diff --git a/fluid/PaddleNLP/neural_machine_translation/transformer/train.py b/fluid/PaddleNLP/neural_machine_translation/transformer/train.py
index 5fc98868aa6e36bc5d1c5c0ad7ab231cda0fd52d..16d48238941a03309cc9ba269cd619bd21e0f561 100644
--- a/fluid/PaddleNLP/neural_machine_translation/transformer/train.py
+++ b/fluid/PaddleNLP/neural_machine_translation/transformer/train.py
@@ -408,10 +408,19 @@ def test_context(exe, train_exe, dev_count):
     test_data = prepare_data_generator(
         args, is_test=True, count=dev_count, pyreader=pyreader)
 
-    exe.run(startup_prog)
+    exe.run(startup_prog)  # to init pyreader for testing
+    if TrainTaskConfig.ckpt_path:
+        fluid.io.load_persistables(
+            exe, TrainTaskConfig.ckpt_path, main_program=test_prog)
+
+    exec_strategy = fluid.ExecutionStrategy()
+    exec_strategy.use_experimental_executor = True
+    build_strategy = fluid.BuildStrategy()
     test_exe = fluid.ParallelExecutor(
         use_cuda=TrainTaskConfig.use_gpu,
         main_program=test_prog,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy,
         share_vars_from=train_exe)
 
     def test(exe=test_exe, pyreader=pyreader):
@@ -457,7 +466,11 @@ def train_loop(exe,
                nccl2_trainer_id=0):
     # Initialize the parameters.
     if TrainTaskConfig.ckpt_path:
-        fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
+        exe.run(startup_prog)  # to init pyreader for training
+        logging.info("load checkpoint from {}".format(
+            TrainTaskConfig.ckpt_path))
+        fluid.io.load_persistables(
+            exe, TrainTaskConfig.ckpt_path, main_program=train_prog)
     else:
         logging.info("init fluid.framework.default_startup_program")
         exe.run(startup_prog)
@@ -741,6 +754,7 @@ if __name__ == "__main__":
     LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
     logging.basicConfig(
         stream=sys.stdout, level=logging.DEBUG, format=LOG_FORMAT)
+    logging.getLogger().setLevel(logging.INFO)
 
     args = parse_args()
     train(args)