add multi thread client

python/examples/imdb/README.md

+### 使用方法
+
+假设数据文件为test.data，配置文件为inference.conf
+
+单进程client
+```
+cat test.data | python test_client.py > result
+```
+多进程client，若进程数为4
+```
+python test_client_multithread.py inference.conf test.data 4 > result
+```

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 import Client
+import sys
+import subprocess
+from multiprocessing import Pool, Queue
+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 = sys.argv[3]
+    predict_multi_thread(p_num)