diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py
index e691cfd83738d0c2db5dfd2ac1cfdb97ad45ac9d..572cb746c44c813a4f297ab3d975f9fab6e332b6 100644
--- a/mindspore/train/callback.py
+++ b/mindspore/train/callback.py
@@ -16,7 +16,6 @@
 
 import os
 import stat
-import shutil
 import time
 import numpy as np
 
@@ -625,8 +624,6 @@ class ModelCheckpoint(Callback):
             global _save_dir
             _save_dir = self._directory
             cur_file = os.path.join(self._directory, cur_ckpoint_file)
-            tmp_ckpt_file_name_for_cur_process = str(os.getpid()) + "-" + 'parameters.ckpt'
-            gen_file = os.path.join(_save_dir, tmp_ckpt_file_name_for_cur_process)
             self._last_time_for_keep = time.time()
             self._last_triggered_step = cb_params.cur_step_num
 
@@ -634,10 +631,8 @@ class ModelCheckpoint(Callback):
                 _set_cur_net(cb_params.train_network)
                 cb_params.train_network.exec_checkpoint_graph()
 
-            _exec_save_checkpoint(cb_params.train_network, gen_file, self._config.integrated_save)
+            _exec_save_checkpoint(cb_params.train_network, cur_file, self._config.integrated_save)
 
-            if os.path.exists(gen_file):
-                shutil.move(gen_file, cur_file)
             self._latest_ckpt_file_name = cur_file
 
     @property
diff --git a/mindspore/train/dataset_helper.py b/mindspore/train/dataset_helper.py
index 28d65349ee78c5b7db5d0e64928dc507907fae01..eb119a2907378eb9e9dcc3a448050da7d5288dd8 100644
--- a/mindspore/train/dataset_helper.py
+++ b/mindspore/train/dataset_helper.py
@@ -84,12 +84,12 @@ class DatasetHelper:
 class _DatasetIter:
     """Base iter for dataset help"""
     def __init__(self, dataset):
-        self.loop_size = 1
+        if not hasattr(dataset, '__loop_size__'):
+            self.loop_size = dataset.get_dataset_size()
+        else:
+            self.loop_size = dataset.__loop_size__
+
         if not hasattr(dataset, '__ME_INITED__'):
-            if not hasattr(dataset, '__loop_size__'):
-                self.loop_size = dataset.get_dataset_size()
-            else:
-                self.loop_size = dataset.__loop_size__
             dataset.__TRANSFER_DATASET__ = _exec_datagraph(dataset, self.loop_size)
             dataset.__ME_INITED__ = dataset.__TRANSFER_DATASET__.queue_name
 
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 4e6e67e32bc2bdcf5e955c94161d4e166c06c8aa..30473ba523ed5ea3409be2143f80fc06ede0cc2a 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -15,6 +15,7 @@
 """Model and parameters serialization."""
 import os
 import stat
+from threading import Thread
 import numpy as np
 
 import mindspore.nn as nn
@@ -96,7 +97,23 @@ def _update_param(param, new_param):
         param.set_parameter_data(type(param.data)(new_param.data))
 
 
-def save_checkpoint(parameter_list, ckpoint_file_name):
+def asyn_thread(fun):
+    def wrapper(*args, **kwargs):
+        thr = Thread(target=fun, args=args, kwargs=kwargs)
+        thr.start()
+    return wrapper
+
+
+@asyn_thread
+def asyn_save_fun(ckpoint_file_name, checkpoint_list):
+    logger.info("Asynchronous execute save checkpoint into file.")
+    with open(ckpoint_file_name, "wb") as f:
+        f.write(checkpoint_list.SerializeToString())
+        os.chmod(ckpoint_file_name, stat.S_IRUSR)
+    logger.info("Asynchronous save checkpoint into file process finish.")
+
+
+def save_checkpoint(parameter_list, ckpoint_file_name, asyn_exec=False):
     """
     Saves checkpoint info to a specified file.
 
@@ -104,6 +121,7 @@ def save_checkpoint(parameter_list, ckpoint_file_name):
         parameter_list (list): Parameters list, each element is a dict
                                like {"name":xx, "type":xx, "shape":xx, "data":xx}.
         ckpoint_file_name (str): Checkpoint file name.
+        asyn_exec (bool): Whether asynchronous execute save checkpoint into file.
 
     Raises:
         RuntimeError: Failed to save the Checkpoint file.
@@ -127,10 +145,12 @@ def save_checkpoint(parameter_list, ckpoint_file_name):
             else:
                 for dim in param['data'].shape():
                     param_tensor.dims.append(dim)
-
-        with open(ckpoint_file_name, "wb") as f:
-            f.write(checkpoint_list.SerializeToString())
-        os.chmod(ckpoint_file_name, stat.S_IRUSR)
+        if asyn_exec:
+            asyn_save_fun(ckpoint_file_name, checkpoint_list)
+        else:
+            with open(ckpoint_file_name, "wb") as f:
+                f.write(checkpoint_list.SerializeToString())
+            os.chmod(ckpoint_file_name, stat.S_IRUSR)
 
     except BaseException as e:
         logger.error("Failed to save the checkpoint file %s.", ckpoint_file_name)
@@ -298,7 +318,7 @@ def _save_graph(network, file_name):
         os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
 
 
-def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True):
+def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True, asyn_save=False):
     """
     Saves checkpoint for 'ms' backend.
 
@@ -329,7 +349,7 @@ def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True
         each_param["data"] = param_data
         param_list.append(each_param)
 
-    save_checkpoint(param_list, ckpoint_file_name)
+    save_checkpoint(param_list, ckpoint_file_name, asyn_save)
 
 
 def _get_merged_param_data(net, param_name, param_data):