diff --git a/python/examples/imdb/README.md b/python/examples/imdb/README.md
index 036bd9d79d216c44db1adbbb867daff94aa5584b..9106dd872ae6ea0a6bd3ee6f732dacf6e8b44498 100644
--- a/python/examples/imdb/README.md
+++ b/python/examples/imdb/README.md
@@ -10,3 +10,4 @@ cat test.data | python test_client.py > result
 ```
 python test_client_multithread.py inference.conf test.data 4 > result
 ```
+batch clienit
diff --git a/python/examples/imdb/test_client_batch.py b/python/examples/imdb/test_client_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..6686df272d62fe62315417470d2ce747a4b3c9ff
--- /dev/null
+++ b/python/examples/imdb/test_client_batch.py
@@ -0,0 +1,50 @@
+# 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
+import time
+
+
+def predict_for_batch(batch_size=4):
+    client = Client()
+    client.load_client_config(conf_file)
+    client.connect(["127.0.0.1:8010"])
+    start = time.time()
+    feed_batch = []
+    for line in sys.stdin:
+        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"]
+        feed_batch.append(feed)
+        if len(feed_batch) == batch_size:
+            fetch_batch = client.predict_for_batch(
+                feed_batch=feed_batch, fetch=fetch)
+            for i in range(batch_size):
+                print("{} {}".format(fetch_batch[i]["prediction"][1],
+                                     feed_batch[i]["label"][0]))
+            feed_batch = []
+    cost = time.time() - start
+    print("total cost : {}".format(cost))
+    print(time.time())
+
+
+if __name__ == '__main__':
+    conf_file = sys.argv[1]
+    batch_size = int(sys.argv[2])
+    predict_for_batch(batch_size)