[GraphBolt] Move examples folder to root directory. (#7501)

docs/source/guide/minibatch-custom-sampler.rst

@@ -130,4 +130,4 @@ criteria, you could write your own transform function. Please check the method
 for reference.
 
 You could also refer to examples in
-`Link Prediction <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/link_prediction.py>`__.
+`Link Prediction <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/link_prediction.py>`__.

docs/source/guide/minibatch-inference.rst

@@ -122,7 +122,7 @@ of unlabeled data. Neighborhood sampling will work fine for model
 selection and validation.
 
 One can see
-`GraphSAGE <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/node_classification.py>`__
+`GraphSAGE <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/node_classification.py>`__
 and
-`RGCN <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/rgcn/hetero_rgcn.py>`__
+`RGCN <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/rgcn/hetero_rgcn.py>`__
 for examples of offline inference.

docs/source/guide/minibatch-link.rst

@@ -139,7 +139,7 @@ above.
 
 
 DGL provides the
-`unsupervised learning GraphSAGE <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/link_prediction.py>`__
+`unsupervised learning GraphSAGE <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/link_prediction.py>`__
 that shows an example of link prediction on homogeneous graphs.
 
 For heterogeneous graphs

docs/source/guide/minibatch-node.rst

@@ -162,7 +162,7 @@ customized batching iterator. During each iteration that yields
 
 
 DGL provides an end-to-end stochastic training example `GraphSAGE
-implementation <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/node_classification.py>`__.
+implementation <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/node_classification.py>`__.
 
 For heterogeneous graphs
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -247,6 +247,6 @@ dictionaries of node types and predictions here.
         opt.step()
 
 DGL provides an end-to-end stochastic training example `RGCN
-implementation <https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/rgcn/hetero_rgcn.py>`__.
+implementation <https://github.com/dmlc/dgl/blob/master/examples/graphbolt/rgcn/hetero_rgcn.py>`__.
 
 

docs/source/guide/minibatch-sparse.rst

@@ -171,4 +171,4 @@ Launch training:
             total_loss += loss.item()
 
 For more details, please refer to the `full example
-<https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/sparse/graphsage.py>`__.
+<https://github.com/dmlc/dgl/blob/master/examples/graphbolt/sparse/graphsage.py>`__.

examples/graphbolt/quickstart/README.md

@@ -4,8 +4,8 @@ Graphbolt provides all you need to create a dataloader to train a Graph Neural N
 
 ## Examples
 
- - The [node_classification.py](https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/quickstart/node_classification.py)
+ - The [node_classification.py](https://github.com/dmlc/dgl/blob/master/examples/graphbolt/quickstart/node_classification.py)
    shows how to create a Graphbolt dataloader to train a 2 layer Graph Convolutional Networks node
    classification model.
- - The [link_prediction.py](https://github.com/dmlc/dgl/blob/master/examples/sampling/graphbolt/quickstart/link_prediction.py)
+ - The [link_prediction.py](https://github.com/dmlc/dgl/blob/master/examples/graphbolt/quickstart/link_prediction.py)
    shows how to create a Graphbolt dataloader to train a 2 layer GraphSage link prediction model.

examples/legacy/README.md

@@ -1,3 +1,7 @@
+# New sampling examples via `dgl.graphbolt`
+Consider taking a look at our new sampling examples in the
+`../graphbolt` folder using `dgl.graphbolt`.
+
 # Sampling Examples Running
 
 ## Requirements

examples/multigpu/graphbolt/node_classification.py

@@ -5,7 +5,7 @@ data loader.
 
 Before reading this example, please familiar yourself with graphsage node
 classification using GtaphBolt data loader by reading the example in the
-`examples/sampling/graphbolt/node_classification.py`.
+`examples/graphbolt/node_classification.py`.
 
 For the usage of DDP provided by PyTorch, please read its documentation:
 https://pytorch.org/tutorials/beginner/dist_overview.html and

examples/pytorch/labor/README.md

@@ -7,7 +7,7 @@ paper with the GraphSAGE GNN model. The model can be changed to any other model
 NeighborSampler can be used.
 
 A more modern and performant version is provided in the
-`examples/sampling/graphbolt/pyg/labor` folder.
+`examples/graphbolt/pyg/labor` folder.
 
 Requirements
 ------------

tests/examples/test_sampling_examples.py

@@ -8,7 +8,6 @@ EXAMPLE_ROOT = os.path.join(
     "..",
     "..",
     "examples",
-    "sampling",
     "graphbolt",
     "quickstart",
 )