diff --git a/PaddleNLP/language_representations_kit/ELMo/args.py b/PaddleNLP/language_representations_kit/ELMo/args.py
index 67537befa9335c6a410203d874f03388891043a2..dbcac53ef50ad2a34ebb4b8b88b6c92a1272020c 100755
--- a/PaddleNLP/language_representations_kit/ELMo/args.py
+++ b/PaddleNLP/language_representations_kit/ELMo/args.py
@@ -118,6 +118,7 @@ def parse_args():
     parser.add_argument('--update_method', type=str, default='nccl2')
     parser.add_argument('--random_seed', type=int, default=0)
     parser.add_argument('--n_negative_samples_batch', type=int, default=8000)
+    parser.add_argument('--enable_ce', action='store_true', help='whether print log for ce')
     args = parser.parse_args()
 
     return args
diff --git a/PaddleNLP/language_representations_kit/ELMo/train.py b/PaddleNLP/language_representations_kit/ELMo/train.py
index 25beb4b1aba1a05109375f4e88ec79887d389218..62f96eb1a5e4b9311cfce17c2ae6f2b637b7b2d5 100755
--- a/PaddleNLP/language_representations_kit/ELMo/train.py
+++ b/PaddleNLP/language_representations_kit/ELMo/train.py
@@ -264,10 +264,17 @@ def train():
     vocab_size = vocab.size
     logger.info("finished load vocab")
 
+    if args.enable_ce:
+        random.seed(args.random_seed)
+        np.random.seed(args.random_seed)
+
     logger.info('build the model...')
     # build model
     train_prog = fluid.Program()
     train_startup_prog = fluid.Program()
+    if args.enable_ce:
+        train_prog.random_seed = args.random_seed
+        train_startup_prog.random_seed = args.random_seed
     # build infer model
     infer_prog = fluid.Program()
     infer_startup_prog = fluid.Program()
@@ -559,6 +566,19 @@ def train_loop(args,
                 os.makedirs(model_path)
             fluid.io.save_persistables(
                 executor=exe, dirname=model_path, main_program=train_prog)
+
+    if args.enable_ce:
+        card_num = get_cards()
+        ce_loss = 0
+        ce_time = 0
+        try:
+            ce_loss = ce_info[-2][0]
+            ce_time = ce_info[-2][1]
+        except:
+            print("ce info error")
+        print("kpis\ttrain_duration_card%s\t%s" % (card_num, ce_time))
+        print("kpis\ttrain_loss_card%s\t%f" % (card_num, ce_loss))
+
     end_time = time.time()
     total_time += end_time - start_time
     epoch_id = int(final_batch_id / n_batches_per_epoch)