fix link in docs

docs/conf.py

@@ -373,6 +373,7 @@ def autodoc_skip_member(app, what, name, obj, skip, options):
         'StagingInputWrapper',
         'StepTensorPrinter',
         'set_tower_func',
+        'TryResumeTraining',
 
         'guided_relu', 'saliency_map', 'get_scalar_var',
         'prediction_incorrect', 'huber_loss',

docs/tutorial/extend/callback.md

@@ -40,11 +40,11 @@ You can overwrite any of the following methods to define a new callback:
   If you're using a `TowerTrainer` instance, more tools are available:
 
   - Use `self.trainer.tower_func.towers` to access the
-  	[tower handles](../modules/tfutils.html#tensorpack.tfutils.tower.TowerTensorHandles),
+  	[tower handles](../../modules/tfutils.html#tensorpack.tfutils.tower.TowerTensorHandles),
   	and therefore the tensors in each tower.
-  - [self.get_tensors_maybe_in_tower()](../modules/callbacks.html#tensorpack.callbacks.Callback.get_tensors_maybe_in_tower)
+  - [self.get_tensors_maybe_in_tower()](../../modules/callbacks.html#tensorpack.callbacks.Callback.get_tensors_maybe_in_tower)
   	is a helper function to access tensors in the first training tower.
-  - [self.trainer.get_predictor()](../modules/train.html#tensorpack.train.TowerTrainer.get_predictor)
+  - [self.trainer.get_predictor()](../../modules/train.html#tensorpack.train.TowerTrainer.get_predictor)
   	is a helper function to create a callable under inference mode.
 
 * `_before_train(self)`

docs/tutorial/extend/dataflow.md

@@ -2,7 +2,7 @@
 ### Write a DataFlow
 
 There are several existing DataFlow, e.g. [ImageFromFile](../../modules/dataflow.html#tensorpack.dataflow.ImageFromFile),
-[DataFromList](../../http://tensorpack.readthedocs.io/en/latest/modules/dataflow.html#tensorpack.dataflow.DataFromList),
+[DataFromList](../../modules/dataflow.html#tensorpack.dataflow.DataFromList),
 which you can use if your data format is simple.
 In general, you probably need to write a source DataFlow to produce data for your task,
 and then compose it with existing modules (e.g. mapping, batching, prefetching, ...).

docs/tutorial/faq.md

@@ -20,11 +20,11 @@ Then it is a good time to open an issue.
 
 1. Learn `tf.Print`.
 
-2. Know [DumpTensors](http://tensorpack.readthedocs.io/en/latest/modules/callbacks.html#tensorpack.callbacks.DumpTensors[]),
-	[ProcessTensors](http://tensorpack.readthedocs.io/en/latest/modules/callbacks.html#tensorpack.callbacks.ProcessTensors) callbacks.
+2. Know [DumpTensors](../modules/callbacks.html#tensorpack.callbacks.DumpTensors[]),
+	[ProcessTensors](../modules/callbacks.html#tensorpack.callbacks.ProcessTensors) callbacks.
 	And it's also easy to write your own version of them.
 
-3. The [ProgressBar](http://tensorpack.readthedocs.io/en/latest/modules/callbacks.html#tensorpack.callbacks.ProgressBar)
+3. The [ProgressBar](../modules/callbacks.html#tensorpack.callbacks.ProgressBar)
 	 callback can print some scalar statistics, though not enabled by default.
 
 ## How to freeze some variables in training

docs/tutorial/save-load.md

@@ -20,11 +20,15 @@ It can dump the model to a `var-name: value` dict saved in npy/npz format.
 
 Model loading (in either training or testing) is through the `session_init` interface.
 Currently there are two ways a session can be restored:
-`session_init=SaverRestore(...)` which restores a
-TF checkpoint, or `session_init=DictRestore(...)` which restores a dict.
-(`get_model_loader` is a small helper to decide which one to use from a file name.)
+[session_init=SaverRestore(...)](../modules/tfutils.html#tensorpack.tfutils.sessinit.SaverRestore)
+which restores a TF checkpoint,
+or [session_init=DictRestore(...)](../modules/tfutils.html#tensorpack.tfutils.sessinit.DictRestore) which restores a dict
+([get_model_loader](../modules/tfutils.html#tensorpack.tfutils.sessinit.get_model_loader)
+is a small helper to decide which one to use from a file name).
+To load multiple models, use [ChainInit](../modules/tfutils.html#tensorpack.tfutils.sessinit.ChainInit)
 
-Variable restoring is completely based on name match between
+
+Variable restoring is completely based on __name match__ between
 variables in the current graph and variables in the `session_init` initializer.
 Variables that appear in only one side will be printed as warning.
 

docs/tutorial/trainer.md

@@ -34,7 +34,7 @@ The function needs to follow some conventions:
 
 ### MultiGPU Trainers
 
-For data-parallel multi-GPU training, different [multi-GPU trainers](http://tensorpack.readthedocs.io/en/latest/modules/train.html)
+For data-parallel multi-GPU training, different [multi-GPU trainers](../modules/train.html)
 implement different parallel logic.
 They take care of device placement, gradient averaging and synchronoization
 in the efficient way and all reach the same performance as the

tensorpack/tfutils/sessinit.py

@@ -264,6 +264,7 @@ def get_model_loader(filename):
 def TryResumeTraining():
     """
     Try loading latest checkpoint from ``logger.get_logger_dir()``, only if there is one.
+    Actually not very useful... better to write your own one.
 
     Returns:
         SessInit: either a :class:`JustCurrentSession`, or a :class:`SaverRestore`.