Remove examples/multi_gpu since all deprecated, point to CuGraph

CHANGELOG.md

@@ -76,6 +76,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Cleaned up examples folder in regards to multi-gpu scaling ([#10489](https://github.com/pyg-team/pytorch_geometric/pull/10489))
 - Adapt `dgcnn_classification` example to work with `ModelNet` and `MedShapeNet` Datasets ([#9823](https://github.com/pyg-team/pytorch_geometric/pull/9823))
 - Chained exceptions explicitly instead of implicitly ([#10242](https://github.com/pyg-team/pytorch_geometric/pull/10242))
 - Updated cuGraph examples to use buffered sampling which keeps data in memory and is significantly faster than the deprecated buffered sampling ([#10079](https://github.com/pyg-team/pytorch_geometric/pull/10079))

examples/README.md

@@ -3,6 +3,8 @@
 This folder contains a plethora of examples covering different GNN use-cases.
 This readme highlights some key examples.
 
+Note: We recommend the [NVIDIA PyG Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg/tags) for best results and easiest setup with NVIDIA GPUs.
+
 A great and simple example to start with is [`gcn.py`](./gcn.py), showing a user how to train a [`GCN`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.GCN.html) model for node-level prediction on small-scale homogeneous data.
 
 For a simple GNN based link prediction example, see [`link_pred.py`](./link_pred.py).
@@ -11,8 +13,6 @@ For an improved GNN based link prediction approach using Attract-Repel embedding
 
 To see an example for doing link prediction with an advanced Graph Transformer called [`LPFormer`](https://arxiv.org/abs/2310.11009), see \[`lpformer.py`\].
 
-For examples on [Open Graph Benchmark](https://ogb.stanford.edu/) datasets, see the `ogbn_*.py` examples:
-
 - [`ogbn_train.py`](./ogbn_train.py) is an example for training a GNN on the large-scale `ogbn-papers100m` dataset, containing approximately ~1.6B edges or the medium scale `ogbn-products` dataset, ~62M edges.
   - Uses SGFormer (a kind of GraphTransformer) by default.
   - [SGFormer Paper](https://arxiv.org/pdf/2306.10759)
@@ -26,10 +26,18 @@ For an example on [Relational Deep Learning](https://arxiv.org/abs/2312.04615) w
 
 For examples on using `torch.compile`, see the examples under [`examples/compile`](./compile).
 
-For examples on scaling PyG up via multi-GPUs, see the examples under [`examples/multi_gpu`](./multi_gpu).
-
 For examples on working with heterogeneous data, see the examples under [`examples/hetero`](./hetero).
 
 For examples on co-training LLMs with GNNs, see the examples under [`examples/llm`](./llm).
 
 - [Kumo.ai x NVIDIA GNN+LLM Webinar](https://www.youtube.com/watch?v=uRIA8e7Y_vs)
+
+### Scale to Trillions of Edges with cuGraph
+
+[cuGraph](https://github.com/rapidsai/cugraph) is a collection of packages focused on GPU-accelerated graph analytics including support for property graphs and scaling up to thousands of GPUs. cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms. It is part of the [RAPIDS](https://rapids.ai) accelerated data science framework.
+
+[cuGraph GNN](https://github.com/rapidsai/cugraph-gnn) is a collection of GPU-accelerated plugins that support PyTorch and PyG natively through the _cuGraph-PyG_ and _WholeGraph_ subprojects. cuGraph GNN is built on top of cuGraph, leveraging its low-level [pylibcugraph](https://github.com/rapidsai/cugraph/python/pylibcugraph) API and C++ primitives for sampling and other GNN operations ([libcugraph](https://github.com/rapidai/cugraph/python/libcugraph)). It also includes the `libwholegraph` and `pylibwholegraph` libraries for high-performance distributed edgelist and embedding storage. Users have the option of working with these lower-level libraries directly, or through the higher-level API in cuGraph-PyG that directly implements the `GraphStore`, `FeatureStore`, `NodeLoader`, and `LinkLoader` interfaces.
+
+Complete documentation on RAPIDS graph packages, including `cugraph`, `cugraph-pyg`, `pylibwholegraph`, and `pylibcugraph` is available on the [RAPIDS docs pages](https://docs.rapids.ai/api/cugraph/nightly/graph_support).
+
+See [`rapidsai/cugraph-gnn/tree/branch-25.12/python/cugraph-pyg/cugraph_pyg/examples` on GitHub](https://github.com/rapidsai/cugraph-gnn/tree/branch-25.12/python/cugraph-pyg/cugraph_pyg/examples) for fully scalable PyG example workflows.