diff --git a/digits/tools/tensorflow/model.py b/digits/tools/tensorflow/model.py
index 310e300c..eee4e3af 100644
--- a/digits/tools/tensorflow/model.py
+++ b/digits/tools/tensorflow/model.py
@@ -132,49 +132,50 @@ def create_model(self, obj_UserModel, stage_scope, batch_x=None):
 
         # Run the user model through the build_model function that should be filled in
         grad_towers = []
+        with tf.variable_scope(tf.get_variable_scope()):
         for dev_i, dev_name in enumerate(available_devices):
             with tf.device(dev_name):
-                current_scope = stage_scope if len(available_devices) == 1 else ('tower_%d' % dev_i)
-                with tf.name_scope(current_scope) as scope_tower:
-
-                    if self.stage != digits.STAGE_INF:
-                        tower_model = self.add_tower(obj_tower=obj_UserModel,
-                                                     x=batch_x_split[dev_i],
-                                                     y=batch_y_split[dev_i])
-                    else:
-                        tower_model = self.add_tower(obj_tower=obj_UserModel,
-                                                     x=batch_x_split[dev_i],
-                                                     y=None)
-
-                    with tf.variable_scope(digits.GraphKeys.MODEL, reuse=dev_i > 0):
-                        tower_model.inference  # touch to initialize
-
-                    if self.stage == digits.STAGE_INF:
-                        # For inferencing we will only use the inference part of the graph
-                        continue
-
-                    with tf.name_scope(digits.GraphKeys.LOSS):
-                        for loss in self.get_tower_losses(tower_model):
-                            tf.add_to_collection(digits.GraphKeys.LOSSES, loss['loss'])
-
-                        # Assemble all made within this scope so far. The user can add custom
-                        # losses to the digits.GraphKeys.LOSSES collection
-                        losses = tf.get_collection(digits.GraphKeys.LOSSES, scope=scope_tower)
-                        losses += ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope=None)
-                        tower_loss = tf.add_n(losses, name='loss')
-
-                        self.summaries.append(tf.summary.scalar(tower_loss.op.name, tower_loss))
-
-                    # Reuse the variables in this scope for the next tower/device
-                    tf.get_variable_scope().reuse_variables()
-
-                    if self.stage == digits.STAGE_TRAIN:
-                        grad_tower_losses = []
-                        for loss in self.get_tower_losses(tower_model):
-                            grad_tower_loss = self.optimizer.compute_gradients(loss['loss'], loss['vars'])
-                            grad_tower_loss = tower_model.gradientUpdate(grad_tower_loss)
-                            grad_tower_losses.append(grad_tower_loss)
-                        grad_towers.append(grad_tower_losses)
+            current_scope = stage_scope if len(available_devices) == 1 else ('tower_%d' % dev_i)
+            with tf.name_scope(current_scope) as scope_tower:
+
+                if self.stage != digits.STAGE_INF:
+                tower_model = self.add_tower(obj_tower=obj_UserModel,
+                                 x=batch_x_split[dev_i],
+                                 y=batch_y_split[dev_i])
+                else:
+                tower_model = self.add_tower(obj_tower=obj_UserModel,
+                                 x=batch_x_split[dev_i],
+                                 y=None)
+
+                with tf.variable_scope(digits.GraphKeys.MODEL, reuse=dev_i > 0):
+                tower_model.inference  # touch to initialize
+
+                if self.stage == digits.STAGE_INF:
+                # For inferencing we will only use the inference part of the graph
+                continue
+
+                with tf.name_scope(digits.GraphKeys.LOSS):
+                for loss in self.get_tower_losses(tower_model):
+                    tf.add_to_collection(digits.GraphKeys.LOSSES, loss['loss'])
+
+                # Assemble all made within this scope so far. The user can add custom
+                # losses to the digits.GraphKeys.LOSSES collection
+                losses = tf.get_collection(digits.GraphKeys.LOSSES, scope=scope_tower)
+                losses += ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope=None)
+                tower_loss = tf.add_n(losses, name='loss')
+
+                self.summaries.append(tf.summary.scalar(tower_loss.op.name, tower_loss))
+
+                # Reuse the variables in this scope for the next tower/device
+                tf.get_variable_scope().reuse_variables()
+
+                if self.stage == digits.STAGE_TRAIN:
+                grad_tower_losses = []
+                for loss in self.get_tower_losses(tower_model):
+                    grad_tower_loss = self.optimizer.compute_gradients(loss['loss'], loss['vars'])
+                    grad_tower_loss = tower_model.gradientUpdate(grad_tower_loss)
+                    grad_tower_losses.append(grad_tower_loss)
+                grad_towers.append(grad_tower_losses)
 
         # Assemble and average the gradients from all towers
         if self.stage == digits.STAGE_TRAIN: