Update README.md

README.md

@@ -101,17 +101,17 @@ git clone https://github.com/PaddlePaddle/Serving
 install python dependencies
 ```
 cd Serving
-pip install -r python/requirements.txt
+pip3 install -r python/requirements.txt
 ```
 
 ```shell
-pip install paddle-serving-client==0.6.0
-pip install paddle-serving-server==0.6.0 # CPU
-pip install paddle-serving-app==0.6.0
-pip install paddle-serving-server-gpu==0.6.0.post102 #GPU with CUDA10.2 + TensorRT7
+pip3 install paddle-serving-client==0.6.0
+pip3 install paddle-serving-server==0.6.0 # CPU
+pip3 install paddle-serving-app==0.6.0
+pip3 install paddle-serving-server-gpu==0.6.0.post102 #GPU with CUDA10.2 + TensorRT7
 # DO NOT RUN ALL COMMANDS! check your GPU env and select the right one
-pip install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6
-pip install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA10.1 + TensorRT7
+pip3 install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6
+pip3 install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA10.1 + TensorRT7
 ```
 
 You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to pip command) to speed up the download.
@@ -129,10 +129,10 @@ Recommended to install paddle >= 2.1.0
 
 ```
 # CPU users, please run
-pip install paddlepaddle==2.1.0
+pip3 install paddlepaddle==2.1.0
 
 # GPU Cuda10.2 please run
-pip install paddlepaddle-gpu==2.1.0 
+pip3 install paddlepaddle-gpu==2.1.0 
 ```
 
 **Note**: If your Cuda version is not 10.2, please do not execute the above commands directly, you need to refer to [Paddle official documentation-multi-version whl package list
@@ -141,7 +141,7 @@ pip install paddlepaddle-gpu==2.1.0
 Select the url link of the corresponding GPU environment and install it. For example, for Python3.6 users of Cuda 10.1, please select `cp36-cp36m` and
 The url corresponding to `cuda10.1-cudnn7-mkl-gcc8.2-avx-trt6.0.1.5`, copy it and run
 ```
-pip install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.0-gpu-cuda10.1-cudnn7-mkl-gcc8.2/paddlepaddle_gpu-2.1.0.post101-cp36-cp36m-linux_x86_64.whl
+pip3 install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.0-gpu-cuda10.1-cudnn7-mkl-gcc8.2/paddlepaddle_gpu-2.1.0.post101-cp36-cp36m-linux_x86_64.whl
 ```
 
 the default `paddlepaddle-gpu==2.1.0` is Cuda 10.2 with no TensorRT. If you want to install PaddlePaddle with TensorRT. please also check the documentation-multi-version whl package list and find key word `cuda10.2-cudnn8.0-trt7.1.3`. More info please check [Paddle Serving uses TensorRT](./doc/TENSOR_RT.md)
@@ -169,7 +169,7 @@ Paddle Serving provides HTTP and RPC based service for users to access
 
 A user can also start a RPC service with `paddle_serving_server.serve`. RPC service is usually faster than HTTP service, although a user needs to do some coding based on Paddle Serving's python client API. Note that we do not specify `--name` here. 
 ``` shell
-python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292
 ```
 <center>
 
@@ -209,7 +209,7 @@ Here, `client.predict` function has two arguments. `feed` is a `python dict` wit
 Users can also put the data format processing logic on the server side, so that they can directly use curl to access the service, refer to the following case whose path is `python/examples/fit_a_line`
 
 ```
-python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --name uci
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --name uci
 ```
 for client side,
 ```
@@ -225,22 +225,22 @@ Paddle Serving provides industry-leading multi-model tandem services, which stro
 
 we get two models
 ```
-python -m paddle_serving_app.package --get_model ocr_rec
+python3 -m paddle_serving_app.package --get_model ocr_rec
 tar -xzvf ocr_rec.tar.gz
-python -m paddle_serving_app.package --get_model ocr_det
+python3 -m paddle_serving_app.package --get_model ocr_det
 tar -xzvf ocr_det.tar.gz
 ```
 then we start server side, launch two models as one standalone web service
 ```
-python web_service.py
+python3 web_service.py
 ```
 http request
 ```
-python pipeline_http_client.py
+python3 pipeline_http_client.py
 ```
 grpc request
 ```
-python pipeline_rpc_client.py
+python3 pipeline_rpc_client.py
 ```
 output
 ```