Add an option to choose the I/O Device for saving and loading models for CheckpointManager.

This option enables saving and restoring models to or from filesystems only
accessible from the localhost when using multiple devices.

PiperOrigin-RevId: 345770754
Change-Id: I8362fbf1402f2e1cb7fa7290a7401cc873c02a1f

tensorflow/python/training/checkpoint_management.py

@@ -752,7 +752,7 @@ class CheckpointManager(object):
     """Returns the `tf.train.Checkpoint` object."""
     return self._checkpoint
 
-  def save(self, checkpoint_number=None, check_interval=True):
+  def save(self, checkpoint_number=None, check_interval=True, options=None):
     """Creates a new checkpoint and manages it.
 
     Args:
@@ -768,6 +768,9 @@ class CheckpointManager(object):
         larger than `checkpoint_interval`. Otherwise it will always save the
         checkpoint unless a checkpoint has already been saved for the current
         step.
+      options: Optional `tf.train.CheckpointOptions` object. This argument only
+        works with TF2 checkpoint objects. For example, options =
+        tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')
 
     Returns:
       The path to the new checkpoint. It is also recorded in the `checkpoints`
@@ -809,7 +812,10 @@ class CheckpointManager(object):
       checkpoint_number = training_util.global_step(
           sess=session, global_step_tensor=checkpoint_number)
     prefix = "%s-%d" % (self._prefix, checkpoint_number)
+    if options is None:
       save_path = self._checkpoint.write(prefix)
+    else:
+      save_path = self._checkpoint.write(prefix, options=options)
     timestamp = time.time()
     # If this is an overwritten checkpoint we were previously tracking, delete
     # and reinsert it to make sure it goes to the end of the queue.

tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint-manager.pbtxt

@@ -32,6 +32,6 @@ tf_class {
   }
   member_method {
     name: "save"
-    argspec: "args=[\'self\', \'checkpoint_number\', \'check_interval\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], "
+    argspec: "args=[\'self\', \'checkpoint_number\', \'check_interval\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
   }
 }

tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-manager.pbtxt

@@ -32,6 +32,6 @@ tf_class {
   }
   member_method {
     name: "save"
-    argspec: "args=[\'self\', \'checkpoint_number\', \'check_interval\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], "
+    argspec: "args=[\'self\', \'checkpoint_number\', \'check_interval\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
   }
 }