diff --git a/python/paddle/fluid/dataset.py b/python/paddle/fluid/dataset.py
index e90c36da9aa5cb88a34cbdd5e8486143d3e885ef..d83ad37a89036c625adc03b11a6a530da3320513 100644
--- a/python/paddle/fluid/dataset.py
+++ b/python/paddle/fluid/dataset.py
@@ -213,6 +213,7 @@ class InMemoryDataset(DatasetBase):
             >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
             >>> filelist = ["a.txt", "b.txt"]
             >>> dataset.set_filelist(filelist)
+            >>> dataset.load_into_memory()
             >>> dataset.local_shuffle()
         """
         self.dataset.local_shuffle()
@@ -230,6 +231,7 @@ class InMemoryDataset(DatasetBase):
             >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
             >>> filelist = ["a.txt", "b.txt"]
             >>> dataset.set_filelist(filelist)
+            >>> dataset.load_into_memory()
             >>> dataset.global_shuffle(fleet)
 
         Args:
@@ -247,6 +249,25 @@ class InMemoryDataset(DatasetBase):
         if fleet is not None:
             fleet.fleet_instance.role_maker_._barrier_worker()
 
+    def release_memory(self):
+        """
+        Release InMemoryDataset memory data, when data will not be used again.
+
+        Example:
+            >>> import paddle.fluid as fluid
+            >>> import paddle.fluid.incubate.fleet.parameter_server as fleet
+            >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
+            >>> filelist = ["a.txt", "b.txt"]
+            >>> dataset.set_filelist(filelist)
+            >>> dataset.load_into_memory()
+            >>> dataset.global_shuffle(fleet)
+            >>> exe = fluid.Executor(fluid.CPUPlace())
+            >>> exe.run(fluid.default_startup_program())
+            >>> exe.train_from_dataset(fluid.default_main_program(), dataset)
+            >>> dataset.release_memory()
+        """
+        self.dataset.release_memory()
+
 
 class QueueDataset(DatasetBase):
     """
diff --git a/python/paddle/fluid/incubate/fleet/base/role_maker.py b/python/paddle/fluid/incubate/fleet/base/role_maker.py
index 528f7b3269eb90435d88cffadfa185cc664e430a..506a38059c33b94f8dc421c1e2e3b800de35fac7 100644
--- a/python/paddle/fluid/incubate/fleet/base/role_maker.py
+++ b/python/paddle/fluid/incubate/fleet/base/role_maker.py
@@ -128,7 +128,7 @@ class MPIRoleMaker(RoleMakerBase):
         """
         finalize the current MPI instance.
         """
-        self.comm_.finalize()
+        pass
 
 
 class MPISymetricRoleMaker(MPIRoleMaker):
diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/__init__.py b/python/paddle/fluid/incubate/fleet/parameter_server/__init__.py
index 044aa33c2b5b572aa40169e8c57936b105ba0121..ee5dd5acf18987c25cab50167a9274ceeece9c73 100644
--- a/python/paddle/fluid/incubate/fleet/parameter_server/__init__.py
+++ b/python/paddle/fluid/incubate/fleet/parameter_server/__init__.py
@@ -228,6 +228,40 @@ class Fleet(object):
         """
         self._fleet_ptr.save_model(save_path)
 
+    def split_filelist(self, filelist):
+        """
+        split filelist before distributed training,
+        for example, filelist is [a, b, c ,d, e]  and trainer_num = 2,
+        then trainer 0 gets [a, b, c] and trainer 1 gets [d, e]
+
+        Example:
+            >>> all_filelist = ["a.txt", "b.txt", "c.txt"]
+            >>> my_filelist = fleet.split_filelist(all_filelist)
+            >>> dataset = fluid.DatasetFactory().create_dataset()
+            >>> dataset.set_filelist(my_filelist)
+
+        Args:
+            filelist(list): list of filename, can be local or hdfs/afs.
+
+        Returns:
+            list of filename which belongs to this trainer.
+        """
+        file_num = len(filelist)
+        trainer_id = self.get_worker_index()
+        trainer_num = self.get_worker_num()
+        if trainer_num > file_num:
+            raise ValueError("trainer_num should be <= file_num : "
+                             "%s > %s" % (trainer_num, file_num))
+        # get interval of filelist, it's [ )
+        start = 0
+        end = 0
+        for i in range(0, trainer_id + 1):
+            length = file_num / trainer_num + (i < (file_num % trainer_num))
+            start = end
+            end += length
+        my_filelist = filelist[start:end]
+        return my_filelist
+
     def _set_opt_info(self, opt_info):
         """
         this function saves the result from DistributedOptimizer.minimize()
@@ -324,3 +358,4 @@ save_pserver_model = fleet_instance.save_pserver_model
 worker_num = fleet_instance.get_worker_num
 server_num = fleet_instance.get_server_num
 worker_index = fleet_instance.get_worker_index
+split_filelist = fleet_instance.split_filelist