*** NOTICE ***
==============

The cuGraph repository has been refactored to make it more efficient to build, maintain and use.

Libraries supporting GNNs are now located in the `cugraph-gnn repository <https://github.com/rapidsai/cugraph-gnn>`_

* `pylibwholegraph <https://github.com/rapidsai/cugraph-gnn/tree/main/python/>`_ - the `Wholegraph <https://docs.rapids.ai/api/cugraph/nightly/wholegraph/>`_ library for client memory management supporting both cuGraph-DGL and cuGraph-PyG for even greater scalability
* `cugraph_dgl <https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_dgl.md>`_ enables the ability to use cugraph Property Graphs with Deep Graph Library (DGL)
* `cugraph_pyg <https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_pyg.md>`_ enables the ability to use cugraph Property Graphs with PyTorch Geometric (PyG).

`RAPIDS nx-cugraph <https://rapids.ai/nx-cugraph/>`_ is now located in the `nx-cugraph repository <https://github.com/rapidsai/nx-cugraph>`_ containing a backend to NetworkX for running supported algorithms with GPU acceleration.

The `cugraph-docs repository <https://github.com/rapidsai/cugraph-docs>`_ contains code to generate cuGraph documentation.

---

RAPIDS Graph documentation
==========================

.. image:: images/cugraph_logo_2.png
   :width: 600

~~~~~~~~~~~~
Introduction
~~~~~~~~~~~~
cuGraph is a library of graph algorithms that seamlessly integrates into the
RAPIDS data science ecosystem and allows data scientists to easily call
graph algorithms using data stored in cuDF/Pandas DataFrames or CuPy/SciPy
sparse matrices.


---------------------------
cuGraph Using NetworkX Code
---------------------------

cuGraph is now available as a NetworkX backend using `nx-cugraph <https://rapids.ai/nx-cugraph/>`_.
Our major integration effort with NetworkX offers NetworkX users a **zero code change** option to accelerate
their existing NetworkX code using an NVIDIA GPU and cuGraph.

Check out `zero code change accelerated NetworkX <nx_cugraph/index.rst>`_. If you would like to continue using standard cuGraph, then continue down below.

----------------------------
Getting started with cuGraph
----------------------------

Required hardware/software for `cuGraph and RAPIDS <https://docs.rapids.ai/install/#system-req>`_


++++++++++++
Installation
++++++++++++

Please see the latest `RAPIDS System Requirements documentation <https://docs.rapids.ai/install#system-req>`_.

This includes several ways to set up cuGraph

* On Unix

  * `Conda <https://docs.rapids.ai/install/#conda>`_
  * `Docker <https://docs.rapids.ai/install/#docker>`_
  * `pip <https://docs.rapids.ai/install/#pip>`_


**Note: Windows use of RAPIDS depends on prior installation of** `WSL2 <https://learn.microsoft.com/en-us/windows/wsl/install>`_.

* On Windows

  * `Conda <https://docs.rapids.ai/install#wsl2-conda>`_
  * `Docker <https://docs.rapids.ai/install#wsl2-docker>`_
  * `pip <https://docs.rapids.ai/install#wsl2-pip>`_

 Cugraph API Example

 .. code-block:: python

  import cugraph
  import cudf

  # Create an instance of the popular Zachary Karate Club graph
  from cugraph.datasets import karate
  G = karate.get_graph()

  # Call cugraph.degree_centrality
  vertex_bc = cugraph.degree_centrality(G)

 There are several resources containing cuGraph examples, the cuGraph `notebook repository <https://github.com/rapidsai/cugraph/blob/HEAD/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>`_ contains script examples of setting up and calling cuGraph algorithms.

 A simple example of `testing the degree centrality algorithm <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py>`_ is a good place to start. There are also `multi-GPU examples <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets as well.

----

~~~~~~~~~~~~~~~~~
Table of Contents
~~~~~~~~~~~~~~~~~

.. toctree::
   :maxdepth: 1

   basics/index
   nx_cugraph/index
   installation/index
   tutorials/index
   graph_support/index
   wholegraph/index
   references/index
   dev_resources/index
   api_docs/index

~~~~~~~~~~~~~~~~~~
Indices and tables
~~~~~~~~~~~~~~~~~~

* :ref:`genindex`
* :ref:`search`