diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
new file mode 100644
index 0000000000000000000000000000000000000000..9693b26d549ca106a12f24eb2a7ef7d646e2c055
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
@@ -0,0 +1,57 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+places = fluid.layers.get_places()
+pd = fluid.layers.ParallelDo(places=places)
+with pd.do():
+    x_ = pd.read_input(x)
+    y_ = pd.read_input(y)
+    y_predict = fluid.layers.fc(input=x_, size=1, act=None)
+    cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
+    pd.write_output(fluid.layers.mean(x=cost))
+
+avg_cost = fluid.layers.mean(x=pd())
+
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer.minimize(avg_cost)
+
+BATCH_SIZE = 20
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+exe = fluid.Executor(place)
+
+exe.run(fluid.default_startup_program())
+
+PASS_NUM = 100
+for pass_id in range(PASS_NUM):
+    for data in train_reader():
+        avg_loss_value, = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+        print(avg_loss_value)
+        if avg_loss_value[0] < 10.0:
+            exit(0)  # if avg cost less than 10.0, we think our code is good.
+exit(1)