[doc] replase paddle_serving_server_gpu with paddle_serving_server

doc/BAIDU_KUNLUN_XPU_SERVING.md

@@ -80,15 +80,15 @@ The first two deployment methods are recommended。
 
 Start the rpc service, deploying on ARM server with Baidu Kunlun chips，and accelerate with Paddle-Lite and Baidu Kunlun xpu.
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --use_xpu --ir_optim
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --use_xpu --ir_optim
 ```
 Start the rpc service, deploying on ARM server，and accelerate with Paddle-Lite.
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --ir_optim
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --ir_optim
 ```
 Start the rpc service, deploying on ARM server.
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292
 ```
 ## 
 ```

doc/BAIDU_KUNLUN_XPU_SERVING_CN.md

@@ -76,15 +76,15 @@ tar -xzf uci_housing.tar.gz
 
 启动rpc服务，使用arm cpu+xpu部署，使用Paddle-Lite xpu优化加速能力
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --use_xpu --ir_optim
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --use_xpu --ir_optim
 ```
 启动rpc服务，使用arm cpu部署, 使用Paddle-Lite加速能力
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --ir_optim
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292 --use_lite --ir_optim
 ```
 启动rpc服务，使用arm cpu部署, 不使用Paddle-Lite加速能力
 ```
-python3 -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 6 --port 9292
+python3 -m paddle_serving_server.serve --model uci_housing_model --thread 6 --port 9292
 ```
 ## client调用
 ```

doc/BERT_10_MINS.md

@@ -52,7 +52,7 @@ python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292  #c
 ```
 Or,start gpu inference service,Run
 ```
-python -m paddle_serving_server_gpu.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0
+python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0
 ```
 | Parameters | Meaning                                  |
 | ---------- | ---------------------------------------- |

doc/BERT_10_MINS_CN.md

@@ -50,7 +50,7 @@ python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292  #
 ```
 或者，启动gpu预测服务，执行
 ```
-python -m paddle_serving_server_gpu.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #在gpu 0上启动gpu预测服务
+python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #在gpu 0上启动gpu预测服务
 
 ```
 

doc/ENCRYPTION.md

@@ -25,7 +25,7 @@ python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_
 ```
 GPU Service
 ```
-python -m paddle_serving_server_gpu.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
+python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
 ```
 
 At this point, the server does not really start, but waits for the key。

doc/ENCRYPTION_CN.md

@@ -25,7 +25,7 @@ python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_
 ```
 GPU Service
 ```
-python -m paddle_serving_server_gpu.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
+python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
 ```
 
 此时，服务器不会真正启动，而是等待密钥。

doc/MULTI_SERVICE_ON_ONE_GPU_CN.md

@@ -5,8 +5,8 @@
 例如：
 
 ```shell
-python -m paddle_serving_server_gpu.serve --model bert_seq128_model --port 9292 --gpu_ids 0
-python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0
+python -m paddle_serving_server.serve --model bert_seq128_model --port 9292 --gpu_ids 0
+python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0
 ```
 
 在卡0上，同时部署了bert示例和iamgenet示例。

doc/SAVE.md

@@ -38,7 +38,7 @@ We can see that the `serving_server` and `serving_client` folders hold the serve
 Start the server (GPU)
 
 ```
-python -m paddle_serving_server_gpu.serve --model serving_server --port 9393 --gpu_id 0
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_id 0
 ```
 
 Client (`test_client.py`)

doc/SAVE_CN.md

@@ -37,7 +37,7 @@ python -m paddle_serving_client.convert --dirname . --model_filename dygraph_mod
 
 启动服务端（GPU）
 ```
-python -m paddle_serving_server_gpu.serve --model serving_server --port 9393 --gpu_id 0
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_id 0
 ```
 
 客户端写法，保存为`test_client.py`

doc/TENSOR_RT.md

@@ -50,7 +50,7 @@ We just need
 ```
 wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_r50_fpn_1x_coco.tar
 tar xf faster_rcnn_r50_fpn_1x_coco.tar
-python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0 --use_trt
+python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0 --use_trt
 ```
 The TensorRT version of the faster_rcnn model server is started
 

doc/TENSOR_RT_CN.md

@@ -50,7 +50,7 @@ pip install paddle-server-server==${VERSION}.post11
 ```
 wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_r50_fpn_1x_coco.tar
 tar xf faster_rcnn_r50_fpn_1x_coco.tar
-python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0 --use_trt
+python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0 --use_trt
 ```
 TensorRT版本的faster_rcnn模型服务端就启动了
 

doc/WINDOWS_TUTORIAL.md

@@ -54,7 +54,7 @@ Currently Windows supports the Local Predictor of the Web Service framework. The
 ```
 # filename:your_webservice.py
 from paddle_serving_server.web_service import WebService
-# If it is the GPU version, please use from paddle_serving_server_gpu.web_service import WebService
+# If it is the GPU version, please use from paddle_serving_server.web_service import WebService
 class YourWebService(WebService):
     def preprocess(self, feed=[], fetch=[]):
         #Implement pre-processing here

doc/WINDOWS_TUTORIAL_CN.md

@@ -54,7 +54,7 @@ python ocr_web_client.py
 ```
 # filename:your_webservice.py
 from paddle_serving_server.web_service import WebService
-# 如果是GPU版本，请使用 from paddle_serving_server_gpu.web_service import WebService
+# 如果是GPU版本，请使用 from paddle_serving_server.web_service import WebService
 class YourWebService(WebService):
     def preprocess(self, feed=[], fetch=[]):
         #在这里实现前处理