+
+2. Follow the instructions to build and activate an environment
+
+4. Install the latest `nx-cugraph` by following the [Installation Guide](installation.md)
+
+5. Follow the instructions written in the README [here](https://github.com/rapidsai/nx-cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/README.md)
diff --git a/docs/cugraph-docs/source/nx_cugraph/how-it-works.md b/docs/cugraph-docs/source/nx_cugraph/how-it-works.md
new file mode 100644
index 0000000..0061b04
--- /dev/null
+++ b/docs/cugraph-docs/source/nx_cugraph/how-it-works.md
@@ -0,0 +1,113 @@
+# How it Works
+
+NetworkX has the ability to **dispatch function calls to separately-installed third-party backends**.
+
+NetworkX backends let users experience improved performance and/or additional functionality without changing their NetworkX Python code. Examples include backends that provide algorithm acceleration using GPUs, parallel processing, graph database integration, and more.
+
+While NetworkX is a pure-Python implementation, backends may be written to use other libraries and even specialized hardware. `nx-cugraph` is a NetworkX backend that uses RAPIDS cuGraph and NVIDIA GPUs to significantly improve NetworkX performance.
+
+
+
+## Enabling nx-cugraph
+
+It is recommended to use `networkx>=3.4` for optimal zero code change performance, but `nx-cugraph` will also work with `networkx 3.2+`.
+
+NetworkX will use `nx-cugraph` as the backend if any of the following are used:
+
+### `NX_CUGRAPH_AUTOCONFIG` environment variable.
+
+The `NX_CUGRAPH_AUTOCONFIG` environment variable can be used to configure NetworkX for full zero code change acceleration using `nx-cugraph`. If a NetworkX function is called that `nx-cugraph` supports, NetworkX will redirect the function call to `nx-cugraph` automatically, or fall back to either another backend if enabled or the default NetworkX implementation. See the [NetworkX documentation on backends](https://networkx.org/documentation/stable/reference/backends.html) for configuring NetworkX manually.
+
+```
+bash> NX_CUGRAPH_AUTOCONFIG=True python my_networkx_script.py
+```
+
+### `backend=` keyword argument
+
+To explicitly specify a particular backend for an API, use the `backend=`
+keyword argument. This argument takes precedence over the
+`NX_CUGRAPH_AUTOCONFIG` environment variable. This requires anyone
+running code that uses the `backend=` keyword argument to have the specified
+backend installed.
+
+Example:
+```python
+nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph")
+```
+
+### Type-based dispatching
+
+NetworkX also supports automatically dispatching to backends associated with
+specific graph types. Like the `backend=` keyword argument example above, this
+requires the user to write code for a specific backend, and therefore requires
+the backend to be installed, but has the advantage of ensuring a particular
+behavior without the potential for runtime conversions.
+
+To use type-based dispatching with `nx-cugraph`, the user must import the backend
+directly in their code to access the utilities provided to create a Graph
+instance specifically for the `nx-cugraph` backend.
+
+Example:
+```python
+import networkx as nx
+import nx_cugraph as nxcg
+
+G = nx.Graph()
+
+# populate the graph
+# ...
+
+nxcg_G = nxcg.from_networkx(G) # conversion happens once here
+nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend
+ # to be used, no conversion necessary
+```
+
+## Command Line Example
+
+---
+
+Create `bc_demo.ipy` and paste the code below.
+
+```python
+import pandas as pd
+import networkx as nx
+
+url = "https://data.rapids.ai/cugraph/datasets/cit-Patents.csv"
+df = pd.read_csv(url, sep=" ", names=["src", "dst"], dtype="int32")
+G = nx.from_pandas_edgelist(df, source="src", target="dst")
+
+%time result = nx.betweenness_centrality(G, k=10)
+```
+Run the command:
+```
+user@machine:/# ipython bc_demo.ipy
+
+CPU times: user 7min 36s, sys: 5.22 s, total: 7min 41s
+Wall time: 7min 41s
+```
+
+You will observe a run time of approximately 7 minutes...more or less depending on your CPU.
+
+Run the command again, this time specifying cugraph as the NetworkX backend.
+```bash
+user@machine:/# NX_CUGRAPH_AUTOCONFIG=True ipython bc_demo.ipy
+
+CPU times: user 4.14 s, sys: 1.13 s, total: 5.27 s
+Wall time: 5.32 s
+```
+This run will be much faster, typically around 5 seconds depending on your GPU.
+
+
+
+*Note, the examples above were run using the following specs*:
+
+ *NetworkX 3.4*
+ *nx-cugraph 24.10*
+ *CPU: Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz 45GB RAM*
+ *GPU: NVIDIA Quadro RTX 8000 80GB RAM*
+
+
+
+---
+
+The latest list of algorithms supported by `nx-cugraph` can be found in [GitHub](https://github.com/rapidsai/nx-cugraph/blob/HEAD/README.md#supported-algorithms), or in the [Supported Algorithms Section](supported-algorithms.md).
diff --git a/docs/cugraph-docs/source/nx_cugraph/index.rst b/docs/cugraph-docs/source/nx_cugraph/index.rst
index ef6f516..0eb8907 100644
--- a/docs/cugraph-docs/source/nx_cugraph/index.rst
+++ b/docs/cugraph-docs/source/nx_cugraph/index.rst
@@ -1,9 +1,66 @@
-===============================
-nxCugraph as a NetworkX Backend
-===============================
+nx-cugraph
+-----------
+``nx-cugraph`` is a NetworkX backend that provides **GPU acceleration** to many popular NetworkX algorithms.
+
+By simply `installing and enabling nx-cugraph `_, users can see significant speedup on workflows where performance is hindered by the default NetworkX implementation.
+
+Users can have GPU-based, large-scale performance **without** changing their familiar and easy-to-use NetworkX code.
+
+.. centered:: Timed result from running the following code snippet (called ``demo.ipy``, showing NetworkX with vs. without ``nx-cugraph``)
+
+.. code-block:: python
+
+ import pandas as pd
+ import networkx as nx
+
+ url = "https://data.rapids.ai/cugraph/datasets/cit-Patents.csv"
+ df = pd.read_csv(url, sep=" ", names=["src", "dst"], dtype="int32")
+ G = nx.from_pandas_edgelist(df, source="src", target="dst")
+
+ %time result = nx.betweenness_centrality(G, k=10)
+
+
+::
+
+ user@machine:/# ipython demo.ipy
+ CPU times: user 7min 36s, sys: 5.22 s, total: 7min 41s
+ Wall time: 7min 41s
+
+
+::
+
+ user@machine:/# NX_CUGRAPH_AUTOCONFIG=True ipython demo.ipy
+ CPU times: user 4.14 s, sys: 1.13 s, total: 5.27 s
+ Wall time: 5.32 s
+
+
+.. figure:: ../_static/colab.png
+ :width: 200px
+ :target: https://nvda.ws/4drM4re
+
+ Try it on Google Colab!
+
+
++--------------------------------------------------------------------------------------------------------+
+| **Zero Code Change Acceleration** |
+| |
+| Just set the environment variable ``NX_CUGRAPH_AUTOCONFIG=True`` to enable ``nx-cugraph`` in NetworkX. |
++--------------------------------------------------------------------------------------------------------+
+| **Run the same code on CPU or GPU** |
+| |
+| Nothing changes, not even your ``import`` statements, when going from CPU to GPU. |
++--------------------------------------------------------------------------------------------------------+
+
+
+``nx-cugraph`` is now Generally Available (GA) as part of the ``RAPIDS`` package. See `RAPIDS
+Quick Start `_ to get up-and-running with ``nx-cugraph``.
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
+ :caption: Contents:
- nx_cugraph.md
+ how-it-works
+ installation
+ supported-algorithms
+ benchmarks
diff --git a/docs/cugraph-docs/source/nx_cugraph/installation.md b/docs/cugraph-docs/source/nx_cugraph/installation.md
new file mode 100644
index 0000000..9675306
--- /dev/null
+++ b/docs/cugraph-docs/source/nx_cugraph/installation.md
@@ -0,0 +1,50 @@
+# Installing nx-cugraph
+
+This guide describes how to install ``nx-cugraph`` and use it in your workflows.
+
+
+## System Requirements
+
+`nx-cugraph` requires the following:
+
+ - **Volta architecture or later NVIDIA GPU, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+**
+ - **[CUDA](https://docs.nvidia.com/cuda/index.html) 11.2, 11.4, 11.5, 11.8, 12.0, 12.2, or 12.5**
+ - **Python >= 3.10**
+ - **[NetworkX](https://networkx.org/documentation/stable/install.html#) >= 3.2 (version 3.4 or higher recommended)**
+
+More details about system requirements can be found in the [RAPIDS System Requirements Documentation](https://docs.rapids.ai/install#system-req).
+
+## Installing Packages
+
+Read the [RAPIDS Quick Start Guide](https://docs.rapids.ai/install) to learn more about installing all RAPIDS libraries.
+
+`nx-cugraph` can be installed using conda or pip. It is included in the RAPIDS metapackage, or can be installed separately.
+
+### Conda
+**Nightly version**
+```bash
+conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph
+```
+
+**Stable version**
+```bash
+conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph
+```
+
+### pip
+**Nightly version**
+```bash
+pip install nx-cugraph-cu11 --extra-index-url https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+```
+
+**Stable version**
+```bash
+pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
+```
+
+
+
+**Note:**
+ - The `pip install` examples above are for CUDA 11. To install for CUDA 12, replace `-cu11` with `-cu12`
+
+
diff --git a/docs/cugraph-docs/source/nx_cugraph/nx_cugraph.md b/docs/cugraph-docs/source/nx_cugraph/nx_cugraph.md
index ab0d057..900362a 100644
--- a/docs/cugraph-docs/source/nx_cugraph/nx_cugraph.md
+++ b/docs/cugraph-docs/source/nx_cugraph/nx_cugraph.md
@@ -1,20 +1,11 @@
### nx_cugraph
-Whereas previous versions of cuGraph have included mechanisms to make it
-trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph
-is now a [networkX backend]().
-The user now need only [install nx-cugraph]()
-to experience GPU speedups.
+`nx-cugraph` is a [networkX backend]() that accelerates many popular NetworkX functions using cuGraph and NVIDIA GPUs.
+Users simply [install and enable nx-cugraph](installation.md) to experience GPU speedups.
Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph.
-Each chart has three measurements.
-* NX - running the algorithm natively with networkX on CPU.
-* nx-cugraph - running with GPU accelerated networkX achieved by simply calling the cugraph backend. This pays the overhead of building the GPU resident object for each algorithm called. This achieves significant improvement but stil isn't compleltely optimum.
-* nx-cugraph (preconvert) - This is a bit more complicated since it involves building (precomputing) the GPU resident graph ahead and reusing it for each algorithm.
-
-
@@ -23,143 +14,3 @@ Each chart has three measurements.
-
-
-The following algorithms are supported and automatically dispatched to nx-cuGraph for acceleration.
-
-#### Algorithms
-```
-bipartite
- ├─ basic
- │ └─ is_bipartite
- └─ generators
- └─ complete_bipartite_graph
-centrality
- ├─ betweenness
- │ ├─ betweenness_centrality
- │ └─ edge_betweenness_centrality
- ├─ degree_alg
- │ ├─ degree_centrality
- │ ├─ in_degree_centrality
- │ └─ out_degree_centrality
- ├─ eigenvector
- │ └─ eigenvector_centrality
- └─ katz
- └─ katz_centrality
-cluster
- ├─ average_clustering
- ├─ clustering
- ├─ transitivity
- └─ triangles
-community
- └─ louvain
- └─ louvain_communities
-components
- ├─ connected
- │ ├─ connected_components
- │ ├─ is_connected
- │ ├─ node_connected_component
- │ └─ number_connected_components
- └─ weakly_connected
- ├─ is_weakly_connected
- ├─ number_weakly_connected_components
- └─ weakly_connected_components
-core
- ├─ core_number
- └─ k_truss
-dag
- ├─ ancestors
- └─ descendants
-isolate
- ├─ is_isolate
- ├─ isolates
- └─ number_of_isolates
-link_analysis
- ├─ hits_alg
- │ └─ hits
- └─ pagerank_alg
- └─ pagerank
-operators
- └─ unary
- ├─ complement
- └─ reverse
-reciprocity
- ├─ overall_reciprocity
- └─ reciprocity
-shortest_paths
- └─ unweighted
- ├─ single_source_shortest_path_length
- └─ single_target_shortest_path_length
-traversal
- └─ breadth_first_search
- ├─ bfs_edges
- ├─ bfs_layers
- ├─ bfs_predecessors
- ├─ bfs_successors
- ├─ bfs_tree
- ├─ descendants_at_distance
- └─ generic_bfs_edges
-tree
- └─ recognition
- ├─ is_arborescence
- ├─ is_branching
- ├─ is_forest
- └─ is_tree
-```
-
-#### Generators
-```
-classic
- ├─ barbell_graph
- ├─ circular_ladder_graph
- ├─ complete_graph
- ├─ complete_multipartite_graph
- ├─ cycle_graph
- ├─ empty_graph
- ├─ ladder_graph
- ├─ lollipop_graph
- ├─ null_graph
- ├─ path_graph
- ├─ star_graph
- ├─ tadpole_graph
- ├─ trivial_graph
- ├─ turan_graph
- └─ wheel_graph
-community
- └─ caveman_graph
-small
- ├─ bull_graph
- ├─ chvatal_graph
- ├─ cubical_graph
- ├─ desargues_graph
- ├─ diamond_graph
- ├─ dodecahedral_graph
- ├─ frucht_graph
- ├─ heawood_graph
- ├─ house_graph
- ├─ house_x_graph
- ├─ icosahedral_graph
- ├─ krackhardt_kite_graph
- ├─ moebius_kantor_graph
- ├─ octahedral_graph
- ├─ pappus_graph
- ├─ petersen_graph
- ├─ sedgewick_maze_graph
- ├─ tetrahedral_graph
- ├─ truncated_cube_graph
- ├─ truncated_tetrahedron_graph
- └─ tutte_graph
-social
- ├─ davis_southern_women_graph
- ├─ florentine_families_graph
- ├─ karate_club_graph
- └─ les_miserables_graph
-```
-
-#### Other
-
-```
-convert_matrix
- ├─ from_pandas_edgelist
- └─ from_scipy_sparse_array
-```
diff --git a/docs/cugraph-docs/source/nx_cugraph/supported-algorithms.rst b/docs/cugraph-docs/source/nx_cugraph/supported-algorithms.rst
new file mode 100644
index 0000000..8dc3c5b
--- /dev/null
+++ b/docs/cugraph-docs/source/nx_cugraph/supported-algorithms.rst
@@ -0,0 +1,356 @@
+Supported Algorithms
+=====================
+
+The nx-cugraph backend to NetworkX connects
+`pylibcugraph `_ (cuGraph's low-level Python
+interface to its CUDA-based graph analytics library) and
+`CuPy `_ (a GPU-accelerated array library) to NetworkX's
+familiar and easy-to-use API.
+
+Below is the list of algorithms that are currently supported in nx-cugraph.
+
+
+Algorithms
+----------
+
++-----------------------------+
+| **Centrality** |
++=============================+
+| betweenness_centrality |
++-----------------------------+
+| edge_betweenness_centrality |
++-----------------------------+
+| degree_centrality |
++-----------------------------+
+| in_degree_centrality |
++-----------------------------+
+| out_degree_centrality |
++-----------------------------+
+| eigenvector_centrality |
++-----------------------------+
+| katz_centrality |
++-----------------------------+
+
++---------------------+
+| **Cluster** |
++=====================+
+| average_clustering |
++---------------------+
+| clustering |
++---------------------+
+| transitivity |
++---------------------+
+| triangles |
++---------------------+
+
++--------------------------+
+| **Community** |
++==========================+
+| louvain_communities |
++--------------------------+
+
++--------------------------+
+| **Bipartite** |
++==========================+
+| betweenness_centrality |
+| complete_bipartite_graph |
++--------------------------+
+
++------------------------------------+
+| **Components** |
++====================================+
+| connected_components |
++------------------------------------+
+| is_connected |
++------------------------------------+
+| node_connected_component |
++------------------------------------+
+| number_connected_components |
++------------------------------------+
+| weakly_connected |
++------------------------------------+
+| is_weakly_connected |
++------------------------------------+
+| number_weakly_connected_components |
++------------------------------------+
+| weakly_connected_components |
++------------------------------------+
+
++-------------+
+| **Core** |
++=============+
+| core_number |
++-------------+
+| k_truss |
++-------------+
+
++-------------+
+| **DAG** |
++=============+
+| ancestors |
++-------------+
+| descendants |
++-------------+
+
++--------------------+
+| **Isolate** |
++====================+
+| is_isolate |
++--------------------+
+| isolates |
++--------------------+
+| number_of_isolates |
++--------------------+
+
++-------------------+
+| **Link analysis** |
++===================+
+| hits |
++-------------------+
+| pagerank |
++-------------------+
+
++----------------+
+| **Operators** |
++================+
+| complement |
++----------------+
+| reverse |
++----------------+
+
++----------------------+
+| **Reciprocity** |
++======================+
+| overall_reciprocity |
++----------------------+
+| reciprocity |
++----------------------+
+
++---------------------------------------+
+| **Shortest Paths** |
++=======================================+
+| has_path |
++---------------------------------------+
+| shortest_path |
++---------------------------------------+
+| shortest_path_length |
++---------------------------------------+
+| all_pairs_shortest_path |
++---------------------------------------+
+| all_pairs_shortest_path_length |
++---------------------------------------+
+| bidirectional_shortest_path |
++---------------------------------------+
+| single_source_shortest_path |
++---------------------------------------+
+| single_source_shortest_path_length |
++---------------------------------------+
+| single_target_shortest_path |
++---------------------------------------+
+| single_target_shortest_path_length |
++---------------------------------------+
+| all_pairs_bellman_ford_path |
++---------------------------------------+
+| all_pairs_bellman_ford_path_length |
++---------------------------------------+
+| all_pairs_dijkstra |
++---------------------------------------+
+| all_pairs_dijkstra_path |
++---------------------------------------+
+| all_pairs_dijkstra_path_length |
++---------------------------------------+
+| bellman_ford_path |
++---------------------------------------+
+| bellman_ford_path_length |
++---------------------------------------+
+| dijkstra_path |
++---------------------------------------+
+| dijkstra_path_length |
++---------------------------------------+
+| single_source_bellman_ford |
++---------------------------------------+
+| single_source_bellman_ford_path |
++---------------------------------------+
+| single_source_bellman_ford_path_length|
++---------------------------------------+
+| single_source_dijkstra |
++---------------------------------------+
+| single_source_dijkstra_path |
++---------------------------------------+
+| single_source_dijkstra_path_length |
++---------------------------------------+
+
++---------------------------+
+| **Traversal** |
++===========================+
+| bfs_edges |
++---------------------------+
+| bfs_layers |
++---------------------------+
+| bfs_predecessors |
++---------------------------+
+| bfs_successors |
++---------------------------+
+| bfs_tree |
++---------------------------+
+| descendants_at_distance |
++---------------------------+
+| generic_bfs_edges |
++---------------------------+
+
++---------------------+
+| **Tree** |
++=====================+
+| is_arborescence |
++---------------------+
+| is_branching |
++---------------------+
+| is_forest |
++---------------------+
+| is_tree |
++---------------------+
+
+
+Utilities
+-------
+
++-------------------------+
+| **Classes** |
++=========================+
+| is_negatively_weighted |
++-------------------------+
+
++----------------------+
+| **Convert** |
++======================+
+| from_dict_of_lists |
++----------------------+
+| to_dict_of_lists |
++----------------------+
+
++--------------------------+
+| **Convert Matrix** |
++==========================+
+| from_pandas_edgelist |
++--------------------------+
+| from_scipy_sparse_array |
++--------------------------+
+
++-----------------------------------+
+| **Relabel** |
++===================================+
+| convert_node_labels_to_integers |
++-----------------------------------+
+| relabel_nodes |
++-----------------------------------+
+
+Generators
+------------
+
++-------------------------------+
+| **Classic** |
++===============================+
+| barbell_graph |
++-------------------------------+
+| circular_ladder_graph |
++-------------------------------+
+| complete_graph |
++-------------------------------+
+| complete_multipartite_graph |
++-------------------------------+
+| cycle_graph |
++-------------------------------+
+| empty_graph |
++-------------------------------+
+| ladder_graph |
++-------------------------------+
+| lollipop_graph |
++-------------------------------+
+| null_graph |
++-------------------------------+
+| path_graph |
++-------------------------------+
+| star_graph |
++-------------------------------+
+| tadpole_graph |
++-------------------------------+
+| trivial_graph |
++-------------------------------+
+| turan_graph |
++-------------------------------+
+| wheel_graph |
++-------------------------------+
+
++-----------------+
+| **Classic** |
++=================+
+| caveman_graph |
++-----------------+
+
++------------+
+| **Ego** |
++============+
+| ego_graph |
++------------+
+
++------------------------------+
+| **small** |
++==============================+
+| bull_graph |
++------------------------------+
+| chvatal_graph |
++------------------------------+
+| cubical_graph |
++------------------------------+
+| desargues_graph |
++------------------------------+
+| diamond_graph |
++------------------------------+
+| dodecahedral_graph |
++------------------------------+
+| frucht_graph |
++------------------------------+
+| heawood_graph |
++------------------------------+
+| house_graph |
++------------------------------+
+| house_x_graph |
++------------------------------+
+| icosahedral_graph |
++------------------------------+
+| krackhardt_kite_graph |
++------------------------------+
+| moebius_kantor_graph |
++------------------------------+
+| octahedral_graph |
++------------------------------+
+| pappus_graph |
++------------------------------+
+| petersen_graph |
++------------------------------+
+| sedgewick_maze_graph |
++------------------------------+
+| tetrahedral_graph |
++------------------------------+
+| truncated_cube_graph |
++------------------------------+
+| truncated_tetrahedron_graph |
++------------------------------+
+| tutte_graph |
++------------------------------+
+
++-------------------------------+
+| **Social** |
++===============================+
+| davis_southern_women_graph |
++-------------------------------+
+| florentine_families_graph |
++-------------------------------+
+| karate_club_graph |
++-------------------------------+
+| les_miserables_graph |
++-------------------------------+
+
+
+To request nx-cugraph backend support for a NetworkX API that is not listed
+above, visit the `nx-cugraph GitHub repo `_.
diff --git a/docs/cugraph-docs/source/top_toc.rst b/docs/cugraph-docs/source/top_toc.rst
deleted file mode 100644
index aa06962..0000000
--- a/docs/cugraph-docs/source/top_toc.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-.. toctree::
- :maxdepth: 2
- :caption: cuGraph documentation Contents:
- :name: top_toc
-
- basics/index
- nx_cugraph/index
- installation/index
- tutorials/index
- graph_support/index
- wholegraph/index
- references/index
- api_docs/index
\ No newline at end of file
diff --git a/docs/cugraph-docs/source/tutorials/basic_cugraph.md b/docs/cugraph-docs/source/tutorials/basic_cugraph.md
new file mode 100644
index 0000000..a0c9ad5
--- /dev/null
+++ b/docs/cugraph-docs/source/tutorials/basic_cugraph.md
@@ -0,0 +1,38 @@
+# Getting started with cuGraph
+
+## Required hardware/software
+
+CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide) and has the following system requirements:
+ * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
+ * CUDA 11.2, 11.4, 11.5, 11.8, 12.0, 12.2, or 12.5
+ * Python version 3.10, 3.11, or 3.12
+ * NetworkX >= version 3.3 or newer in order to use use [NetworkX Configs](https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs) **This is required for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).**
+
+## Installation
+The latest RAPIDS System Requirements documentation is located [here](https://docs.rapids.ai/install#system-req).
+
+This includes several ways to set up cuGraph
+* From Unix
+ * [Conda](https://docs.rapids.ai/install#wsl-conda)
+ * [Docker](https://docs.rapids.ai/install#wsl-docker)
+ * [pip](https://docs.rapids.ai/install#wsl-pip)
+
+* In windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and then choose one of the following:
+ * [Conda](https://docs.rapids.ai/install#wsl-conda)
+ * [Docker](https://docs.rapids.ai/install#wsl-docker)
+ * [pip](https://docs.rapids.ai/install#wsl-pip)
+
+* Build From Source
+
+To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the [selector tool](https://docs.rapids.ai/install#selector). If additional help is needed reach out on our [Slack Channel](https://rapids-goai.slack.com/archives/C5E06F4DC).
+
+## CuGraph Using NetworkX Code
+While the steps above are required to use the full suite of cuGraph graph analytics, cuGraph is now supported as a NetworkX backend using [nx-cugraph](https://docs.rapids.ai/api/cugraph/nightly/nx_cugraph/nx_cugraph/).
+Nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms.
+
+
+## Cugraph API Example
+Coming soon !
+
+
+Until then, [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) gives examples of python scripts settng 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 examples show [multi-GPU tests/examples with larger data sets](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py) as well.
diff --git a/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst b/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst
index 3665f42..57fa011 100644
--- a/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst
+++ b/docs/cugraph-docs/source/tutorials/cugraph_blogs.rst
@@ -65,9 +65,9 @@ Academic Papers
* Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press
- * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020
+ * S Kang, A. Fender, J. Eaton, B. Rees. `Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters `_. In IEEE HPEC, Sep. 2020
- * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows`. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
+ * Hricik, T., Bader, D., & Green, O. (2020, September). `Using RAPIDS AI to accelerate graph data science workflows `_. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
* Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504).
diff --git a/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md b/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md
index 559ba36..6d7840d 100644
--- a/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md
+++ b/docs/cugraph-docs/source/tutorials/cugraph_notebooks.md
@@ -55,10 +55,9 @@ Running the example in these notebooks requires:
* Download via Docker, Conda (See [__Getting Started__](https://rapids.ai/start.html))
* cuGraph is dependent on the latest version of cuDF. Please install all components of RAPIDS
-* Python 3.8+
-* A system with an NVIDIA GPU: Pascal architecture or better
+* Python 3.10+
+* A system with an NVIDIA GPU: Volta architecture or newer
* CUDA 11.4+
-* NVIDIA driver 450.51+
## Copyright
diff --git a/docs/cugraph-docs/source/tutorials/how_to_guides.md b/docs/cugraph-docs/source/tutorials/how_to_guides.md
index 80be5b4..998957a 100644
--- a/docs/cugraph-docs/source/tutorials/how_to_guides.md
+++ b/docs/cugraph-docs/source/tutorials/how_to_guides.md
@@ -1,5 +1,5 @@
# How To Guides
-- Basic use of cuGraph, on the page
+- [Basic use of cuGraph](./basic_cugraph.md)
- Property graph with analytic flow
- GNN – model building
- cuGraph Service – client/server setup and use (ucx)
diff --git a/docs/cugraph-docs/source/wholegraph/installation/container.md b/docs/cugraph-docs/source/wholegraph/installation/container.md
index 3a2c627..4068ead 100644
--- a/docs/cugraph-docs/source/wholegraph/installation/container.md
+++ b/docs/cugraph-docs/source/wholegraph/installation/container.md
@@ -21,9 +21,10 @@ RUN pip3 install -U py
RUN pip3 install Cython setuputils3 scikit-build nanobind pytest-forked pytest
```
-To run GNN applications, you may also need cuGraphOps, DGL and/or PyG libraries to run the GNN layers.
+To run GNN applications, you may also need DGL and/or PyG libraries to run the GNN layers.
You may refer to [DGL](https://www.dgl.ai/pages/start.html) or [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
For example, to install DGL, you may need to add:
+
```dockerfile
-RUN pip3 install dgl -f https://data.dgl.ai/wheels/cu118/repo.html
+RUN pip3 install dgl -f https://data.dgl.ai/wheels/torch-2.3/cu118/repo.html
```
diff --git a/docs/cugraph-docs/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph-docs/source/wholegraph/installation/getting_wholegraph.md
index e2c5b3a..c93dae1 100644
--- a/docs/cugraph-docs/source/wholegraph/installation/getting_wholegraph.md
+++ b/docs/cugraph-docs/source/wholegraph/installation/getting_wholegraph.md
@@ -21,7 +21,8 @@ The RAPIDS Docker containers (as of Release 23.10) contain all RAPIDS packages,
## Conda
-It is easy to install WholeGraph using conda. We recommend using [Miniforge](https://github.com/conda-forge/miniforge).
+
+It is easy to install WholeGraph using conda. You can get a minimal conda installation with [miniforge](https://github.com/conda-forge/miniforge).
WholeGraph conda packages
* libwholegraph
diff --git a/docs/cugraph-docs/source/wholegraph/installation/source_build.md b/docs/cugraph-docs/source/wholegraph/installation/source_build.md
index a7727ac..7213cbf 100644
--- a/docs/cugraph-docs/source/wholegraph/installation/source_build.md
+++ b/docs/cugraph-docs/source/wholegraph/installation/source_build.md
@@ -16,8 +16,7 @@ __Compiler__:
__CUDA__:
* CUDA 11.8+
-* NVIDIA driver 450.80.02+
-* Pascal architecture or better
+* Volta architecture or better
You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).
@@ -177,7 +176,7 @@ Run either the C++ or the Python tests with datasets
```
-Note: This conda installation only applies to Linux and Python versions 3.8/3.10.
+Note: This conda installation only applies to Linux and Python versions 3.10, 3.11, and 3.12.
## Creating documentation