
+python -m paddle_serving_server.serve --model model --port 8000 >std.log 2>err.log &
+exit
+```
+
+Similarly, start the LSTM server, which enables the `9000` port:
+
+```bash
+docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/ctr/paddleserving:0.1.3
+docker exec -it lstm-server bash
+pip install paddle-serving-server
+python -m paddle_serving_server.serve --model model --port 9000 >std.log 2>err.log &
+exit
+```
+
+### Start Client
+
+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
+from paddle_serving_client import Client
+
+client = Client()
+client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
+client.add_variant("bow", ["127.0.0.1:8000"], 10)
+client.add_variant("lstm", ["127.0.0.1:9000"], 90)
+client.connect()
+
+with open('processed.data') as f:
+    cnt = {"bow": {'acc': 0, 'total': 0}, "lstm": {'acc': 0, 'total': 0}}
+    for line in f:
+        word_ids, label = line.split(';')
+        word_ids = [int(x) for x in word_ids.split(',')]
+        feed = {"words": word_ids}
+        fetch = ["acc", "cost", "prediction"]
+        [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True)
+        if (float(fetch_map["prediction"][1]) - 0.5) * (float(label[0]) - 0.5) > 0:
+            cnt[tag]['acc'] += 1
+        cnt[tag]['total'] += 1
+
+    for tag, data in cnt.items():
+        print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc']) / float(data['total'])))
+```
+
+In the code, the function `client.add_variant(tag, clusters, variant_weight)` is to add a variant with label `tag` and flow weight `variant_weight`. In this example, a BOW variant with label of `bow` and flow weight of `10`, and an LSTM variant with label of `lstm` and a flow weight of `90` are added. The flow on the client side will be distributed to two variants according to the ratio of `10:90`.
+
+When making prediction on the client side, if the parameter `need_variant_tag=True` is specified, the response will contains the variant tag corresponding to the distribution flow.
+
+### Expected Results
+
+``` python
+[lstm](total: 1867) acc: 0.490091055169
+[bow](total: 217) acc: 0.73732718894
+```

doc/ABTEST_IN_PADDLE_SERVING_CN.md

+# 如何使用Paddle Serving做ABTEST
+
+该文档将会用一个基于IMDB数据集的文本分类任务的例子，介绍如何使用Paddle Serving搭建A/B Test框架，例中的Client端、Server端结构如下图所示。
+
+<img src="/abtest.png" style="zoom:33%;" />
+
+需要注意的是：A/B Test只适用于RPC模式，而不适用于WEB模式。
+
+### 下载数据以及模型
+
+``` shell
+cd Serving/python/examples/imdb
+sh get_data.sh
+```
+
+### 处理数据
+
+下面Python代码将处理`test_data/part-0`的数据，写入`processed.data`文件中。
+
+```python
+from imdb_reader import IMDBDataset
+imdb_dataset = IMDBDataset()
+imdb_dataset.load_resource('imdb.vocab')
+
+with open('test_data/part-0') as fin:
+    with open('processed.data', 'w') as fout:
+        for line in fin:
+            word_ids, label = imdb_dataset.get_words_and_label(line)
+            fout.write("{};{}\n".format(','.join([str(x) for x in word_ids]), label[0]))
+```
+
+### 启动Server端
+
+这里采用[Docker方式](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER_CN.md)启动Server端服务。
+
+首先启动BOW Server，该服务启用`8000`端口：
+
+```bash
+docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/ctr/paddleserving:0.1.3
+docker exec -it bow-server bash
+pip install paddle-serving-server
+python -m paddle_serving_server.serve --model model --port 8000 >std.log 2>err.log &
+exit
+```
+
+同理启动LSTM Server，该服务启用`9000`端口：
+
+```bash
+docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/ctr/paddleserving:0.1.3
+docker exec -it lstm-server bash
+pip install paddle-serving-server
+python -m paddle_serving_server.serve --model model --port 9000 >std.log 2>err.log &
+exit
+```
+
+### 启动Client端
+
+在宿主机运行下面Python代码启动Client端，需要确保宿主机装好`paddle-serving-client`包。
+
+```python
+from paddle_serving_client import Client
+
+client = Client()
+client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
+client.add_variant("bow", ["127.0.0.1:8000"], 10)
+client.add_variant("lstm", ["127.0.0.1:9000"], 90)
+client.connect()
+
+with open('processed.data') as f:
+    cnt = {"bow": {'acc': 0, 'total': 0}, "lstm": {'acc': 0, 'total': 0}}
+    for line in f:
+        word_ids, label = line.split(';')
+        word_ids = [int(x) for x in word_ids.split(',')]
+        feed = {"words": word_ids}
+        fetch = ["acc", "cost", "prediction"]
+        [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True)
+        if (float(fetch_map["prediction"][1]) - 0.5) * (float(label[0]) - 0.5) > 0:
+            cnt[tag]['acc'] += 1
+        cnt[tag]['total'] += 1
+
+    for tag, data in cnt.items():
+        print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc']) / float(data['total'])))
+```
+
+代码中，`client.add_variant(tag, clusters, variant_weight)`是为了添加一个标签为`tag`、流量权重为`variant_weight`的variant。在这个样例中，添加了一个标签为`bow`、流量权重为`10`的BOW variant，以及一个标签为`lstm`、流量权重为`90`的LSTM variant。Client端的流量会根据`10:90`的比例分发到两个variant。
+
+Client端做预测时，若指定参数`need_variant_tag=True`，返回值则包含分发流量对应的variant标签。
+
+### 预期结果
+
+``` bash
+[lstm](total: 1867) acc: 0.490091055169
+[bow](total: 217) acc: 0.73732718894
+```