fix use of nr_tower (fix #1077)

examples/A3C-Gym/train-atari.py

@@ -206,6 +206,7 @@ class MySimulatorMaster(SimulatorMaster, Callback):
 
 
 def train():
+    assert tf.test.is_gpu_available(), "Training requires GPUs!"
     dirname = os.path.join('train_log', 'train-atari-{}'.format(ENV_NAME))
     logger.set_logger_dir(dirname)
 
@@ -259,7 +260,7 @@ def train():
         session_init=get_model_loader(args.load) if args.load else None,
         max_epoch=1000,
     )
-    trainer = SimpleTrainer() if config.nr_tower == 1 else AsyncMultiGPUTrainer(train_tower)
+    trainer = SimpleTrainer() if num_gpu == 1 else AsyncMultiGPUTrainer(train_tower)
     launch_train_with_config(config, trainer)
 
 

examples/boilerplate.py

@@ -71,8 +71,6 @@ if __name__ == '__main__':
 
     config = get_config()
 
-    if args.gpu:
-        config.nr_tower = len(args.gpu.split(','))
     if args.load:
         config.session_init = SaverRestore(args.load)
 

tensorpack/train/base.py

@@ -104,12 +104,12 @@ class Trainer(object):
     The ``tf.Session`` object the trainer is using.
     Available after :meth:`initialize()`.
 
-    Using ``trainer.sess.run`` to evaluate tensors that depend on the inputs
-    can lead to unexpected effect:
+    Using ``trainer.sess.run`` to evaluate tensors that depend on the training
+    ``InputSource`` may have unexpected effect:
 
     For example, if you use ``trainer.sess.run`` to evaluate a tensor that depends on the
     inputs coming from a ``StagingArea``,
-    this will take a datapoint from the ``StagingArea``, making the ``StagingArea`` empty, and as a result
+    it will take a datapoint from the ``StagingArea``, making the ``StagingArea`` empty, and as a result
     make the training hang.
     """
 

tensorpack/train/trainers.py

@@ -137,7 +137,7 @@ class AsyncMultiGPUTrainer(SingleCostTrainer):
     def _setup_graph(self, input, get_cost_fn, get_opt_fn):
         if len(self.devices) > 1:
             assert isinstance(input, FeedfreeInput), input
-        tower_fn = self._make_get_grad_fn(input, get_cost_fn, get_opt_fn),
+        tower_fn = self._make_get_grad_fn(input, get_cost_fn, get_opt_fn)
         grad_list = self._builder.call_for_each_tower(tower_fn)
         self.train_op = self._builder.build(grad_list, get_opt_fn)
         return []