diff --git a/fluid/PaddleCV/deeplabv3+/.run_ce.sh b/fluid/PaddleCV/deeplabv3+/.run_ce.sh
new file mode 100755
index 0000000000000000000000000000000000000000..540fb964ba94fd29dc28bb51342cdba839d433e7
--- /dev/null
+++ b/fluid/PaddleCV/deeplabv3+/.run_ce.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+export MKL_NUM_THREADS=1
+export OMP_NUM_THREADS=1
+
+DATASET_PATH=${HOME}/.cache/paddle/dataset/cityscape/
+
+cudaid=${deeplabv3plus:=0} # use 0-th card as default
+export CUDA_VISIBLE_DEVICES=$cudaid
+
+FLAGS_benchmark=true  python train.py \
+--batch_size=2 \
+--train_crop_size=769 \
+--total_step=50 \
+--save_weights_path=output1 \
+--dataset_path=$DATASET_PATH \
+--enable_ce | python _ce.py
+
+cudaid=${deeplabv3plus_m:=0,1,2,3} # use 0,1,2,3 card as default
+export CUDA_VISIBLE_DEVICES=$cudaid
+
+FLAGS_benchmark=true  python train.py \
+--batch_size=2 \
+--train_crop_size=769 \
+--total_step=50 \
+--save_weights_path=output4 \
+--dataset_path=$DATASET_PATH \
+--enable_ce | python _ce.py
diff --git a/fluid/PaddleCV/deeplabv3+/__init__.py b/fluid/PaddleCV/deeplabv3+/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/fluid/PaddleCV/deeplabv3+/_ce.py b/fluid/PaddleCV/deeplabv3+/_ce.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0127d6445213b9d3934220fa36e9eb44d3e04b4
--- /dev/null
+++ b/fluid/PaddleCV/deeplabv3+/_ce.py
@@ -0,0 +1,60 @@
+# this file is only used for continuous evaluation test!
+
+import os
+import sys
+sys.path.append(os.environ['ceroot'])
+from kpi import CostKpi
+from kpi import DurationKpi
+
+each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.1, 0, actived=True)
+train_loss_card1_kpi = CostKpi('train_loss_card1', 0.05, 0)
+each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.1, 0, actived=True)
+train_loss_card4_kpi = CostKpi('train_loss_card4', 0.05, 0)
+
+tracking_kpis = [
+        each_pass_duration_card1_kpi,
+        train_loss_card1_kpi,
+        each_pass_duration_card4_kpi,
+        train_loss_card4_kpi,
+        ]
+
+
+def parse_log(log):
+    '''
+    This method should be implemented by model developers.
+
+    The suggestion:
+
+    each line in the log should be key, value, for example:
+
+    "
+    train_cost\t1.0
+    test_cost\t1.0
+    train_cost\t1.0
+    train_cost\t1.0
+    train_acc\t1.2
+    "
+    '''
+    for line in log.split('\n'):
+        fs = line.strip().split('\t')
+        print(fs)
+        if len(fs) == 3 and fs[0] == 'kpis':
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
+
+def log_to_ce(log):
+    kpi_tracker = {}
+    for kpi in tracking_kpis:
+        kpi_tracker[kpi.name] = kpi
+
+    for (kpi_name, kpi_value) in parse_log(log):
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
+
+
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    log_to_ce(log)
diff --git a/fluid/PaddleCV/deeplabv3+/train.py b/fluid/PaddleCV/deeplabv3+/train.py
old mode 100644
new mode 100755
index 817d53d173467f9146918ec9bb6b44141eb0ac3f..a95fb12e4eee6c53a1046f067051f15c0d2dae6b
--- a/fluid/PaddleCV/deeplabv3+/train.py
+++ b/fluid/PaddleCV/deeplabv3+/train.py
@@ -34,6 +34,7 @@ def add_arguments():
     add_argument('parallel', bool, False, "using ParallelExecutor.")
     add_argument('use_gpu', bool, True, "Whether use GPU or CPU.")
     add_argument('num_classes', int, 19, "Number of classes.")
+    parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
 
 
 def load_model():
@@ -84,6 +85,14 @@ def loss(logit, label):
     return loss, label_nignore
 
 
+def get_cards(args):
+    if args.enable_ce:
+        cards = os.environ.get('CUDA_VISIBLE_DEVICES')
+        num = len(cards.split(","))
+        return num
+    else:
+        return args.num_devices
+
 CityscapeDataset = reader.CityscapeDataset
 parser = argparse.ArgumentParser()
 
@@ -99,6 +108,13 @@ deeplabv3p = models.deeplabv3p
 
 sp = fluid.Program()
 tp = fluid.Program()
+
+# only for ce
+if args.enable_ce:
+    SEED = 102
+    sp.random_seed = SEED
+    tp.random_seed = SEED
+
 crop_size = args.train_crop_size
 batch_size = args.batch_size
 image_shape = [crop_size, crop_size]
@@ -155,7 +171,13 @@ if args.parallel:
 
 batches = dataset.get_batch_generator(batch_size, total_step)
 
+total_time = 0.0
+epoch_idx = 0
+train_loss = 0
+
 for i, imgs, labels, names in batches:
+    epoch_idx += 1
+    begin_time = time.time()
     prev_start_time = time.time()
     if args.parallel:
         retv = exe_p.run(fetch_list=[pred.name, loss_mean.name],
@@ -167,11 +189,22 @@ for i, imgs, labels, names in batches:
                              'label': labels},
                        fetch_list=[pred, loss_mean])
     end_time = time.time()
+    total_time += end_time - begin_time
     if i % 100 == 0:
         print("Model is saved to", args.save_weights_path)
         save_model()
     print("step {:d}, loss: {:.6f}, step_time_cost: {:.3f}".format(
         i, np.mean(retv[1]), end_time - prev_start_time))
 
+    # only for ce
+    train_loss = np.mean(retv[1])
+
+if args.enable_ce:
+    gpu_num = get_cards(args)
+    print("kpis\teach_pass_duration_card%s\t%s" %
+            (gpu_num, total_time / epoch_idx))
+    print("kpis\ttrain_loss_card%s\t%s" %
+            (gpu_num, train_loss))
+
 print("Training done. Model is saved to", args.save_weights_path)
 save_model()
diff --git a/fluid/PaddleNLP/text_matching_on_quora/.run_ce.sh b/fluid/PaddleNLP/text_matching_on_quora/.run_ce.sh
old mode 100644
new mode 100755
index eca247a40a3f680a6a59c4a183bfba006ced8d44..f1bb7febd3f2c572544612baf24be14c711108e3
--- a/fluid/PaddleNLP/text_matching_on_quora/.run_ce.sh
+++ b/fluid/PaddleNLP/text_matching_on_quora/.run_ce.sh
@@ -6,9 +6,9 @@ export OMP_NUM_THREADS=1
 cudaid=${text_matching_on_quora:=0} # use 0-th card as default
 export CUDA_VISIBLE_DEVICES=$cudaid
 
-FLAGS_benchmark=true  python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce | python _ce.py
+FLAGS_benchmark=true  python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce --epoch_num=5 | python _ce.py
 
 cudaid=${text_matching_on_quora_m:=0,1,2,3} # use 0,1,2,3 card as default
 export CUDA_VISIBLE_DEVICES=$cudaid
 
-FLAGS_benchmark=true  python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce | python _ce.py
+FLAGS_benchmark=true  python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce --epoch_num=5 | python _ce.py
diff --git a/fluid/PaddleNLP/text_matching_on_quora/_ce.py b/fluid/PaddleNLP/text_matching_on_quora/_ce.py
index b38ad21a1e0eb7407f78d100a3cb3659f6c5d8d3..eadeb821da6f7049d1916a65a1ae4eb995c5cb6d 100644
--- a/fluid/PaddleNLP/text_matching_on_quora/_ce.py
+++ b/fluid/PaddleNLP/text_matching_on_quora/_ce.py
@@ -7,11 +7,11 @@ from kpi import CostKpi
 from kpi import DurationKpi
 
 
-each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.05, 0, actived=True)
-train_avg_cost_card1_kpi = CostKpi('train_avg_cost_card1', 0.2, 0)
+each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.08, 0, actived=True)
+train_avg_cost_card1_kpi = CostKpi('train_avg_cost_card1', 0.08, 0)
 train_avg_acc_card1_kpi = CostKpi('train_avg_acc_card1', 0.02, 0)
-each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.05, 0, actived=True)
-train_avg_cost_card4_kpi = CostKpi('train_avg_cost_card4', 0.2, 0)
+each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.08, 0, actived=True)
+train_avg_cost_card4_kpi = CostKpi('train_avg_cost_card4', 0.08, 0)
 train_avg_acc_card4_kpi = CostKpi('train_avg_acc_card4', 0.02, 0)
 
 tracking_kpis = [
diff --git a/fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py b/fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py
index 714fa6f970d9f213efdc6b6e1799b244696fb20d..0f88c6b6ef13aec25e08527b7efabe8638a3af25 100755
--- a/fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py
+++ b/fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py
@@ -34,6 +34,7 @@ parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument('--model_name',       type=str,   default='cdssmNet',                  help="Which model to train")
 parser.add_argument('--config',           type=str,   default='cdssm_base',       help="The global config setting")
 parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
+parser.add_argument('--epoch_num', type=int, help='Number of epoch')
 
 DATA_DIR = os.path.join(os.path.expanduser('~'), '.cache/paddle/dataset')
 
@@ -241,6 +242,9 @@ def main():
     args = parser.parse_args()
     global_config = configs.__dict__[args.config]()
 
+    if args.epoch_num != None:
+        global_config.epoch_num = args.epoch_num
+
     print("net_name: ", args.model_name)
     net = models.__dict__[args.model_name](global_config)