add http_proto and grpcclient

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_REPO "https://github.com/apache/incubator-brpc")
     set(BRPC_TAG "master")
 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 "master")
 endif()
 
 # If minimal .a is need, you can set  WITH_DEBUG_SYMBOLS=OFF

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/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 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

@@ -13,34 +13,45 @@
 # 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 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")
+#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)
-fetch_list = client.get_fetch_names()
+
 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]
-    fetch_map = client.grpc_client_predict(
+    fetch_map = client.predict(
         feed={"x": new_data}, fetch=fetch_list, batch=True)
     print(fetch_map)
     break

python/paddle_serving_client/httpclient.py

@@ -66,7 +66,14 @@ def data_bytes_number(datalist):
     return total_bytes_number
 
 
-class HttpClient(object):
+# 此文件名，暂时为httpclient.py，待后续测试后考虑是否替换client.py
+# 默认使用http方式，默认使用Proto in HTTP-body
+# 如果想使用JSON in HTTP-body, set_http_proto(False)
+# Predict()是包装类http_client_predict/grpc_client_predict
+# 可以直接调用需要的http_client_predict/grpc_client_predict
+# 例如，如果想使用GRPC方式，set_use_grpc_client(True)
+# 或者直接调用grpc_client_predict()
+class GeneralClient(object):
     def __init__(self,
                  ip="0.0.0.0",
                  port="9393",
@@ -77,7 +84,7 @@ class HttpClient(object):
         self.feed_shapes_ = {}
         self.feed_types_ = {}
         self.feed_names_to_idx_ = {}
-        self.http_timeout_ms = 200000
+        self.timeout_ms = 200000
         self.ip = ip
         self.port = port
         self.server_port = port
@@ -86,7 +93,9 @@ class HttpClient(object):
         self.try_request_gzip = False
         self.try_response_gzip = False
         self.total_data_number = 0
-        self.http_proto = False
+        self.http_proto = True
+        self.max_body_size = 512 * 1024 * 1024
+        self.use_grpc_client = False
 
     def load_client_config(self, model_config_path_list):
         if isinstance(model_config_path_list, str):
@@ -144,11 +153,14 @@ class HttpClient(object):
                 self.lod_tensor_set.add(var.alias_name)
         return
 
-    def set_http_timeout_ms(self, http_timeout_ms):
-        if not isinstance(http_timeout_ms, int):
-            raise ValueError("http_timeout_ms must be int type.")
+    def set_max_body_size(self, max_body_size):
+        self.max_body_size = max_body_size
+
+    def set_timeout_ms(self, timeout_ms):
+        if not isinstance(timeout_ms, int):
+            raise ValueError("timeout_ms must be int type.")
         else:
-            self.http_timeout_ms = http_timeout_ms
+            self.timeout_ms = timeout_ms
 
     def set_ip(self, ip):
         self.ip = ip
@@ -168,6 +180,9 @@ class HttpClient(object):
     def set_http_proto(self, http_proto):
         self.http_proto = http_proto
 
+    def set_use_grpc_client(self, use_grpc_client):
+        self.use_grpc_client = use_grpc_client
+
     # use_key is the function of encryption.
     def use_key(self, key_filename):
         with open(key_filename, "rb") as f:
@@ -195,50 +210,6 @@ class HttpClient(object):
     def get_fetch_names(self):
         return self.fetch_names_
 
-    # feed 支持Numpy类型，以及直接List、tuple
-    # 不支持str类型，因为proto中为repeated.
-    def predict(self,
-                feed=None,
-                fetch=None,
-                batch=False,
-                need_variant_tag=False,
-                log_id=0):
-
-        feed_dict = self.get_feedvar_dict(feed)
-        fetch_list = self.get_legal_fetch(fetch)
-        headers = {}
-        postData = ''
-
-        if self.http_proto == True:
-            postData = self.process_proto_data(feed_dict, fetch_list, batch,
-                                               log_id).SerializeToString()
-            headers["Content-Type"] = "application/proto"
-        else:
-            postData = self.process_json_data(feed_dict, fetch_list, batch,
-                                              log_id)
-            headers["Content-Type"] = "application/json"
-
-        web_url = "http://" + self.ip + ":" + self.server_port + self.service_name
-        # 当数据区长度大于512字节时才压缩.
-        if self.try_request_gzip and self.total_data_number > 512:
-            postData = gzip.compress(bytes(postData, 'utf-8'))
-            headers["Content-Encoding"] = "gzip"
-        if self.try_response_gzip:
-            headers["Accept-encoding"] = "gzip"
-
-        # requests支持自动识别解压
-        result = requests.post(url=web_url, headers=headers, data=postData)
-        if result == None:
-            return None
-        if result.status_code == 200:
-            if result.headers["Content-Type"] == 'application/proto':
-                response = general_model_service_pb2.Response()
-                response.ParseFromString(result.content)
-                return response
-            else:
-                return result.json()
-        return result
-
     def get_legal_fetch(self, fetch):
         if fetch is None:
             raise ValueError("You should specify feed and fetch for prediction")
@@ -265,29 +236,32 @@ class HttpClient(object):
     def get_feedvar_dict(self, feed):
         if feed is None:
             raise ValueError("You should specify feed and fetch for prediction")
-
-        feed_batch = []
+        feed_dict = {}
         if isinstance(feed, dict):
-            feed_batch.append(feed)
+            feed_dict = feed
         elif isinstance(feed, (list, str, tuple)):
             # if input is a list or str or tuple, and the number of feed_var is 1.
-            # create a temp_dict { key = feed_var_name, value = list}
-            # put the temp_dict into the feed_batch.
-            if len(self.feed_names_) != 1:
-                raise ValueError(
-                    "input is a list, but we got 0 or 2+ feed_var, don`t know how to divide the feed list"
-                )
-            temp_dict = {}
-            temp_dict[self.feed_names_[0]] = feed
-            feed_batch.append(temp_dict)
-        else:
-            raise ValueError("Feed only accepts dict and list of dict")
+            # create a feed_dict { key = feed_var_name, value = list}
+            if len(self.feed_names_) == 1:
+                feed_dict[self.feed_names_[0]] = feed
+            elif len(self.feed_names_) > 1:
+                if isinstance(feed, str):
+                    raise ValueError(
+                        "input is a str, but we got 2+ feed_var, don`t know how to divide the string"
+                    )
+                # feed is a list or tuple
+                elif len(self.feed_names_) == len(feed):
+                    for index in range(len(feed)):
+                        feed_dict[self.feed_names_[index]] = feed[index]
+                else:
+                    raise ValueError("len(feed) ≠ len(feed_var), error")
+            else:
+                raise ValueError("we got feed, but feed_var is None")
 
-        # batch_size must be 1, cause batch is already in Tensor.
-        if len(feed_batch) != 1:
-            raise ValueError("len of feed_batch can only be 1.")
+        else:
+            raise ValueError("Feed only accepts dict/str/list/tuple")
 
-        return feed_batch[0]
+        return feed_dict
 
     def process_json_data(self, feed_dict, fetch_list, batch, log_id):
         Request = {}
@@ -429,6 +403,64 @@ class HttpClient(object):
             tensor_dict["lod"] = lod
         return tensor_dict
 
+    # feed结构必须为dict、List、tuple、string
+    # feed中数据支持Numpy、list、tuple、以及基本类型
+    # fetch默认是从模型的配置文件中获取全部的fetch_var
+    def predict(self,
+                feed=None,
+                fetch=None,
+                batch=False,
+                need_variant_tag=False,
+                log_id=0):
+        if self.use_grpc_client:
+            return self.grpc_client_predict(feed, fetch, batch,
+                                            need_variant_tag, log_id)
+        else:
+            return self.http_client_predict(feed, fetch, batch,
+                                            need_variant_tag, log_id)
+
+    def http_client_predict(self,
+                            feed=None,
+                            fetch=None,
+                            batch=False,
+                            need_variant_tag=False,
+                            log_id=0):
+
+        feed_dict = self.get_feedvar_dict(feed)
+        fetch_list = self.get_legal_fetch(fetch)
+        headers = {}
+        postData = ''
+
+        if self.http_proto == True:
+            postData = self.process_proto_data(feed_dict, fetch_list, batch,
+                                               log_id).SerializeToString()
+            headers["Content-Type"] = "application/proto"
+        else:
+            postData = self.process_json_data(feed_dict, fetch_list, batch,
+                                              log_id)
+            headers["Content-Type"] = "application/json"
+
+        web_url = "http://" + self.ip + ":" + self.server_port + self.service_name
+        # 当数据区长度大于512字节时才压缩.
+        if self.try_request_gzip and self.total_data_number > 512:
+            postData = gzip.compress(bytes(postData, 'utf-8'))
+            headers["Content-Encoding"] = "gzip"
+        if self.try_response_gzip:
+            headers["Accept-encoding"] = "gzip"
+
+        # requests支持自动识别解压
+        result = requests.post(url=web_url, headers=headers, data=postData)
+        if result == None:
+            return None
+        if result.status_code == 200:
+            if result.headers["Content-Type"] == 'application/proto':
+                response = general_model_service_pb2.Response()
+                response.ParseFromString(result.content)
+                return response
+            else:
+                return result.json()
+        return result
+
     def grpc_client_predict(self,
                             feed=None,
                             fetch=None,
@@ -440,19 +472,17 @@ class HttpClient(object):
         fetch_list = self.get_legal_fetch(fetch)
 
         postData = self.process_proto_data(feed_dict, fetch_list, batch, log_id)
-        print('proto data', postData)
-        '''
+
         # https://github.com/tensorflow/serving/issues/1382
-        options = [('grpc.max_receive_message_length', 512 * 1024 * 1024),
-                   ('grpc.max_send_message_length', 512 * 1024 * 1024),
-                   ('grpc.lb_policy_name', 'round_robin')]
-        '''
+        options = [('grpc.max_receive_message_length', self.max_body_size),
+                   ('grpc.max_send_message_length', self.max_body_size)]
+
         endpoints = [self.ip + ":" + self.server_port]
         g_endpoint = 'ipv4:{}'.format(','.join(endpoints))
         print("my endpoint is ", g_endpoint)
         self.channel_ = grpc.insecure_channel(g_endpoint, options=options)
         self.stub_ = general_model_service_pb2_grpc.GeneralModelServiceStub(
             self.channel_)
-        resp = self.stub_.inference(postData, timeout=self.http_timeout_ms)
+        resp = self.stub_.inference(postData, timeout=self.timeout_ms)
 
         return resp

python/paddle_serving_server/serve.py

@@ -108,9 +108,13 @@ 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(
@@ -180,8 +184,6 @@ def serve_args():
         default=False,
         action="store_true",
         help="Use gpu_multi_stream")
-    parser.add_argument(
-        "--grpc", default=False, action="store_true", help="Use grpc test")
     return parser.parse_args()
 
 
@@ -386,33 +388,4 @@ if __name__ == "__main__":
         )
         server.serve_forever()
     else:
-        # this is for grpc Test
-        if args.grpc:
-            from .proto import general_model_service_pb2
-            sys.path.append(
-                os.path.join(
-                    os.path.abspath(os.path.dirname(__file__)), 'proto'))
-            from .proto import general_model_service_pb2_grpc
-            import google.protobuf.text_format
-            from concurrent import futures
-            import grpc
-
-            _ONE_DAY_IN_SECONDS = 60 * 60 * 24
-
-            class GeneralModelService(
-                    general_model_service_pb2_grpc.GeneralModelServiceServicer):
-                def inference(self, request, context):
-                    return general_model_service_pb2.Response()
-
-            server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
-            general_model_service_pb2_grpc.add_GeneralModelServiceServicer_to_server(
-                GeneralModelService(), server)
-            server.add_insecure_port('[::]:9393')
-            server.start()
-            try:
-                while True:
-                    time.sleep(_ONE_DAY_IN_SECONDS)
-            except KeyboardInterrupt:
-                server.stop(0)
-        else:
-            start_multi_card(args)
+        start_multi_card(args)

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