update docs

docs/Makefile

@@ -8,7 +8,7 @@ SPHINXPROJ    = tensorpack
 SOURCEDIR     = .
 BUILDDIR      = build
 
-.PHONY: help Makefile docset
+.PHONY: help Makefile docset clean
 
 all: html
 
@@ -24,3 +24,6 @@ docset: html
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 html: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+clean:
+	rm -rf build

tensorpack/callbacks/prof.py

@@ -169,7 +169,7 @@ def _write_event(self, metadata):
 class PeakMemoryTracker(Callback):
     """
     Track peak memory in each session run, by
-    :module:`tf.contrib.memory_stats`.
+    :mod:`tf.contrib.memory_stats`.
     It can only be used for GPUs.
     """
     def __init__(self, devices=['/gpu:0']):

tensorpack/graph_builder/distributed.py

@@ -173,11 +173,15 @@ def build(self, input, get_cost_fn, get_opt_fn):
             get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
 
         Returns:
-            tf.Operation: the training op
-            tf.Operation: the op which sync all the local variables from PS.
-                This op sholud be run before training.
-            tf.Operation: the op which sync all the local `MODEL_VARIABLES` from PS.
-                You can choose how often to run it by yourself.
+            (tf.Operation, tf.Operation, tf.Operation):
+
+            1. the training op.
+
+            2. the op which sync all the local variables from PS.
+            This op sholud be run before training.
+
+            3. the op which sync all the local `MODEL_VARIABLES` from PS.
+            You can choose how often to run it by yourself.
         """
         with override_to_local_variable():
             get_global_step_var()

tensorpack/graph_builder/training.py

@@ -272,8 +272,11 @@ def build(self, input, get_cost_fn, get_opt_fn):
             get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
 
         Returns:
-            tf.Operation: the training op.
-            tf.Operation: the op which sync variables from GPU 0 to other GPUs.
+            (tf.Operation, tf.Operation)
+
+            1. the training op.
+
+            2. the op which sync variables from GPU 0 to other GPUs.
                 It has to be run before the training has started.
                 And you can optionally run it later to sync non-trainable variables.
         """

tensorpack/input_source/input_source.py

@@ -25,7 +25,7 @@
 __all__ = ['PlaceholderInput', 'FeedInput', 'DataParallelFeedInput',
            'FeedfreeInput',
            'QueueInput', 'BatchQueueInput',
-           'ZMQInput', 'DummyConstantInput', 'TensorInput',
+           'DummyConstantInput', 'TensorInput',
            'TFDatasetInput',
            'StagingInputWrapper']
 

tensorpack/input_source/input_source_base.py

@@ -101,18 +101,23 @@ def _setup(self, inputs_desc):
         pass
 
     def setup_done(self):
+        """
+        Returns:
+            bool: whether :meth:`setup()` has been called.
+        """
         return self._setup_done
 
     @memoized
     def get_callbacks(self):
         """
         An InputSource might need some extra maintainance during training,
         which is done also through the Callback interface.
-        This method returns the Callbacks and the return value will be memoized.
+        This method returns the callbacks and the return value will be memoized.
 
         Returns:
             list[Callback]: extra callbacks needed by this InputSource.
         """
+        assert self.setup_done()
         return [CallbackFactory(
             before_train=lambda _: self.reset_state())] + self._get_callbacks()
 

tensorpack/train/base.py

@@ -34,6 +34,7 @@ class TrainLoop(object):
     """
     Manage the double for loop.
     """
+
     def __init__(self):
         self._epoch_num = 0
         self._global_step = 0
@@ -82,7 +83,7 @@ def global_step(self):
     @property
     def local_step(self):
         """
-        The number of (tensorpack) steps that have finished in the current epoch.
+        The number of steps that have finished in the current epoch.
         """
         return self._local_step
 
@@ -97,9 +98,12 @@ class Trainer(object):
         hooked_sess (tf.train.MonitoredSession): the session with hooks.
         monitors (Monitors): the monitors. Other callbacks can use it for logging.
     """
-    # step attr only available after before_train?
 
     is_chief = True
+    """
+    Whether this process is the chief worker in distributed training.
+    Only chief worker will run some callbacks.
+    """
 
     def __init__(self, config):
         """
@@ -283,17 +287,22 @@ def vs_name_for_predictor(self):
         return ""
 
 
-def _delegate_attr(name):
+def _get_property(name):
     """
     Delegate property to self.loop
     """
-    setattr(Trainer, name, property(
-        lambda self: getattr(self.loop, name)))
+    ret = property(
+        lambda self: getattr(self.loop, name))
+    try:
+        ret.__doc__ = getattr(TrainLoop, name).__doc__
+    except AttributeError:
+        pass
+    return ret
 
 
 for name in ['global_step', 'local_step', 'steps_per_epoch',
              'epoch_num', 'starting_epoch', 'max_epoch']:
-    _delegate_attr(name)
+    setattr(Trainer, name, _get_property(name))
 
 
 def launch_train(