fix conflict

core/general-client/include/general_model.h

@@ -45,12 +45,12 @@ class PredictorRes {
   ~PredictorRes() {}
 
  public:
-  const std::vector<std::vector<int64_t>> & get_int64_by_name(
-      const std::string & name) {
+  const std::vector<std::vector<int64_t>>& get_int64_by_name(
+      const std::string& name) {
     return _int64_map[name];
   }
-  const std::vector<std::vector<float>> & get_float_by_name(
-      const std::string & name) {
+  const std::vector<std::vector<float>>& get_float_by_name(
+      const std::string& name) {
     return _float_map[name];
   }
 
@@ -71,7 +71,7 @@ class PredictorClient {
   void set_predictor_conf(const std::string& conf_path,
                           const std::string& conf_file);
 
-  int create_predictor_by_desc(const std::string & sdk_desc);
+  int create_predictor_by_desc(const std::string& sdk_desc);
 
   int create_predictor();
   int destroy_predictor();
@@ -81,7 +81,8 @@ class PredictorClient {
               const std::vector<std::vector<int64_t>>& int_feed,
               const std::vector<std::string>& int_feed_name,
               const std::vector<std::string>& fetch_name,
-              PredictorRes & predict_res); // NOLINT
+              PredictorRes& predict_res,  // NOLINT
+              const int& pid);
 
   std::vector<std::vector<float>> predict(
       const std::vector<std::vector<float>>& float_feed,

core/general-client/src/general_model.cpp

@@ -132,13 +132,13 @@ int PredictorClient::create_predictor() {
   _api.thrd_initialize();
 }
 
-int PredictorClient::predict(
-    const std::vector<std::vector<float>>& float_feed,
-    const std::vector<std::string>& float_feed_name,
-    const std::vector<std::vector<int64_t>>& int_feed,
-    const std::vector<std::string>& int_feed_name,
-    const std::vector<std::string>& fetch_name,
-    PredictorRes & predict_res) { // NOLINT
+int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
+                             const std::vector<std::string> &float_feed_name,
+                             const std::vector<std::vector<int64_t>> &int_feed,
+                             const std::vector<std::string> &int_feed_name,
+                             const std::vector<std::string> &fetch_name,
+                             PredictorRes &predict_res,
+                             const int &pid) {  // NOLINT
   predict_res._int64_map.clear();
   predict_res._float_map.clear();
   Timer timeline;
@@ -218,6 +218,7 @@ int PredictorClient::predict(
       VLOG(2) << "fetch name: " << name;
       if (_fetch_name_to_type[name] == 0) {
         int len = res.insts(0).tensor_array(idx).int64_data_size();
+        VLOG(2) << "fetch tensor : " << name << " type: int64 len : " << len;
         predict_res._int64_map[name].resize(1);
         predict_res._int64_map[name][0].resize(len);
         for (int i = 0; i < len; ++i) {
@@ -226,6 +227,7 @@ int PredictorClient::predict(
         }
       } else if (_fetch_name_to_type[name] == 1) {
         int len = res.insts(0).tensor_array(idx).float_data_size();
+        VLOG(2) << "fetch tensor : " << name << " type: float32 len : " << len;
         predict_res._float_map[name].resize(1);
         predict_res._float_map[name][0].resize(len);
         for (int i = 0; i < len; ++i) {
@@ -240,6 +242,7 @@ int PredictorClient::predict(
   if (FLAGS_profile_client) {
     std::ostringstream oss;
     oss << "PROFILE\t"
+        << "pid:" << pid << "\t"
         << "prepro_0:" << preprocess_start << " "
         << "prepro_1:" << preprocess_end << " "
         << "client_infer_0:" << client_infer_start << " "
@@ -342,7 +345,7 @@ std::vector<std::vector<std::vector<float>>> PredictorClient::batch_predict(
     }
 
     VLOG(2) << "batch [" << bi << "] "
-            << "itn feed value prepared";
+            << "int feed value prepared";
   }
 
   int64_t preprocess_end = timeline.TimeStampUS();

core/general-client/src/pybind_general_model.cpp

@@ -31,13 +31,15 @@ PYBIND11_MODULE(serving_client, m) {
   py::class_<PredictorRes>(m, "PredictorRes", py::buffer_protocol())
       .def(py::init())
       .def("get_int64_by_name",
-           [](PredictorRes &self, std::string & name) {
+           [](PredictorRes &self, std::string &name) {
              return self.get_int64_by_name(name);
-           }, py::return_value_policy::reference)
+           },
+           py::return_value_policy::reference)
       .def("get_float_by_name",
-           [](PredictorRes &self, std::string & name) {
+           [](PredictorRes &self, std::string &name) {
              return self.get_float_by_name(name);
-           }, py::return_value_policy::reference);
+           },
+           py::return_value_policy::reference);
 
   py::class_<PredictorClient>(m, "PredictorClient", py::buffer_protocol())
       .def(py::init())
@@ -56,8 +58,9 @@ PYBIND11_MODULE(serving_client, m) {
              self.set_predictor_conf(conf_path, conf_file);
            })
       .def("create_predictor_by_desc",
-           [](PredictorClient &self, const std::string & sdk_desc) {
-             self.create_predictor_by_desc(sdk_desc); })
+           [](PredictorClient &self, const std::string &sdk_desc) {
+             self.create_predictor_by_desc(sdk_desc);
+           })
       .def("create_predictor",
            [](PredictorClient &self) { self.create_predictor(); })
       .def("destroy_predictor",
@@ -69,13 +72,15 @@ PYBIND11_MODULE(serving_client, m) {
               const std::vector<std::vector<int64_t>> &int_feed,
               const std::vector<std::string> &int_feed_name,
               const std::vector<std::string> &fetch_name,
-           PredictorRes & predict_res) {
+              PredictorRes &predict_res,
+              const int &pid) {
              return self.predict(float_feed,
                                  float_feed_name,
                                  int_feed,
                                  int_feed_name,
                                  fetch_name,
-                                 predict_res);
+                                 predict_res,
+                                 pid);
            })
       .def("batch_predict",
            [](PredictorClient &self,

python/examples/bert/bert_client.py

@@ -36,6 +36,7 @@ class BertService():
         self.show_ids = show_ids
         self.do_lower_case = do_lower_case
         self.retry = retry
+        self.pid = os.getpid()
         self.profile = True if ("FLAGS_profile_client" in os.environ and
                                 os.environ["FLAGS_profile_client"]) else False
 
@@ -78,7 +79,8 @@ class BertService():
                 }
                 prepro_end = time.time()
                 if self.profile:
-                    print("PROFILE\tbert_pre_0:{} bert_pre_1:{}".format(
+                    print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
+                        self.pid,
                         int(round(prepro_start * 1000000)),
                         int(round(prepro_end * 1000000))))
                 fetch_map = self.client.predict(feed=feed, fetch=fetch)
@@ -111,7 +113,8 @@ class BertService():
                 feed_batch.append(feed)
             prepro_end = time.time()
             if self.profile:
-                print("PROFILE\tbert_pre_0:{} bert_pre_1:{}".format(
+                print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
+                    self.pid,
                     int(round(prepro_start * 1000000)),
                     int(round(prepro_end * 1000000))))
             fetch_map_batch = self.client.batch_predict(
@@ -120,7 +123,6 @@ class BertService():
 
 
 def test():
-
     bc = BertService(
         model_name='bert_chinese_L-12_H-768_A-12',
         max_seq_len=20,
@@ -130,9 +132,13 @@ def test():
     config_file = './serving_client_conf/serving_client_conf.prototxt'
     fetch = ["pooled_output"]
     bc.load_client(config_file, server_addr)
-    batch_size = 4
+    batch_size = 1
     batch = []
     for line in sys.stdin:
+        if batch_size == 1:
+            result = bc.run_general([[line.strip()]], fetch)
+            print(result)
+        else:
             if len(batch) < batch_size:
                 batch.append([line.strip()])
             else:
@@ -140,6 +146,11 @@ def test():
                 batch = []
                 for r in result:
                     print(r)
+    if len(batch) > 0:
+        result = bc.run_batch_general(batch, fetch)
+        batch = []
+        for r in result:
+            print(r)
 
 
 if __name__ == '__main__':

python/examples/bert/bert_server.py

@@ -31,8 +31,6 @@ op_seq_maker.add_op(general_response_op)
 server = Server()
 server.set_op_sequence(op_seq_maker.get_op_sequence())
 server.set_num_threads(4)
-server.set_local_bin(
-    "~/github/Serving/build_server/core/general-server/serving")
 
 server.load_model_config(sys.argv[1])
 port = int(sys.argv[2])

python/examples/bert/get_data.sh

+wget https://paddle-serving.bj.bcebos.com/bert_example/data-c.txt --no-check-certificate

python/examples/imdb/test_gpu_server.py → python/examples/fit_a_line/benchmark.py

@@ -11,25 +11,38 @@
 # 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.
-
-import os
+from paddle_serving_client import Client
+from paddle_serving_client.utils import MultiThreadRunner
+from paddle_serving_client.utils import benchmark_args
+import time
+import paddle
 import sys
-from paddle_serving_server_gpu import OpMaker
-from paddle_serving_server_gpu import OpSeqMaker
-from paddle_serving_server_gpu import Server
+import requests
 
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-general_infer_op = op_maker.create('general_infer')
+args = benchmark_args()
 
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(general_infer_op)
+def single_func(idx, resource):
+    if args.request == "rpc":
+        client = Client()
+        client.load_client_config(args.model)
+        client.connect([args.endpoint])
+        train_reader = paddle.batch(paddle.reader.shuffle(
+            paddle.dataset.uci_housing.train(), buf_size=500), batch_size=1)
+        start = time.time()
+        for data in train_reader():
+            fetch_map = client.predict(feed={"x": data[0][0]}, fetch=["price"])
+        end = time.time()
+        return [[end - start]]
+    elif args.request == "http":
+        train_reader = paddle.batch(paddle.reader.shuffle(
+            paddle.dataset.uci_housing.train(), buf_size=500), batch_size=1)
+        start = time.time()
+        for data in train_reader():
+            r = requests.post('http://{}/uci/prediction'.format(args.endpoint),
+                              data = {"x": data[0]})
+        end = time.time()
+        return [[end - start]]
 
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-server.set_num_threads(12)
-server.load_model_config(sys.argv[1])
-port = int(sys.argv[2])
-server.prepare_server(workdir="work_dir1", port=port, device="gpu")
-server.run_server()
+multi_thread_runner = MultiThreadRunner()
+result = multi_thread_runner.run(single_func, args.thread, {})
+print(result)

python/examples/imdb/README.md

@@ -19,7 +19,7 @@ cat test.data | python test_client_batch.py inference.conf 4 > result
 
 设备 ：Intel(R) Xeon(R)  Gold 6271 CPU @ 2.60GHz * 48
 
-模型 ：IMDB-CNN
+模型 ：[CNN](https://github.com/PaddlePaddle/Serving/blob/develop/python/examples/imdb/nets.py)
 
 server thread num ： 16
 

python/examples/imdb/benchmark.py

@@ -13,55 +13,45 @@
 # limitations under the License.
 
 import sys
+import time
+import requests
+from imdb_reader import IMDBDataset
 from paddle_serving_client import Client
-from paddle_serving_client.metric import auc
 from paddle_serving_client.utils import MultiThreadRunner
-import time
+from paddle_serving_client.utils import benchmark_args
 
+args = benchmark_args()
 
-def predict(thr_id, resource):
+def single_func(idx, resource):
+    imdb_dataset = IMDBDataset()
+    imdb_dataset.load_resource(args.vocab)
+    filelist_fn = args.filelist
+    filelist = []
+    start = time.time()
+    with open(filelist_fn) as fin:
+        for line in fin:
+            filelist.append(line.strip())
+    filelist = filelist[idx::args.thread]
+    if args.request == "rpc":
         client = Client()
-    client.load_client_config(resource["conf_file"])
-    client.connect(resource["server_endpoint"])
-    thread_num = resource["thread_num"]
-    file_list = resource["filelist"]
-    line_id = 0
-    prob = []
-    label_list = []
-    dataset = []
-    for fn in file_list:
+        client.load_client_config(args.model)
+        client.connect([args.endpoint])
+        for fn in filelist:
             fin = open(fn)
             for line in fin:
-            if line_id % thread_num == thr_id - 1:
-                group = line.strip().split()
-                words = [int(x) for x in group[1:int(group[0])]]
-                label = [int(group[-1])]
-                feed = {"words": words, "label": label}
-                dataset.append(feed)
-            line_id += 1
-        fin.close()
-
-    start = time.time()
-    fetch = ["acc", "cost", "prediction"]
-    for inst in dataset:
-        fetch_map = client.predict(feed=inst, fetch=fetch)
-        prob.append(fetch_map["prediction"][1])
-        label_list.append(label[0])
+                word_ids, label = imdb_dataset.get_words_and_label(line)
+                fetch_map = client.predict(feed={"words": word_ids},
+                                           fetch=["prediction"])
+    elif args.request == "http":
+        for fn in filelist:
+            fin = open(fn)
+            for line in fin:
+                word_ids, label = imdb_dataset.get_words_and_label(line)
+                r = requests.post("http://{}/imdb/prediction".format(args.endpoint),
+                                  data={"words": word_ids})
     end = time.time()
-    client.release()
-    return [prob, label_list, [end - start]]
-
-
-if __name__ == '__main__':
-    conf_file = sys.argv[1]
-    data_file = sys.argv[2]
-    resource = {}
-    resource["conf_file"] = conf_file
-    resource["server_endpoint"] = ["127.0.0.1:9293"]
-    resource["filelist"] = [data_file]
-    resource["thread_num"] = int(sys.argv[3])
-
-    thread_runner = MultiThreadRunner()
-    result = thread_runner.run(predict, int(sys.argv[3]), resource)
+    return [[end - start]]
 
-    print("total time {} s".format(sum(result[-1]) / len(result[-1])))
+multi_thread_runner = MultiThreadRunner()
+result = multi_thread_runner.run(single_func, args.thread, {})
+print(result)

python/examples/imdb/get_data.sh

 wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
 tar -zxvf text_classification_data.tar.gz
-#wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imdb-demo%2Fimdb.tar.gz
-#tar -xzf imdb-demo%2Fimdb.tar.gz
+tar -zxvf imdb_model.tar.gz

python/examples/imdb/imdb_reader.py

@@ -30,6 +30,14 @@ class IMDBDataset(dg.MultiSlotDataGenerator):
         self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
         self.return_value = ("words", [1, 2, 3, 4, 5, 6]), ("label", [0])
 
+    def get_words_only(self, line):
+        sent = line.lower().replace("<br />", " ").strip()
+        words = [x for x in self._pattern.split(sent) if x and x != " "]
+        feas = [
+            self._vocab[x] if x in self._vocab else self._unk_id for x in words
+        ]
+        return feas
+
     def get_words_and_label(self, line):
         send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
                                                               " ").strip()

python/examples/imdb/imdb_web_service_demo.sh

-wget https://paddle-serving.bj.bcebos.com/imdb-demo%2Fimdb_service.tar.gz
+wget https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_service.tar.gz
 tar -xzf imdb_service.tar.gz
 wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
 tar -zxvf text_classification_data.tar.gz

python/examples/imdb/local_train.py

@@ -49,8 +49,9 @@ if __name__ == "__main__":
     dataset.set_batch_size(128)
     dataset.set_filelist(filelist)
     dataset.set_thread(10)
-    from nets import bow_net
-    avg_cost, acc, prediction = bow_net(data, label, dict_dim)
+    from nets import lstm_net
+    model_name = "imdb_lstm"
+    avg_cost, acc, prediction = lstm_net(data, label, dict_dim)
     optimizer = fluid.optimizer.SGD(learning_rate=0.01)
     optimizer.minimize(avg_cost)
 
@@ -65,6 +66,7 @@ if __name__ == "__main__":
             program=fluid.default_main_program(), dataset=dataset, debug=False)
         logger.info("TRAIN --> pass: {}".format(i))
         if i == 5:
-            serving_io.save_model("imdb_model", "imdb_client_conf",
+            serving_io.save_model("{}_model".format(model_name),
+                                  "{}_client_conf".format(model_name),
                                   {"words": data}, {"prediction": prediction},
                                   fluid.default_main_program())

python/examples/imdb/test_client.py

+# Copyright (c) 2020 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.
 from paddle_serving_client import Client
+from imdb_reader import IMDBDataset
 import sys
 
 client = Client()
 client.load_client_config(sys.argv[1])
 client.connect(["127.0.0.1:9393"])
 
+# you can define any english sentence or dataset here
+# This example reuses imdb reader in training, you
+# can define your own data preprocessing easily.
+imdb_dataset = IMDBDataset()
+imdb_dataset.load_resource(sys.argv[2])
+
 for line in sys.stdin:
-    group = line.strip().split()
-    words = [int(x) for x in group[1:int(group[0]) + 1]]
-    label = [int(group[-1])]
-    feed = {"words": words, "label": label}
+    word_ids, label = imdb_dataset.get_words_and_label(line)
+    feed = {"words": word_ids, "label": label}
     fetch = ["acc", "cost", "prediction"]
     fetch_map = client.predict(feed=feed, fetch=fetch)
     print("{} {}".format(fetch_map["prediction"][1], label[0]))

python/examples/imdb/test_client_multithread.py

-# Copyright (c) 2020 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.
-
-from paddle_serving_client import Client
-import sys
-import subprocess
-from multiprocessing import Pool
-import time
-
-
-def predict(p_id, p_size, data_list):
-    client = Client()
-    client.load_client_config(conf_file)
-    client.connect(["127.0.0.1:8010"])
-    result = []
-    for line in data_list:
-        group = line.strip().split()
-        words = [int(x) for x in group[1:int(group[0])]]
-        label = [int(group[-1])]
-        feed = {"words": words, "label": label}
-        fetch = ["acc", "cost", "prediction"]
-        fetch_map = client.predict(feed=feed, fetch=fetch)
-        #print("{} {}".format(fetch_map["prediction"][1], label[0]))
-        result.append([fetch_map["prediction"][1], label[0]])
-    return result
-
-
-def predict_multi_thread(p_num):
-    data_list = []
-    with open(data_file) as f:
-        for line in f.readlines():
-            data_list.append(line)
-    start = time.time()
-    p = Pool(p_num)
-    p_size = len(data_list) / p_num
-    result_list = []
-    for i in range(p_num):
-        result_list.append(
-            p.apply_async(predict,
-                          [i, p_size, data_list[i * p_size:(i + 1) * p_size]]))
-    p.close()
-    p.join()
-    for i in range(p_num):
-        result = result_list[i].get()
-        for j in result:
-            print("{} {}".format(j[0], j[1]))
-    cost = time.time() - start
-    print("{} threads cost {}".format(p_num, cost))
-
-
-if __name__ == '__main__':
-    conf_file = sys.argv[1]
-    data_file = sys.argv[2]
-    p_num = int(sys.argv[3])
-    predict_multi_thread(p_num)

python/examples/imdb/test_server.py

-# Copyright (c) 2020 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.
-
-import os
-import sys
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpSeqMaker
-from paddle_serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-general_infer_op = op_maker.create('general_infer')
-general_response_op = op_maker.create('general_response')
-
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(general_infer_op)
-op_seq_maker.add_op(general_response_op)
-
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-server.set_num_threads(4)
-
-server.load_model_config(sys.argv[1])
-port = int(sys.argv[2])
-server.prepare_server(workdir="work_dir1", port=port, device="cpu")
-server.run_server()

python/examples/imdb/text_classify_service.py

@@ -11,7 +11,6 @@
 # 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.
-#!flask/bin/python
 from paddle_serving_server.web_service import WebService
 from imdb_reader import IMDBDataset
 import sys
@@ -27,7 +26,7 @@ class IMDBService(WebService):
         if "words" not in feed:
             exit(-1)
         res_feed = {}
-        res_feed["words"] = self.dataset.get_words_and_label(feed["words"])[0]
+        res_feed["words"] = self.dataset.get_words_only(feed["words"])[0]
         return res_feed, fetch
 
 imdb_service = IMDBService(name="imdb")

python/examples/util/timeline_trace.py

@@ -5,8 +5,9 @@ import sys
 profile_file = sys.argv[1]
 
 
-def prase(line, counter):
-    event_list = line.split(" ")
+def prase(pid_str, time_str, counter):
+    pid = pid_str.split(":")[1]
+    event_list = time_str.split(" ")
     trace_list = []
     for event in event_list:
         name, ts = event.split(":")
@@ -19,7 +20,7 @@ def prase(line, counter):
         event_dict = {}
         event_dict["name"] = name
         event_dict["tid"] = 0
-        event_dict["pid"] = 0
+        event_dict["pid"] = pid
         event_dict["ts"] = ts
         event_dict["ph"] = ph
 
@@ -36,7 +37,7 @@ if __name__ == "__main__":
         for line in f.readlines():
             line = line.strip().split("\t")
             if line[0] == "PROFILE":
-                trace_list = prase(line[1], counter)
+                trace_list = prase(line[1], line[2], counter)
                 counter += 1
                 for trace in trace_list:
                     all_list.append(trace)

python/paddle_serving_client/__init__.py

@@ -78,6 +78,7 @@ class Client(object):
         self.feed_types_ = {}
         self.feed_names_to_idx_ = {}
         self.rpath()
+        self.pid = os.getpid()
 
     def rpath(self):
         lib_path = os.path.dirname(paddle_serving_client.__file__)
@@ -160,6 +161,7 @@ class Client(object):
         int_feed_names = []
         float_feed_names = []
         fetch_names = []
+
         for key in feed:
             self.shape_check(feed, key)
             if key not in self.feed_names_:
@@ -177,7 +179,7 @@ class Client(object):
 
         ret = self.client_handle_.predict(float_slot, float_feed_names,
                                           int_slot, int_feed_names, fetch_names,
-                                          self.result_handle_)
+                                          self.result_handle_, self.pid)
 
         result_map = {}
         for i, name in enumerate(fetch_names):

python/paddle_serving_client/utils/__init__.py

@@ -11,16 +11,28 @@
 # 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.
-
+import os
 import sys
 import subprocess
+import argparse
 from multiprocessing import Pool
 
+def benchmark_args():
+    parser = argparse.ArgumentParser("benchmark")
+    parser.add_argument("--thread", type=int, default=10, help="concurrecy")
+    parser.add_argument("--model", type=str, default="", help="model for evaluation")
+    parser.add_argument("--endpoint", type=str, default="127.0.0.1:9292", help="endpoint of server")
+    parser.add_argument("--request", type=str, default="rpc", help="mode of service")
+    return parser.parse_args()
+
+
 class MultiThreadRunner(object):
     def __init__(self):
         pass
 
     def run(self, thread_func, thread_num, global_resource):
+        os.environ["http_proxy"] = ""
+        os.environ["https_proxy"] = ""
         p = Pool(thread_num)
         result_list = []
         for i in range(thread_num):