fix bert doc

python/examples/bert/README.md

@@ -11,14 +11,16 @@ This example use model [BERT Chinese Model](https://www.paddlepaddle.org.cn/hubd
 
 Install paddlehub first
 ```
-pip install paddlehub
+pip3 install paddlehub
 ```
 
 run 
 ```
-python prepare_model.py 128
+python3 prepare_model.py 128
 ```
 
+**PaddleHub only support Python 3.5+**
+
 the 128 in the command above means max_seq_len in BERT model, which is the length of sample after preprocessing.
 the config file and model file for server side are saved in the folder bert_seq128_model.
 the config file generated for client side is saved in the folder bert_seq128_client.

python/examples/bert/README_CN.md

@@ -10,11 +10,11 @@
 示例中采用[Paddlehub](https://github.com/PaddlePaddle/PaddleHub)中的[BERT中文模型](https://www.paddlepaddle.org.cn/hubdetail?name=bert_chinese_L-12_H-768_A-12&en_category=SemanticModel)。
 请先安装paddlehub
 ```
-pip install paddlehub
+pip3 install paddlehub
 ```
 执行
 ```
-python prepare_model.py 128
+python3 prepare_model.py 128
 ```
 参数128表示BERT模型中的max_seq_len，即预处理后的样本长度。
 生成server端配置文件与模型文件，存放在bert_seq128_model文件夹。

python/examples/pipeline/imagenet/README_CN.md

 # Imagenet Pipeline WebService
 
-这里以 Uci 服务为例来介绍 Pipeline WebService 的使用。
+这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
 
 ## 获取模型
 ```
@@ -10,10 +10,11 @@ sh get_model.sh
 ## 启动服务
 
 ```
-python web_service.py &>log.txt &
+python resnet50_web_service.py &>log.txt &
 ```
 
 ## 测试
 ```
-curl -X POST -k http://localhost:18082/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+python pipeline_rpc_client.py
 ```
+