remove supervisor and use sessionmanager

tensorpack/models/common.py

@@ -136,7 +136,7 @@ def layer_register(
                         # log shape info and add activation
                         logger.info("{} output: {}".format(
                             scope.name, get_shape_str(outputs)))
-                        _LAYER_LOGGED.add(scope.name)
+                        _LAYER_LOGGED.add(scope_name)
             else:
                 # run the actual function
                 outputs = func(*args, **actual_args)

tensorpack/tfutils/summary.py

@@ -154,7 +154,8 @@ def add_moving_summary(v, *args, **kwargs):
     for x in v:
         assert isinstance(x, tf.Tensor), x
         assert x.get_shape().ndims == 0, x.get_shape()
-    # TODO will produce tower0/xxx?
+    # TODO will produce variable tower0/xxx?
+    # TODO not saved under distributed
     # TODO use zero_debias
     gs = get_global_step_var()
     with tf.name_scope(None), tf.device(gs.device):

tensorpack/train/distributed.py

@@ -215,26 +215,26 @@ class DistributedReplicatedTrainer(SingleCostFeedfreeTrainer):
                 or self.config.session_config is not None:
             raise ValueError(
                 "Cannot set session_creator or session_config for distributed training! "
-                "To use a custom session config, pass it to the tf.train.Server constructor.")
-
-        # TODO use scaffold + monitored session
-        class SupervisedSessionCreator(tf.train.SessionCreator):
-            def __init__(self, is_chief, target):
-                self.is_chief = is_chief
-                self.target = target
+                "To use a custom session config, pass it with tf.train.Server.")
+
+        init_op = tf.global_variables_initializer()
+        local_init_op = tf.local_variables_initializer()
+        ready_op = tf.report_uninitialized_variables()
+        sm = tf.train.SessionManager(
+            local_init_op=local_init_op,
+            ready_op=ready_op, graph=tf.get_default_graph())
+
+        def _create_session():
+            if self.is_chief:
+                return sm.prepare_session(master=self.server.target, init_op=init_op)
+            else:
+                return sm.wait_for_session(master=self.server.target)
 
+        class _Creator(tf.train.SessionCreator):
             def create_session(self):
-                # supervisor will finalize the graph..
-                self.sv = tf.train.Supervisor(
-                    is_chief=self.is_chief,
-                    logdir=None, saver=None,
-                    global_step=get_global_step_var(),
-                    summary_op=None, save_model_secs=0, summary_writer=None)
-                return self.sv.prepare_or_wait_for_session(
-                    master=self.target, start_standard_services=False)
-
-        self.config.session_creator = SupervisedSessionCreator(
-            self.is_chief, self.server.target)
+                return _create_session()
+
+        self.config.session_creator = _Creator()
 
     def add_sync_queues_and_barrier(self, name_prefix, enqueue_after_list):
         """Adds ops to enqueue on all worker queues.