diff --git a/tensorflow/python/distribute/multi_process_runner.py b/tensorflow/python/distribute/multi_process_runner.py
index 7f653c0e2de..e5be4fa4a14 100644
--- a/tensorflow/python/distribute/multi_process_runner.py
+++ b/tensorflow/python/distribute/multi_process_runner.py
@@ -205,7 +205,7 @@ class MultiProcessRunner(object):
     self._outstanding_subprocess_count = 0
     self._reading_threads = []
 
-    self._manager = multiprocessing.Manager()
+    self._manager = manager()
     self._process_status_queue = self._manager.Queue()
     self._parent_to_sub_queue = self._manager.Queue()
     parties = sum(len(addresses) for addresses in self._cluster_spec.values())
@@ -568,29 +568,6 @@ class MultiProcessRunner(object):
                      task_type, task_id)
     self._all_forced_terminated = True
 
-  def get_manager(self):
-    """Returns the multiprocessing manager object for concurrency tools.
-
-    The manager object is useful as it controls a server process that holds
-    the python objects that can be shared across processes. This can be used
-    for parent-subprocess communication:
-
-    ```python
-    mpr = multi_process_runner.MultiProcessRunner(...)
-    manager = mpr.get_manager()
-    some_event_happening_in_subprocess = manager.Event()
-    mpr.set_args(args=(some_event_happening_in_subprocess,))
-    mpr.start()
-    some_event_happening_in_subprocess.wait()
-    # Do something that only should after some event happens in subprocess.
-    ```
-
-    Note that the user of multi_process_runner should not create additional
-    `multiprocessing.Manager()` objects; doing so can result in segfault in
-    some cases.
-    """
-    return self._manager
-
 
 class _Process(multi_process_lib.Process):
   """A modified `multiprocessing.Process` that can set up environment variables."""
@@ -991,6 +968,41 @@ def barrier():
   return _barrier
 
 
+_manager = None
+_manager_lock = threading.Lock()
+
+
+def manager():
+  """Returns the multiprocessing manager object for concurrency tools.
+
+  The manager object is useful as it controls a server process that holds
+  the python objects that can be shared across processes. This can be used
+  for parent-subprocess communication:
+
+  ```python
+  manager = multi_process_runner.manager()
+  some_event_happening_in_subprocess = manager.Event()
+  mpr = multi_process_runner.MultiProcessRunner(proc_func, cluster_spec,
+      args=(some_event_happening_in_subprocess,))
+  mpr.start()
+  some_event_happening_in_subprocess.wait()
+  # Do something that only should after some event happens in subprocess.
+  ```
+
+  Note that the user of multi_process_runner should not create additional
+  `multiprocessing.Manager()` objects; doing so can result in segfault in
+  some cases.
+
+  This method should only be called after multi_process_runner.test_main() is
+  called.
+  """
+  global _manager
+  with _manager_lock:
+    if _manager is None:
+      _manager = multiprocessing.Manager()
+    return _manager
+
+
 def test_main():
   """Main function to be called within `__main__` of a test file."""
   multi_process_lib.test_main()