misc fix

examples/README.md

@@ -4,7 +4,6 @@
 Examples with __reproducible__ and meaningful performancce.
 
 + [An illustrative mnist example](mnist-convnet.py)
-+ [A small Cifar10 ConvNet with 91% accuracy](cifar-convnet.py)
 + [A tiny SVHN ConvNet with 97.5% accuracy](svhn-digit-convnet.py)
 + [Reproduce some reinforcement learning papers](Atari2600)
 + [char-rnn for fun](char-rnn)

tensorpack/callbacks/common.py

@@ -81,9 +81,10 @@ because {} will be saved".format(v.name, var_dict[name].name))
             logger.exception("Exception in ModelSaver.trigger_epoch!")
 
 class MinSaver(Callback):
-    def __init__(self, monitor_stat, reverse=True):
+    def __init__(self, monitor_stat, reverse=True, filename=None):
         self.monitor_stat = monitor_stat
         self.reverse = reverse
+        self.filename = filename
         self.min = None
 
     def _get_stat(self):
@@ -107,7 +108,8 @@ class MinSaver(Callback):
                 "Cannot find a checkpoint state. Do you forget to use ModelSaver?")
         path = chpt.model_checkpoint_path
         newname = os.path.join(logger.LOG_DIR,
-                'max-' if self.reverse else 'min-' + self.monitor_stat)
+                self.filename or
+                ('max-' if self.reverse else 'min-' + self.monitor_stat + '.tfmodel'))
         shutil.copy(path, newname)
         logger.info("Model with {} '{}' saved.".format(
             'maximum' if self.reverse else 'minimum', self.monitor_stat))

tensorpack/train/config.py

@@ -63,6 +63,8 @@ class TrainConfig(object):
         assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))
 
     def set_tower(self, nr_tower=None, tower=None):
+        logger.warn("config.set_tower is deprecated. set config.tower or config.nr_tower directly")
+        # this is a deprecated function
         assert nr_tower is None or tower is None, "Cannot set both nr_tower and tower!"
         if nr_tower:
             tower = list(range(nr_tower))

tensorpack/train/multigpu.py

@@ -22,6 +22,7 @@ class MultiGPUTrainer(QueueInputTrainer):
     """ Base class for multi-gpu training"""
     def __init__(self, config, input_queue=None, predict_tower=None):
         super(MultiGPUTrainer, self).__init__(config, input_queue, predict_tower)
+        assert len(config.tower) >= 1, "MultiGPUTrainer must be used with at least one GPU."
         self.dequed_inputs = []
 
     @staticmethod