diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/v2/Dockerfile
index 32e68b6150ff428ea5b88d7dbe5525b4f5e2e191..ac85b1a7d06cd30f054e76745a93523ad5efcc30 100644
--- a/benchmark/cluster/v2/Dockerfile
+++ b/benchmark/cluster/v2/Dockerfile
@@ -1,5 +1,5 @@
 FROM paddlepaddle/paddlecloud-job
-RUN mkdir -p /workspace && mkdir -p /root/.cache/paddle/dataset/flowers/
-ADD vgg16.py reader.py /workspace/
-COPY 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/
-
+RUN mkdir -p /workspace
+ADD reader.py /workspace/
+RUN python /workspace/reader.py
+ADD vgg16.py /workspace/
diff --git a/benchmark/cluster/v2/reader.py b/benchmark/cluster/v2/reader.py
index 060bf2bda2f3eb54173155ad08e06ce1795fcf63..16ac2dbcef4b758a2bf7a057a4a99e4ce7e136cb 100644
--- a/benchmark/cluster/v2/reader.py
+++ b/benchmark/cluster/v2/reader.py
@@ -67,4 +67,4 @@ if __name__ == '__main__':
     #    print len(im[0])
     #for im in train_reader('test.list'):
     #    print len(im[0])
-    paddle.dataset.flowers.train()
+    paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 8644a547b33a6b2e6f2b855d364dde92c7a2790a..91894932761f559ed8d3c694306891e44bf1a7de 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -14,13 +14,15 @@
 
 import gzip
 
-import paddle.v2.dataset.flowers as flowers
+import paddle.v2.dataset.cifar as cifar
 import paddle.v2 as paddle
 import reader
+import time
 
-DATA_DIM = 3 * 224 * 224  # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
-CLASS_DIM = 102
+DATA_DIM = 3 * 32 * 32
+CLASS_DIM = 10
 BATCH_SIZE = 128
+ts = 0
 
 
 def vgg(input, nums, class_dim):
@@ -74,6 +76,7 @@ def vgg19(input, class_dim):
 
 
 def main():
+    global ts
     paddle.init(use_gpu=False, trainer_count=1)
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(DATA_DIM))
@@ -100,13 +103,13 @@ def main():
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            flowers.train(),
+            cifar.train10(),
             # To use other data, replace the above line with:
             # reader.train_reader('train.list'),
             buf_size=1000),
         batch_size=BATCH_SIZE)
     test_reader = paddle.batch(
-        flowers.valid(),
+        cifar.test10(),
         # To use other data, replace the above line with:
         # reader.test_reader('val.list'),
         batch_size=BATCH_SIZE)
@@ -120,10 +123,14 @@ def main():
 
     # End batch and end pass event handler
     def event_handler(event):
+        global ts
+        if isinstance(event, paddle.event.BeginIteration):
+            ts = time.time()
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 1 == 0:
-                print "\nPass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
+                print "\nPass %d, Batch %d, Cost %f, %s, spent: %f" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    time.time() - ts)
         if isinstance(event, paddle.event.EndPass):
             with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
                 trainer.save_parameter_to_tar(f)
@@ -137,3 +144,4 @@ def main():
 
 if __name__ == '__main__':
     main()
+