[Doc] fix some document warnings (dmlc#645)

* fix doc

* fix some format and warnings

* fix

Author: jermainewang

docs/source/api/python/graph_store.rst

@@ -7,7 +7,7 @@ Graph Store -- Graph for multi-processing and distributed training
 .. autoclass:: SharedMemoryDGLGraph
 
 Querying the distributed setting
-------------------------
+--------------------------------
 
 .. autosummary::
     :toctree: ../../generated/
@@ -26,7 +26,7 @@ Using Node/edge features
     SharedMemoryDGLGraph.init_edata
 
 Computing with Graph store
------------------------
+--------------------------
 
 .. autosummary::
     :toctree: ../../generated/

docs/source/api/python/nodeflow.rst

@@ -1,7 +1,7 @@
 .. _apinodeflow:
 
 NodeFlow -- Graph sampled from a large graph
-=========================================
+============================================
 
 .. currentmodule:: dgl
 .. autoclass:: NodeFlow

python/dgl/graph.py

@@ -3323,25 +3323,27 @@ def __repr__(self):
 
     # pylint: disable=invalid-name
     def to(self, ctx):
-        """
-        Move both ndata and edata to the targeted mode (cpu/gpu)
+        """Move both ndata and edata to the targeted mode (cpu/gpu)
         Framework agnostic
 
         Parameters
         ----------
-        ctx : framework specific context object
+        ctx : framework-specific context object
+            The context to move data to.
 
-        Examples (Pytorch & MXNet)
+        Examples
         --------
-        >>> import backend as F
+        The following example uses PyTorch backend.
+
+        >>> import torch
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(5, {'h': torch.ones((5, 2))})
         >>> G.add_edges([0, 1], [1, 2], {'m' : torch.ones((2, 2))})
         >>> G.add_edges([0, 1], [1, 2], {'m' : torch.ones((2, 2))})
-        >>> G.to(F.cuda())
-
+        >>> G.to(torch.device('cuda:0'))
         """
         for k in self.ndata.keys():
             self.ndata[k] = F.copy_to(self.ndata[k], ctx)
         for k in self.edata.keys():
             self.edata[k] = F.copy_to(self.edata[k], ctx)
+    # pylint: enable=invalid-name

python/dgl/nodeflow.py

@@ -558,12 +558,17 @@ def block_incidence_matrix(self, block_id, typestr, ctx):
         or not.
 
         There are two types of an incidence matrix `I`:
-        * "in":
-          - I[v, e] = 1 if e is the in-edge of v (or v is the dst node of e);
-          - I[v, e] = 0 otherwise.
-        * "out":
-          - I[v, e] = 1 if e is the out-edge of v (or v is the src node of e);
-          - I[v, e] = 0 otherwise.
+
+        * ``in``:
+
+            - I[v, e] = 1 if e is the in-edge of v (or v is the dst node of e);
+            - I[v, e] = 0 otherwise.
+
+        * ``out``:
+
+            - I[v, e] = 1 if e is the out-edge of v (or v is the src node of e);
+            - I[v, e] = 0 otherwise.
+
         "both" isn't defined in the block of a NodeFlow.
 
         Parameters

tutorials/models/5_giant_graph/1_sampling_mx.py

@@ -1,5 +1,5 @@
 """
-.. _sampling:
+.. _model-sampling:
 
 NodeFlow and Sampling
 =======================================

tutorials/models/5_giant_graph/2_giant.py

@@ -1,5 +1,5 @@
 """
-.. _sampling:
+.. _model-graph-store:
 
 Large-Scale Training of Graph Neural Networks
 =============================================