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Merge branch 'branch-25.06' into cuda-12.9.0-revert
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docs/cugraph-docs/source/api_docs/cugraph-dgl/cugraph_dgl.old

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docs/cugraph-docs/source/api_docs/cugraph-dgl/cugraph_dgl.rst

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docs/cugraph-docs/source/api_docs/index.rst

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:maxdepth: 3
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:caption: Graph Neural Networks API Documentation
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cugraph-dgl/cugraph_dgl.rst
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cugraph-pyg/cugraph_pyg.rst
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.. wholegraph/index.rst
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docs/cugraph-docs/source/basics/index.md

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docs/cugraph-docs/source/conf.py

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# General information about the project.
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project = 'cugraph-docs'
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copyright = '2024, NVIDIA Corporation'
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copyright = '2024-2025, NVIDIA Corporation'
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author = 'NVIDIA Corporation'
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# The version info for the project you're documenting, acts as replacement for
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# This patterns also effect to html_static_path and html_extra_path
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exclude_patterns = []
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suppress_warnings = ['duplicate_declaration.cpp']
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = 'sphinx'
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docs/cugraph-docs/source/dev_resources/contributing.md

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@@ -5,7 +5,7 @@ There are multiple ways to be involved and contribute to the cuGraph community,
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* [File an Issue](https://github.com/rapidsai/docs/issues/new)
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* [Implement a New Feature](https://docs.rapids.ai/contributing/code/#your-first-issue)
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* [Work on an Existing Issue](#F)
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* [Work on an Existing Issue](https://github.com/rapidsai/cugraph/issues)
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If you are ready to contribute, jump right to the [Contribute Code](https://docs.rapids.ai/contributing/issues/) section.
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## So you want to contribute code
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**TL;DR General Development Process**
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1. Read the documentation on [building from source](https://docs.rapids.ai/api/cugraph/nightly/installation/source_build/) to learn how to setup, and validate, the development environment
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2. Read the RAPIDS [Code of Conduct](https://docs.rapids.ai/resources/conduct/)
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3. Find or submit an issue to work on (include a comment that you are working issue)
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4. Fork the cuGraph [repo](#fork) and Code (make sure to add unit tests)!
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1. Read the project's README to learn how to setup the development environment
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2. Find an issue to work on. The best way is to look for the <span class="label" style="background: #7057ff; color: #ffffff; text-transform: none">good first issue</span> or <span class="label" style="background: #008672; color: #ffffff; text-transform: none">help wanted</span> labels
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3. Comment on the issue saying you are going to work on it
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4. Code! Make sure to update unit tests!
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5. All RAPIDS projects are released under the Apache-2.0 license, so also make sure all source files that support comments include a copyright and the Apache-2.0 license text.
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6. When done, and code passes local CI, create your pull request (PR)
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1. Ensure the code matches the [style guide](https://docs.rapids.ai/resources/style/)
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7. Verify that cuGraph CI passes all [status checks](https://help.github.com/articles/about-status-checks/). Fix if needed
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6. When done, [create your pull request]({% link contributing/prs.md %})
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7. Verify that CI passes all [status checks](https://help.github.com/articles/about-status-checks/). Fix if needed
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8. Wait for other developers to review your code and update code as needed
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9. PR will require the proper [tags](https://docs.rapids.ai/resources/label-checker/) be added by someone with repository permission.
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10. Once reviewed and approved, a RAPIDS developer will merge your pull request
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9. Once reviewed and approved, a RAPIDS developer will merge your pull request
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Remember, if you are unsure about anything, don't hesitate to comment on issues
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and ask for clarifications!
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1. commit your code
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```git push```
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6. From the GitHub web page, open a Pull Request
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1. follow the Pull Request [tagging policy](./PRTAGS.md)
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1. follow the Pull Request in the repository: https://github.com/rapidsai/```<REPO NAME>```/pulls
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### Development Environment
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docs/cugraph-docs/source/graph_support/DGL_support.md

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docs/cugraph-docs/source/graph_support/cugraph_cpp_support.md

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* Prefer algorithms from STL and Thrust to raw loops.
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* Prefer cugraph and RMM to raw pointers and raw memory allocation.
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Documentation is discussed in the [Documentation Guide](TODO).
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### Includes
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The following guidelines apply to organizing `#include` lines.

docs/cugraph-docs/source/graph_support/gnn_support.rst

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Graph Neural Network Support
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============================
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**cuGraph-DGL has been removed from cuGraph GNN as of release 25.06.** We recommend migrating to
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cuGraph-PyG, which offers the same functionality along with additional features like support for heterogeneous sampling and a unified API.
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The cuGraph team is not planning any further work in the DGL ecosystem going forward.
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----
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The cuGraph GNN stack
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.. image:: ../../_static/cugraph-pyg-stack.png
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:alt: The cuGraph-
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:width: 400px
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:align: center
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----
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Here is a talk that explains `Training GNNs at Internet Scale using cuGraph and WholeGraph <https://www.youtube.com/watch?v=rS-bcWm3jQo&t=2762s>`_
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.. toctree::
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:maxdepth: 2
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PyG_support.md
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DGL_support.md
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wholegraph_support.md
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Blogs to explain how RAPIDS cuGraph supports GNN'S

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