Add sphinx-lint pre-commit (and some docs fixes)

.pre-commit-config.yaml

@@ -49,6 +49,7 @@ repos:
     rev: 24.10.0
     hooks:
       - id: black
+      # - id: black-jupyter  # Consider adding this
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.7.3
     hooks:
@@ -108,6 +109,11 @@ repos:
         language: python
         pass_filenames: false
         additional_dependencies: ["networkx>=3.4"]
+  - repo: https://github.com/sphinx-contrib/sphinx-lint
+    rev: v1.0.0
+    hooks:
+      - id: sphinx-lint
+        args: ["--enable=all", "--disable=line-too-long"]
   - repo: https://github.com/rapidsai/pre-commit-hooks
     rev: v0.4.0
     hooks:

docs/nx-cugraph/source/index.rst

@@ -23,7 +23,7 @@ Required hardware/software for cuGraph and `RAPIDS <https://docs.rapids.ai/user-
  * NVIDIA GPU, Volta architecture or later, with `compute capability <https://developer.nvidia.com/cuda-gpus> 7.0+`_
  * CUDA 11.2-11.8, 12.0-12.5
  * Python version 3.10, 3.11, or 3.12
- * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. NetworkX version 3.4 or newer is recommended. (`see below <#cugraph-using-networkx-code>`).
+ * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. NetworkX version 3.4 or newer is recommended. (`see below <#cugraph-using-networkx-code>`_).
 
 Installation
 The latest RAPIDS System Requirements documentation is located `here <https://docs.rapids.ai/install#system-req>`_.
@@ -69,7 +69,7 @@ their existing NetworkX code using an NVIDIA GPU and cuGraph.
 
 There are several resources containing cuGraph examples, `the cuGraph notebook repository <https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md>`_
 has many examples of loading graph data and running algorithms in Jupyter notebooks.
-The `cuGraph test code <https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests>_` contain python scripts setting up and calling cuGraph algorithms.
+The `cuGraph test code <https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests>`_ contain python scripts setting up and calling cuGraph algorithms.
 A simple example of `testing the degree centrality algorithm <https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py>`_
 is a good place to start. Some of these show `multi-GPU tests/examples <https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets as well.
 

docs/nx-cugraph/source/nx_cugraph/index.rst

@@ -30,7 +30,7 @@ By simply `installing and enabling nx-cugraph <https://github.com/rapidsai/cugra
 +------------------------------------------------------------------------------------------------------------------------+
 | **Run the same code on CPU or GPU**                                                                                    |
 |                                                                                                                        |
-| Nothing changes, not even your `import` statements, when going from CPU to GPU.                                        |
+| Nothing changes, not even your ``import`` statements, when going from CPU to GPU.                                      |
 +------------------------------------------------------------------------------------------------------------------------+
 
 

docs/nx-cugraph/source/nx_cugraph/supported-algorithms.rst

@@ -180,7 +180,7 @@ Algorithms
 +---------------------------------------+
 
 +---------------------------+
-| **Traversal**       		|
+| **Traversal**             |
 +===========================+
 | bfs_edges                 |
 +---------------------------+