Merge pull request #1345 from HexToString/grpc_update

增加GRPC直接请求和HTTP（PROTO）

cmake/external/brpc.cmake

@@ -39,11 +39,11 @@ INCLUDE_DIRECTORIES(${BRPC_INCLUDE_DIR})
 set(prefix_path "${THIRD_PARTY_PATH}/install/gflags|${THIRD_PARTY_PATH}/install/leveldb|${THIRD_PARTY_PATH}/install/snappy|${THIRD_PARTY_PATH}/install/gtest|${THIRD_PARTY_PATH}/install/protobuf|${THIRD_PARTY_PATH}/install/zlib|${THIRD_PARTY_PATH}/install/glog")
 
 if(WITH_LITE)
-    set(BRPC_REPO "https://github.com/zhangjun/incubator-brpc.git")
-    set(BRPC_TAG "master")
+    set(BRPC_REPO "https://github.com/apache/incubator-brpc")
+    set(BRPC_TAG "1.0.0-rc01")
 else()
-    set(BRPC_REPO "https://github.com/wangjiawei04/brpc")
-    set(BRPC_TAG "6d79e0b17f25107c35b705ea58d888083f59ff47")
+    set(BRPC_REPO "https://github.com/apache/incubator-brpc")
+    set(BRPC_TAG "1.0.0-rc01")
 endif()
 
 # If minimal .a is need, you can set  WITH_DEBUG_SYMBOLS=OFF

core/configure/CMakeLists.txt

@@ -33,7 +33,9 @@ if (WITH_PYTHON)
   add_custom_target(general_model_config_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
   add_dependencies(general_model_config_py_proto general_model_config_py_proto_init)
   
-
+  py_grpc_proto_compile(general_model_service_py_proto SRCS proto/general_model_service.proto)
+  add_custom_target(general_model_service_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
+  add_dependencies(general_model_service_py_proto general_model_service_py_proto_init)
   
   if (CLIENT)
     py_proto_compile(sdk_configure_py_proto SRCS proto/sdk_configure.proto)
@@ -50,7 +52,12 @@ if (WITH_PYTHON)
                     COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
                     COMMENT "Copy generated general_model_config proto file into directory paddle_serving_client/proto."
                     WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
-    
+
+    add_custom_command(TARGET general_model_service_py_proto POST_BUILD
+                    COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                    COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                    COMMENT "Copy generated general_model_service proto file into directory paddle_serving_client/proto."
+                    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
   endif()
   
@@ -77,6 +84,12 @@ if (WITH_PYTHON)
     		COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
     		COMMENT "Copy generated general_model_config proto file into directory paddle_serving_server/proto."
     		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
+    add_custom_command(TARGET general_model_service_py_proto POST_BUILD
+                    COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+                    COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+                    COMMENT "Copy generated general_model_service proto file into directory paddle_serving_server/proto."
+                    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
     
   endif()
 

core/configure/proto/general_model_service.proto

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+package baidu.paddle_serving.predictor.general_model;
+option java_multiple_files = true;
+
+message Tensor {
+  repeated string data = 1;
+  repeated int32 int_data = 2;
+  repeated int64 int64_data = 3;
+  repeated float float_data = 4;
+  optional int32 elem_type =
+      5; // 0 means int64, 1 means float32, 2 means int32, 3 means string
+  repeated int32 shape = 6;       // shape should include batch
+  repeated int32 lod = 7;         // only for fetch tensor currently
+  optional string name = 8;       // get from the Model prototxt
+  optional string alias_name = 9; // get from the Model prototxt
+};
+
+message Request {
+  repeated Tensor tensor = 1;
+  repeated string fetch_var_names = 2;
+  optional bool profile_server = 3 [ default = false ];
+  required uint64 log_id = 4 [ default = 0 ];
+};
+
+message Response {
+  repeated ModelOutput outputs = 1;
+  repeated int64 profile_time = 2;
+};
+
+message ModelOutput {
+  repeated Tensor tensor = 1;
+  optional string engine_name = 2;
+}
+
+service GeneralModelService {
+  rpc inference(Request) returns (Response) {}
+  rpc debug(Request) returns (Response) {}
+};

core/general-server/proto/general_model_service.proto

@@ -20,12 +20,12 @@ package baidu.paddle_serving.predictor.general_model;
 option cc_generic_services = true;
 
 message Tensor {
-  repeated bytes data = 1;
+  repeated string data = 1;
   repeated int32 int_data = 2;
   repeated int64 int64_data = 3;
   repeated float float_data = 4;
   optional int32 elem_type =
-      5; // 0 means int64, 1 means float32, 2 means int32, 3 means bytes(string)
+      5; // 0 means int64, 1 means float32, 2 means int32, 3 means string
   repeated int32 shape = 6;       // shape should include batch
   repeated int32 lod = 7;         // only for fetch tensor currently
   optional string name = 8;       // get from the Model prototxt

core/sdk-cpp/proto/general_model_service.proto

@@ -20,12 +20,12 @@ package baidu.paddle_serving.predictor.general_model;
 option cc_generic_services = true;
 
 message Tensor {
-  repeated bytes data = 1;
+  repeated string data = 1;
   repeated int32 int_data = 2;
   repeated int64 int64_data = 3;
   repeated float float_data = 4;
   optional int32 elem_type =
-      5; // 0 means int64, 1 means float32, 2 means int32, 3 means bytes(string)
+      5; // 0 means int64, 1 means float32, 2 means int32, 3 means string
   repeated int32 shape = 6;       // shape should include batch
   repeated int32 lod = 7;         // only for fetch tensor currently
   optional string name = 8;       // get from the Model prototxt

java/README_CN.md

@@ -2,7 +2,7 @@
 
 ([English](./README.md)|简体中文)
 
-### 开发环境
+## 开发环境
 
 为了方便用户使用java进行开发，我们提供了编译好的Serving工程放置在java镜像当中，获取镜像并进入开发环境的方式是
 
@@ -15,7 +15,7 @@ cd Serving/java
 
 Serving文件夹是镜像生成时的develop分支工程目录，需要git pull 到最新版本，或者git checkout 到想要的分支。
 
-### 安装客户端依赖
+## 安装客户端依赖
 
 由于依赖库数量庞大，因此镜像已经在生成时编译过一次，用户执行以下操作即可
 
@@ -27,7 +27,34 @@ mvn compile
 mvn install
 ```
 
-### 启动服务端(Pipeline方式)
+## 请求BRPC-Server
+
+###服务端启动
+
+以fit_a_line模型为例，服务端启动与常规BRPC-Server端启动命令一样。
+
+```
+cd ../../python/examples/fit_a_line
+sh get_data.sh
+python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393
+```
+
+###客户端预测
+客户端目前支持多种请求方式，目前支持HTTP（数据为JSON格式）、HTTP（数据为PROTO格式）、GRPC
+
+推荐您使用HTTP（数据为PROTO格式），此时数据体为PROTO格式，传输的数据量小，速度快，目前已经帮用户实现了HTTP/GRPC的数据体（JSON/PROTO）的封装函数,详见[Client.java](./src/main/java/io/paddle/serving/client/Client.java)
+```
+cd ../../../java/examples/target
+java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar PaddleServingClientExample http_proto <configPath>
+```
+**注意  <configPath>为客户端配置文件，一般是名为serving_client_conf.prototxt的文件。**
+
+更多示例详见[PaddleServingClientExample.java](./examples/src/main/java/PaddleServingClientExample.java)
+
+
+## 请求Pipeline-Server
+
+###服务端启动
 
 对于input data type = string类型，以IMDB model ensemble模型为例，服务端启动
 
@@ -71,7 +98,7 @@ java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar Pipeli
 
 ### 注意事项
 
-1.在示例中，所有非Pipeline模型都需要使用`--use_multilang`来启动GRPC多编程语言支持，以及端口号都是9393，如果需要别的端口，需要在java文件里修改。
+1.在示例中，端口号都是9393，ip默认设置为了0.0.0.0表示本机，注意ip和port需要与Server端对应。
 
 2.目前Serving已推出Pipeline模式（原理详见[Pipeline Serving](../doc/PIPELINE_SERVING_CN.md)），面向Java的Pipeline Serving Client已发布。
 
@@ -84,5 +111,3 @@ java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar Pipeli
 第一种是GPU Serving和Java Client在运行在同一个GPU镜像中，需要用户在启动GPU镜像后，把在java镜像中编译完成后的文件(位于/Serving/java目录下)拷贝到GPU镜像中的/Serving/java目录下。
 
 第二种是GPU Serving和Java Client分别在各自的docker镜像中(或具备编译开发环境的不同主机上)部署，此时仅需注意Java Client端与GPU Serving端的ip和port需要对应，详见上述注意事项中的第3项。
-
-

java/examples/src/main/java/PaddleServingClientExample.java

@@ -11,7 +11,7 @@ import org.nd4j.linalg.factory.Nd4j;
 import java.util.*;
 
 public class PaddleServingClientExample {
-    boolean fit_a_line(String model_config_path) {
+    boolean http_proto(String model_config_path) {
         float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
             0.0582f, -0.0727f, -0.1583f, -0.0584f,
             0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
@@ -24,7 +24,7 @@ public class PaddleServingClientExample {
             }};
         List<String> fetch = Arrays.asList("price");
         
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -34,6 +34,55 @@ public class PaddleServingClientExample {
         return true;
     }
 
+    boolean http_json(String model_config_path) {
+        float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
+            0.0582f, -0.0727f, -0.1583f, -0.0584f,
+            0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
+        INDArray npdata = Nd4j.createFromArray(data);
+        long[] batch_shape = {1,13};
+        INDArray batch_npdata = npdata.reshape(batch_shape);
+        HashMap<String, Object> feed_data
+            = new HashMap<String, Object>() {{
+                put("x", batch_npdata);
+            }};
+        List<String> fetch = Arrays.asList("price");
+        
+        Client client = new Client();
+        //注意：跨docker，需要设置--net-host或直接访问另一个docker的ip
+        client.setIP("0.0.0.0");
+        client.setPort("9393");
+        client.set_http_proto(false);
+        client.loadClientConfig(model_config_path);
+        String result = client.predict(feed_data, fetch, true, 0);
+        
+        System.out.println(result);
+        return true;
+    }
+
+    boolean grpc(String model_config_path) {
+        float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
+            0.0582f, -0.0727f, -0.1583f, -0.0584f,
+            0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
+        INDArray npdata = Nd4j.createFromArray(data);
+        long[] batch_shape = {1,13};
+        INDArray batch_npdata = npdata.reshape(batch_shape);
+        HashMap<String, Object> feed_data
+            = new HashMap<String, Object>() {{
+                put("x", batch_npdata);
+            }};
+        List<String> fetch = Arrays.asList("price");
+        
+        Client client = new Client();
+        client.setIP("0.0.0.0");
+        client.setPort("9393");
+        client.loadClientConfig(model_config_path);
+        client.set_use_grpc_client(true);
+        String result = client.predict(feed_data, fetch, true, 0);
+        
+        System.out.println(result);
+        return true;
+    }
+
     boolean encrypt(String model_config_path,String keyFilePath) {
         float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
             0.0582f, -0.0727f, -0.1583f, -0.0584f,
@@ -47,7 +96,7 @@ public class PaddleServingClientExample {
             }};
         List<String> fetch = Arrays.asList("price");
         
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -55,7 +104,6 @@ public class PaddleServingClientExample {
         try {
             Thread.sleep(1000*3);   // 休眠3秒，等待Server启动
         } catch (Exception e) {
-            //TODO: handle exception
         }
         String result = client.predict(feed_data, fetch, true, 0);
         
@@ -76,7 +124,7 @@ public class PaddleServingClientExample {
             }};
         List<String> fetch = Arrays.asList("price");
         
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -127,7 +175,7 @@ public class PaddleServingClientExample {
                 put("im_size", batch_im_size);
             }};
         List<String> fetch = Arrays.asList("save_infer_model/scale_0.tmp_0");
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -149,7 +197,7 @@ public class PaddleServingClientExample {
                 put("segment_ids", Nd4j.createFromArray(segment_ids));
             }};
         List<String> fetch = Arrays.asList("pooled_output");
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -219,7 +267,7 @@ public class PaddleServingClientExample {
                 put("embedding_0.tmp_0", Nd4j.createFromArray(embedding_0));
             }};
         List<String> fetch = Arrays.asList("prob");
-        HttpClient client = new HttpClient();
+        Client client = new Client();
         client.setIP("0.0.0.0");
         client.setPort("9393");
         client.loadClientConfig(model_config_path);
@@ -236,13 +284,17 @@ public class PaddleServingClientExample {
         
         if (args.length < 2) {
             System.out.println("Usage: java -cp <jar> PaddleServingClientExample <test-type> <configPath>.");
-            System.out.println("<test-type>: fit_a_line bert cube_local yolov4 encrypt");
+            System.out.println("<test-type>: http_proto http_json grpc bert cube_local yolov4 encrypt");
             return;
         }
         String testType = args[0];
         System.out.format("[Example] %s\n", testType);
-        if ("fit_a_line".equals(testType)) {
-            succ = e.fit_a_line(args[1]);
+        if ("http_proto".equals(testType)) {
+            succ = e.http_proto(args[1]);
+        } else if ("http_json".equals(testType)) {
+            succ = e.http_json(args[1]);
+        } else if ("grpc".equals(testType)) {
+            succ = e.grpc(args[1]);
         } else if ("compress".equals(testType)) {
             succ = e.compress(args[1]);
         } else if ("bert".equals(testType)) {

java/src/main/proto/general_model_service.proto

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+package baidu.paddle_serving.predictor.general_model;
+option java_multiple_files = true;
+
+message Tensor {
+  repeated string data = 1;
+  repeated int32 int_data = 2;
+  repeated int64 int64_data = 3;
+  repeated float float_data = 4;
+  optional int32 elem_type =
+      5; // 0 means int64, 1 means float32, 2 means int32, 3 means string
+  repeated int32 shape = 6;       // shape should include batch
+  repeated int32 lod = 7;         // only for fetch tensor currently
+  optional string name = 8;       // get from the Model prototxt
+  optional string alias_name = 9; // get from the Model prototxt
+};
+
+message Request {
+  repeated Tensor tensor = 1;
+  repeated string fetch_var_names = 2;
+  optional bool profile_server = 3 [ default = false ];
+  required uint64 log_id = 4 [ default = 0 ];
+};
+
+message Response {
+  repeated ModelOutput outputs = 1;
+  repeated int64 profile_time = 2;
+};
+
+message ModelOutput {
+  repeated Tensor tensor = 1;
+  optional string engine_name = 2;
+}
+
+service GeneralModelService {
+  rpc inference(Request) returns (Response) {}
+  rpc debug(Request) returns (Response) {}
+};

python/examples/fit_a_line/test_httpclient.py

@@ -13,24 +13,41 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 
-from paddle_serving_client.httpclient import HttpClient
+from paddle_serving_client.httpclient import GeneralClient
 import sys
 import numpy as np
 import time
 
-client = HttpClient()
+client = GeneralClient()
 client.load_client_config(sys.argv[1])
-# if you want to enable Encrypt Module,uncommenting the following line
-# client.use_key("./key")
-client.set_response_compress(True)
-client.set_request_compress(True)
-fetch_list = client.get_fetch_names()
+''' 
+if you want use GRPC-client, set_use_grpc_client(True)
+or you can directly use client.grpc_client_predict(...)
+as for HTTP-client,set_use_grpc_client(False)(which is default)
+or you can directly use client.http_client_predict(...)
+'''
+#client.set_use_grpc_client(True)
+'''
+if you want to enable Encrypt Module,uncommenting the following line
+'''
+#client.use_key("./key")
+'''
+if you want to compress,uncommenting the following line
+'''
+#client.set_response_compress(True)
+#client.set_request_compress(True)
+'''
+we recommend use Proto data format in HTTP-body, set True(which is default)
+if you want use JSON data format in HTTP-body, set False
+'''
+#client.set_http_proto(True)
+
 import paddle
 test_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.test(), buf_size=500),
     batch_size=1)
-
+fetch_list = client.get_fetch_names()
 for data in test_reader():
     new_data = np.zeros((1, 13)).astype("float32")
     new_data[0] = data[0][0]

python/paddle_serving_client/client.py

@@ -345,68 +345,69 @@ class Client(object):
             raise ValueError(
                 "Fetch names should not be empty or out of saved fetch list.")
 
-        feed_i = feed_batch[0]
-        for key in feed_i:
+        feed_dict = feed_batch[0]
+        for key in feed_dict:
             if ".lod" not in key and key not in self.feed_names_:
                 raise ValueError("Wrong feed name: {}.".format(key))
             if ".lod" in key:
                 continue
 
-            self.shape_check(feed_i, key)
+            self.shape_check(feed_dict, key)
             if self.feed_types_[key] in int_type:
                 int_feed_names.append(key)
                 shape_lst = []
                 if batch == False:
-                    feed_i[key] = np.expand_dims(feed_i[key], 0).repeat(
+                    feed_dict[key] = np.expand_dims(feed_dict[key], 0).repeat(
                         1, axis=0)
-                if isinstance(feed_i[key], np.ndarray):
-                    shape_lst.extend(list(feed_i[key].shape))
+                if isinstance(feed_dict[key], np.ndarray):
+                    shape_lst.extend(list(feed_dict[key].shape))
                     int_shape.append(shape_lst)
                 else:
                     int_shape.append(self.feed_shapes_[key])
-                if "{}.lod".format(key) in feed_i:
-                    int_lod_slot_batch.append(feed_i["{}.lod".format(key)])
+                if "{}.lod".format(key) in feed_dict:
+                    int_lod_slot_batch.append(feed_dict["{}.lod".format(key)])
                 else:
                     int_lod_slot_batch.append([])
 
-                if isinstance(feed_i[key], np.ndarray):
-                    int_slot.append(np.ascontiguousarray(feed_i[key]))
+                if isinstance(feed_dict[key], np.ndarray):
+                    int_slot.append(np.ascontiguousarray(feed_dict[key]))
                     self.has_numpy_input = True
                 else:
-                    int_slot.append(np.ascontiguousarray(feed_i[key]))
+                    int_slot.append(np.ascontiguousarray(feed_dict[key]))
                     self.all_numpy_input = False
 
             elif self.feed_types_[key] in float_type:
                 float_feed_names.append(key)
                 shape_lst = []
                 if batch == False:
-                    feed_i[key] = np.expand_dims(feed_i[key], 0).repeat(
+                    feed_dict[key] = np.expand_dims(feed_dict[key], 0).repeat(
                         1, axis=0)
-                if isinstance(feed_i[key], np.ndarray):
-                    shape_lst.extend(list(feed_i[key].shape))
+                if isinstance(feed_dict[key], np.ndarray):
+                    shape_lst.extend(list(feed_dict[key].shape))
                     float_shape.append(shape_lst)
                 else:
                     float_shape.append(self.feed_shapes_[key])
-                if "{}.lod".format(key) in feed_i:
-                    float_lod_slot_batch.append(feed_i["{}.lod".format(key)])
+                if "{}.lod".format(key) in feed_dict:
+                    float_lod_slot_batch.append(feed_dict["{}.lod".format(key)])
                 else:
                     float_lod_slot_batch.append([])
 
-                if isinstance(feed_i[key], np.ndarray):
-                    float_slot.append(np.ascontiguousarray(feed_i[key]))
+                if isinstance(feed_dict[key], np.ndarray):
+                    float_slot.append(np.ascontiguousarray(feed_dict[key]))
                     self.has_numpy_input = True
                 else:
-                    float_slot.append(np.ascontiguousarray(feed_i[key]))
+                    float_slot.append(np.ascontiguousarray(feed_dict[key]))
                     self.all_numpy_input = False
             #if input is string, feed is not numpy.
             elif self.feed_types_[key] in string_type:
                 string_feed_names.append(key)
                 string_shape.append(self.feed_shapes_[key])
-                if "{}.lod".format(key) in feed_i:
-                    string_lod_slot_batch.append(feed_i["{}.lod".format(key)])
+                if "{}.lod".format(key) in feed_dict:
+                    string_lod_slot_batch.append(feed_dict["{}.lod".format(
+                        key)])
                 else:
                     string_lod_slot_batch.append([])
-                string_slot.append(feed_i[key])
+                string_slot.append(feed_dict[key])
                 self.has_numpy_input = True
 
         self.profile_.record('py_prepro_1')

python/paddle_serving_server/serve.py

@@ -107,9 +107,13 @@ def is_gpu_mode(unformatted_gpus):
 def serve_args():
     parser = argparse.ArgumentParser("serve")
     parser.add_argument(
-        "--thread", type=int, default=2, help="Concurrency of server")
+        "--thread",
+        type=int,
+        default=4,
+        help="Concurrency of server,[4,1024]",
+        choices=range(4, 1025))
     parser.add_argument(
-        "--port", type=int, default=9292, help="Port of the starting gpu")
+        "--port", type=int, default=9393, help="Port of the starting gpu")
     parser.add_argument(
         "--device", type=str, default="cpu", help="Type of device")
     parser.add_argument(

python/paddle_serving_server/web_service.py

@@ -123,7 +123,7 @@ class WebService(object):
                             workdir,
                             port=9292,
                             gpus=None,
-                            thread_num=2,
+                            thread_num=4,
                             mem_optim=True,
                             use_lite=False,
                             use_xpu=False,
@@ -236,7 +236,7 @@ class WebService(object):
                        use_lite=False,
                        use_xpu=False,
                        ir_optim=False,
-                       thread_num=2,
+                       thread_num=4,
                        mem_optim=True,
                        use_trt=False,
                        gpu_multi_stream=False,

tools/cpp_examples/demo-serving/proto/general_model_service.proto

@@ -20,12 +20,12 @@ package baidu.paddle_serving.predictor.general_model;
 option cc_generic_services = true;
 
 message Tensor {
-  repeated bytes data = 1;
+  repeated string data = 1;
   repeated int32 int_data = 2;
   repeated int64 int64_data = 3;
   repeated float float_data = 4;
   optional int32 elem_type =
-      5; // 0 means int64, 1 means float32, 2 means int32, 3 means bytes(string)
+      5; // 0 means int64, 1 means float32, 2 means int32, 3 means string
   repeated int32 shape = 6;       // shape should include batch
   repeated int32 lod = 7;         // only for fetch tensor currently
   optional string name = 8;       // get from the Model prototxt