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rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/releases/index.rst | ========
Releases
========
https://github.com/rapidsai/cugraph/blob/main/CHANGELOG.md | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/index.rst | WholeGraph
==========
RAPIDS WholeGraph has following package:
* pylibwholegraph: shared memory-based GPU-accelerated GNN training
.. toctree::
:maxdepth: 2
:caption: Contents:
basics/index
installation/index
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/basics/wholememory_intro.md | ## WholeMemory
WholeMemory can be regarded as a whole view of GPU memory.
WholeMemory exposes a handle to the memory instance no matter how the underlying data is stored across multiple GPUs.
WholeMemory assumes that a separate process is used to control each GPU.
### WholeMemory Basics
To define WholeMemory, we need ... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md | # WholeGraph Introduction
WholeGraph helps train large-scale Graph Neural Networks(GNN).
WholeGraph provides underlying storage structure called WholeMemory.
WholeMemory is a Tensor like storage and provides multi-GPU support.
It is optimized for NVLink systems like DGX A100 servers.
By working together with cuGraph, c... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md | # WholeMemory Implementation Details
As described in [WholeMemory Introduction](wholegraph_intro.md), there are two WholeMemory location and three
WholeMemory types. So there will be total six WholeMemory.
| Type | CONTINUOUS | CONTINUOUS | CHUNKED | CHUNKED | DISTRIBUTED | DISTRIBUTED |
|:-------------:... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/basics/index.rst | ======
Basics
======
.. toctree::
:maxdepth: 2
wholegraph_intro
wholememory_intro
wholememory_implementation_details
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/installation/container.md | # Build Container for WholeGraph
To run WholeGraph or build WholeGraph from source, set up the environment first.
We recommend using Docker images.
For example, to build the WholeGraph base image from the NGC pytorch 22.10 image, you can follow `Dockerfile`:
```dockerfile
FROM nvcr.io/nvidia/pytorch:22.10-py3
RUN apt ... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/installation/source_build.md | # Building from Source
The following instructions are for users wishing to build wholegraph from source code. These instructions are tested on supported distributions of Linux,CUDA,
and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for a list of supported environments.
Other operating systems _mi... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md |
# Getting the WholeGraph Packages
Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install)
and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options.
There are 4 ways to get WholeGraph packages:
1. [Quick start with Docker Repo](#docker)
2. ... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/wholegraph/installation/index.rst | Installation
============
.. toctree::
:maxdepth: 2
getting_wholegraph
container
source_build
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/_static/references.css |
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padding... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/sphinxext/github_link.py | # Copyright (c) 2019-2023, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/index.rst | API Reference
=============
This page provides a list of all publicly accessible Python modules with in the Graph collection
Core Graph API Documentation
----------------------------
.. toctree::
:maxdepth: 3
:caption: Core Graph API Documentation
cugraph/index.rst
plc/pylibcugraph.rst
cugraph_c... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/wholegraph/index.rst | WholeGraph API reference
========================
This page provides WholeGraph API reference
.. toctree::
:maxdepth: 2
:caption: WholeGraph API Documentation
libwholegraph/index.rst
pylibwholegraph/index.rst
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst | =======================
pylibwholegraph API doc
=======================
.. currentmodule:: pylibwholegraph
APIs
----
.. autosummary::
:toctree: ../../api/wg
torch.initialize.init_torch_env
torch.initialize.init_torch_env_and_create_wm_comm
torch.initialize.finalize
torch.comm.WholeMemoryCommunica... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/wholegraph | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst | =====================
libwholegraph API doc
=====================
Doxygen WholeGraph C API documentation
--------------------------------------
For doxygen documentation, please refer to `Doxygen Documentation <../../doxygen_docs/libwholegraph/html/index.html>`_
WholeGraph C API documentation
------------------------... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/service/cugraph_service_server.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-service-server API Reference
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-service
.. currentmodule:: cugraph-service
.. autosummary::
:toctree: ../api/service/
.. cugraph_service_server.cugraph_handler.call_algo
cugraph_service_server.cugraph_handler.ExtensionSer... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/service/cugraph_service_client.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-service-client API Reference
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-service
.. currentmodule:: cugraph-service
.. autosummary::
:toctree: ../api/service/
.. cugraph_service_client.client.RunAsyncioThread
cugraph_service_client.client.run_async
cugraph_se... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/service/index.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-service API Reference
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. toctree::
:maxdepth: 2
:caption: cugraph-service API Documentation
cugraph_service_client
cugraph_service_server | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/plc/pylibcugraph.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~~
pylibcugraph API reference
~~~~~~~~~~~~~~~~~~~~~~~~~~
pylibcugraph
.. currentmodule:: pylibcugraph
Methods
-------
.. autosummary::
:toctree: ../api/plc/
pylibcugraph.eigenvector_centrality
pylibcugraph.katz_centrality
pylibcugraph.strongly_connected_components
pylibcugraph... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/centrality.rst | Centrality
==========
PageRank
--------
.. doxygenfunction:: cugraph_pagerank
:project: libcugraph
.. doxygenfunction:: cugraph_pagerank_allow_nonconvergence
:project: libcugraph
Personalized PageRank
---------------------
.. doxygenfunction:: cugraph_personalized_pagerank
:project: libcugraph
.. doxyge... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/labeling.rst | Components
==========
Weakly Connected Components
---------------------------
.. doxygenfunction:: cugraph_weakly_connected_components
:project: libcugraph
Strongly Connected Components
-----------------------------
.. doxygenfunction:: cugraph_strongly_connected_components
:project: libcugraph
Support
----... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/sampling.rst | Sampling
========
Uniform Random Walks
--------------------
.. doxygenfunction:: cugraph_uniform_random_walks
:project: libcugraph
Biased Random Walks
--------------------
.. doxygenfunction:: cugraph_biased_random_walks
:project: libcugraph
Random Walks via Node2Vec
-------------------------
.. doxygenfunct... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/traversal.rst | Traversal
==========
Breadth First Search (BFS)
--------------------------
.. doxygenfunction:: cugraph_bfs
:project: libcugraph
Single-Source Shortest-Path (SSSP)
----------------------------------
.. doxygenfunction:: cugraph_sssp
:project: libcugraph
Path Extraction
---------------
.. doxygenfunction:: c... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/core.rst | Core
====
Core Number
-----------
.. doxygenfunction:: cugraph_core_number
:project: libcugraph
K-Core
------
.. doxygenfunction:: cugraph_k_core
:project: libcugraph
Core Support Functions
----------------------
.. doxygengroup:: core
:project: libcugraph
:members:
:content-only:
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/community.rst | Community
=========
.. role:: py(code)
:language: c
:class: highlight
``#include <cugraph_c/community_algorithms.h>``
Triangle Counting
-----------------
.. doxygenfunction:: cugraph_triangle_count
:project: libcugraph
Louvain
-------
.. doxygenfunction:: cugraph_louvain
:project: libcugraph
Leiden
-... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/index.rst | ===========================
cuGraph C API documentation
===========================
.. toctree::
:maxdepth: 3
:caption: API Documentation
centrality.rst
community.rst
core.rst
labeling.rst
sampling.rst
similarity.rst
traversal.rst
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph_c/similarity.rst | Similarity
==========
Jaccard
-------
.. doxygenfunction:: cugraph_jaccard_coefficients
:project: libcugraph
Sorensen
--------
.. doxygenfunction:: cugraph_sorensen_coefficients
:project: libcugraph
Overlap
-------
.. doxygenfunction:: cugraph_overlap_coefficients
:project: libcugraph
Support
-------
.... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/generators.rst | ==========
Generators
==========
.. currentmodule:: cugraph
RMAT
----
.. autosummary::
:toctree: ../api/cugraph/
cugraph.generators.rmat
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/components.rst | ==========
Components
==========
.. currentmodule:: cugraph
Connected Components
--------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.connected_components
cugraph.strongly_connected_components
cugraph.weakly_connected_components
Connected Components (MG)
-------------------------
.. ... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/linear_assignment.rst | =================
Linear Assignment
=================
.. currentmodule:: cugraph
Hungarian
---------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.hungarian
cugraph.dense_hungarian
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/link_prediction.rst | ===============
Link Prediction
===============
.. currentmodule:: cugraph
Jaccard Coefficient
-------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.jaccard
cugraph.jaccard_coefficient
cugraph.jaccard_w
Overlap Coefficient
-------------------
.. autosummary::
:toctree: ../api/cugrap... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/centrality.rst | ==========
Centrality
==========
.. currentmodule:: cugraph
Betweenness Centrality
----------------------
single-GPU
^^^^^^^^^^
.. autosummary::
:toctree: ../api/cugraph/
cugraph.centrality.betweenness_centrality
cugraph.centrality.edge_betweenness_centrality
multi-GPU
^^^^^^^^^^
.. autosummary::
:toct... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst | ~~~~~~~~~~~~~~~~~~~~~~
Multi-GPU with cuGraph
~~~~~~~~~~~~~~~~~~~~~~
cuGraph supports multi-GPU leveraging `Dask <https://dask.org>`_. Dask is a flexible library for parallel computing in Python which makes scaling out your workflow smooth and simple. cuGraph also uses other Dask-based RAPIDS projects such as `dask-cu... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/layout.rst | ======
Layout
======
.. currentmodule:: cugraph
Force Atlas 2
-------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.force_atlas2
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/property_graph.rst | ====================
Property Graph
====================
.. currentmodule:: cugraph.experimental
Property Graph
-------------------------
.. autosummary::
:toctree: ../api/cugraph/
PropertySelection
PropertyGraph
PropertyGraph.add_edge_data
PropertyGraph.add_vertex_data
PropertyGraph.annotate_dataf... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/sampling.rst | ========
Sampling
========
.. currentmodule:: cugraph
Random Walks
------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.random_walks
cugraph.ego_graph
cugraph.uniform_neighbor_sample
Node2Vec
---------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.node2vec
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/graph_implementation.rst | ====================
Graph Implementation
====================
.. currentmodule:: cugraph.structure.graph_implementation.simpleGraphImpl
Graph Implementation
--------------------
.. autosummary::
:toctree: ../api/cugraph/
view_edge_list
delete_edge_list
view_adj_list
view_transposed_adj_list
... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/traversal.rst | =========
Traversal
=========
.. currentmodule:: cugraph
Breadth-first-search
--------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.bfs
cugraph.bfs_edges
Breadth-first-search (MG)
-------------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.dask.traversal.bfs.bfs
S... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/link_analysis.rst | =============
Link Analysis
=============
.. currentmodule:: cugraph
HITS
----
.. autosummary::
:toctree: ../api/cugraph/
cugraph.hits
HITS (MG)
---------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.dask.link_analysis.hits.hits
Pagerank
--------
.. autosummary::
:toctree: ../api/cugraph/
... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/helper_functions.rst | ========================
DASK MG Helper functions
========================
.. currentmodule:: cugraph
Methods
-------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.dask.comms.comms.initialize
cugraph.dask.comms.comms.destroy
cugraph.dask.comms.comms.is_initialized
cugraph.dask.comms.comms.get_com... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/tree.rst | ====
Tree
====
.. currentmodule:: cugraph
Minimum Spanning Tree
---------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.tree.minimum_spanning_tree.minimum_spanning_tree
Maximum Spanning Tree
---------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.tree.minimum_spanning... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/cores.rst | =====
Cores
=====
.. currentmodule:: cugraph
Core Number
-----------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.core_number
K-Core
------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.k_core
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/community.rst | =========
Community
=========
.. currentmodule:: cugraph
EgoNet
------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.batched_ego_graphs
cugraph.ego_graph
Ensemble clustering for graphs (ECG)
------------------------------------
.. autosummary::
:toctree: ../api/cugraph/
cugraph.ecg
K-Truss
-... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/structure.rst | =============
Graph Classes
=============
.. currentmodule:: cugraph
Constructors
------------
.. autosummary::
:toctree: ../api/cugraph/
Graph
MultiGraph
Adding Data
-----------
.. autosummary::
:toctree: ../api/cugraph/
Graph.from_cudf_adjlist
Graph.from_cudf_edgelist
Graph.from_dask_cudf_ed... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph/index.rst | ~~~~~~~~~~~~~~~~~~~~~
cugraph API Reference
~~~~~~~~~~~~~~~~~~~~~
.. toctree::
:maxdepth: 2
:caption: API Documentation
structure
graph_implementation
property_graph
centrality
community
components
cores
layout
linear_assignment
link_analysis
link_prediction
sam... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/index.rst | cugraph-ops API reference
=========================
This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
.. toctree::
:maxdepth: 2
:caption: API Documentation
python/index
c_cpp/index | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst | ==========================
PyTorch Autograd Wrappers
==========================
.. currentmodule:: pylibcugraphops.pytorch
Simple Neighborhood Aggregator (SAGEConv)
-----------------------------------------
.. autosummary::
:toctree: ../../api/ops
operators.agg_concat_n2n
Graph Attention (GATConv/GATv2Conv)
-... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst | =============================
Operators for Message-Passing
=============================
.. currentmodule:: pylibcugraphops
Simple Neighborhood Aggregator (SAGEConv)
-----------------------------------------
.. autosummary::
:toctree: ../../api/ops
operators.agg_simple_n2n_fwd
operators.agg_simple_n2n_bwd
... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst | =================
Dimenet operators
=================
.. currentmodule:: pylibcugraphops
Radial Basis Functions
----------------------
.. autosummary::
:toctree: ../../api/ops
dimenet.radial_basis_fwd
dimenet.radial_basis_bwd
dimenet.radial_basis_bwd_bwd
Edge-to-Edge Aggregation
------------------------... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst | cugraph-ops Python API reference
================================
This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace.
.. toctree::
:maxdepth: 2
:caption: API Documentation
graph_types
operators
dimenet
pytorch
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst | ===========
Graph types
===========
.. currentmodule:: pylibcugraphops
CSC Graph
-----------------
.. autosummary::
:toctree: ../../api/ops
make_csc
Heterogenous CSC Graph
----------------------
.. autosummary::
:toctree: ../../api/ops
make_csc_hg
Bipartite Graph
-----------------
.. autosummary::
... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst | cugraph-ops C++ API Reference
=============================
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-dgl/cugraph_dgl.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-dgl API Reference
~~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-dgl
.. currentmodule:: cugraph_dgl
Methods
-------
.. autosummary::
:toctree: ../api/cugraph-dgl/
convert.cugraph_storage_from_heterograph
cugraph_storage.CuGraphStorage
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs | rapidsai_public_repos/cugraph/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst | ~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-pyg API Reference
~~~~~~~~~~~~~~~~~~~~~~~~~
cugraph-pyg
.. currentmodule:: cugraph_pyg
.. autosummary::
:toctree: ../api/cugraph-pyg/
.. cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore
.. cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/property_graph.md | # Property Graph
Part of [RAPIDS](https://rapids.ai) cuGraph, Property Graph allows all the great benefits of cuGraph to be applied to property-rich datasets stored in a graph structure. A Property Graph is really a data model rather than a type of graph. Within the cuGraph ecosystem, a Property Graph is a meta-graph... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/compatibility.rst | Compatibility
=============
.. toctree::
:maxdepth: 3
Compatibility document coming soon | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/knowledge_stores.md | # Knowledge Store
Coming Soon | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/graph_algorithms.rst | ==========
Algorithms
==========
.. toctree::
:maxdepth: 3
algorithms.md | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/wholegraph_support.md | # WholeGraph
[RAPIDS](https://rapids.ai) [WholeGraph](https://github.com/rapidsai/wholegraph) is designed to help train large-scale Graph Neural Networks(GNN).
Please see [WholeGraph Introduction](https://github.com/rapidsai/wholegraph/blob/main/README.md) for more details | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/cugraph_service.rst | ===============
CuGraph Service
===============
.. toctree::
:maxdepth: 3
Cugraph Service for remote access to a server-based cuGraph(https://github.com/rapidsai/cugraph/blob/branch-23.04/python/cugraph-service/README.md) | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/feature_stores.md | # Feature Store
Coming Soon | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/DGL_support.md | # cugraph_dgl
## Description
[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.DGLGraph) class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/PyG_support.md | # cugraph_pyg
[RAPIDS](https://rapids.ai) cugraph_pyg enables the ability to use cugraph graph storage and sampling with PyTorch Geometric (PyG). PyG users will have access to cuGraph through the PyG GraphStore, FeatureStore, and Sampler interfaces. | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/cugraphops_support.rst | ==================
cugraphops Support
==================
cugraph-ops aims to be a low-level, framework agnostic library providing commonly used computational primitives for GNNs and other graph operations.
.. toctree::
:maxdepth: 3
https://github.com/rapidsai/cugraph-ops/blob/branch-23.04/README.md | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/datastores.rst | Data Stores
===========
.. toctree::
:maxdepth: 3
property_graph.md
knowledge_stores.md
feature_stores.md
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms.md | # List of Supported and Planned Algorithms
## Supported Graph
| Type | Description |
| ---------- | ----------------------------------------------------------- |
| Graph | A directed or undirected Graph (use directed={True, False}) |
| Multigraph | A Graph wi... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/index.rst | =============
Graph Support
=============
.. toctree::
:maxdepth: 3
graph_algorithms.rst
compatibility.rst
gnn_support.rst
datastores.rst
cugraph_service.rst | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/gnn_support.rst | ============================
Graph Neural Network Support
============================
.. toctree::
:maxdepth: 2
PyG_support.md
DGL_support.md
cugraphops_support.rst
wholegraph_support.md | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/katz_centrality.md | # Katz Centrality
Katz centrality is a measure of the relative importance of a vertex within the graph based on measuring the influence across the total number of walks between vertex pairs. Katz is similar to Eigenvector centrality. The main difference is that Katz also takes into account indirect relationships. The ... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/degree_centrality.md | # Degree Centrality
Degree centrality is the simplest measure of the relative importance based on counting the connections with each vertex. Vertices with the most connections are the most central by this measure.
See [Degree Centrality on Wikipedia](https://en.wikipedia.org/wiki/Degree_centrality) for more details on... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/jaccard_similarity.md | # Jaccard Similarity
The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection divided by the volume of their union.
The Jaccard Similarity can then be defined as
Jaccard similarity coefficient = $\frac{|A \cap B|}{|A \cup B|}$
In graphs, the sets refer to the set of conne... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/Centrality.md |
# cuGraph Centrality Notebooks
<img src="../images/zachary_graph_centrality.png" width="35%"/>
The RAPIDS cuGraph Centrality folder contains a collection of Jupyter Notebooks that demonstrate algorithms to identify and quantify the importance of vertices to the structure of the graph. In the diagram above, the high... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/Similarity.md |
# cuGraph Similarity Notebooks
The RAPIDS cuGraph Similarity folder contain a collection of Jupyter Notebooks that demonstrate algorithms to quantify the similarity between pairs of vertices in the graph.
Results of Similarity algorithms are often used to answer questions like:
* Could two vertices be duplicates or a... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/overlap_similarity.md | # Overlap Similarity
The Overlap Coefficient, also known as th Szymkiewicz–Simpson coefficient, between two sets is defined as the ratio of the volume of their intersection divided by the volume of the smaller set.
The Overlap Coefficient can be defined as
$overlap(A,B) = \frac{|A \cap B|}{min(|A|,|B|)}$
[Learn more... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/betweenness_centrality.md | # Betweenness Centrality (BC)
Betweenness centrality is a measure of the relative importance based on measuring the number of shortest paths that pass through each vertex or over each edge. High betweenness centrality vertices have a greater number of path cross through the vertex. Likewise, high centrality edges ha... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/eigenvector_centrality.md | # Eigenvector Centrality
Eigenvector centrality computes the centrality for a vertex based on the
centrality of its neighbors. The Eigenvector of a node measures influence within a graph by taking into account a vertex's connections to other highly connected vertices.
See [Eigenvector Centrality on Wikipedia](https:... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support | rapidsai_public_repos/cugraph/docs/cugraph/source/graph_support/algorithms/sorensen_coefficient.md | # Sørensen Coefficient
The Sørensen Coefficient, also called the Sørensen-Dice similarity coefficient, quantifies the similarity and overlap between two samples.
It is defined as two times the size of the set intersection divided by the sum of the size of the two sets. The value ranges from 0 to 1.
Sørensen coeffici... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/basics/cugraph_intro.md |
# cuGraph Introduction
The Data Scientist has a collection of techniques within their
proverbial toolbox. Data engineering, statistical analysis, and
machine learning are among the most commonly known. However, there
are numerous cases where the focus of the analysis is on the
relationship between data elements. I... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/basics/nx_transition.rst | **************************************
NetworkX Compatibility and Transition
**************************************
*Note: this is a work in progress and will be updatred and changed as we better flesh out
compatibility issues*
One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify
the transitio... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/basics/cugraph_cascading.md |
# Method Cascading and cuGraph
BLUF: cuGraph does not support method cascading
[Method Cascading](https://en.wikipedia.org/wiki/Method_cascading) is a popular, and useful, functional programming concept and is a great way to make code more readable. Python supports method cascading ... _for the most part_. There a... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/basics/index.rst | ======
Basics
======
.. toctree::
:maxdepth: 2
cugraph_intro
nx_transition
cugraph_cascading
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/tutorials/cugraph_notebooks.md | # cuGraph Notebooks
This repository contains a collection of Jupyter Notebooks that outline how to run various cuGraph analytics. The notebooks do not address a complete data science problem. The notebooks are simpl... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/tutorials/how_to_guides.md | # How To Guides
- Basic use of cuGraph, on the page
- Property graph with analytic flow
- GNN – model building
- cuGraph Service – client/server setup and use (ucx)
- MNMG Graph – dask, rmm basics and examples
- Pylibcugraph – why and how
- Cugraph for C, C++ users
- Use of nvidia-smi with cugraph | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/tutorials/community_resources.md | # Commmunity Resources
[Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/tutorials/cugraph_blogs.rst |
cuGraph Blogs and Presentations
************************************************
The RAPIDS team blogs at https://medium.com/rapids-ai, and many of
these blog posts provide deeper dives into features from cuGraph.
Here, we've selected just a few that are of particular interest to cuGraph users:
Blogs & Conferences... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/tutorials/index.rst | =========
Tutorials
=========
.. toctree::
:maxdepth: 3
how_to_guides.md
cugraph_blogs.rst
community_resources.md
cugraph_notebooks.md
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/dev_resources/API.rst | ===
API
===
https://docs.rapids.ai/api/cugraph/nightly/api_docs/index.html | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/dev_resources/index.rst | ===================
Developer Resources
===================
.. toctree::
:maxdepth: 3
https://docs.rapids.ai/maintainers
API.rst | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/references/licenses.md | # License
Most of the Graph code is open-sourced and developed under the Apache 2.0 licnese.
The cugraph-ops code is closed sourced and developed under a NVIDIA copyright
Apache License
Version 2.0, January 2004
http://www.apache.o... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/references/datasets.md | # Data Sets
karate
- W. W. Zachary, *An information flow model for conflict and fission in small groups*, Journal of Anthropological Research 33, 452-473 (1977).
dolphins
- D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson,
*The bottlenose dolphin community of Doubtful Sound feat... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/references/cugraph_ref.md | # References
## Architecture
2-D Data Partitioning
- Kang, S., Fender, A., Eaton, J., & Rees, B. (2020, September) *Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters*. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.
- S. Kang, J. Nke and B. Rees, (2022 September... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/references/index.rst | ==========
References
==========
.. toctree::
:maxdepth: 3
cugraph_ref.md
datasets.md
licenses.md
| 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/installation/getting_cugraph.md |
# Getting cuGraph Packages
Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install)
and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options.
There are 4 ways to get cuGraph packages:
1. [Quick start with Docker Repo](#docker)
2. [Conda Ins... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/installation/source_build.md | # Building from Source
The following instructions are for users wishing to build cuGraph from source code. These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments. Other operating systems _migh... | 0 |
rapidsai_public_repos/cugraph/docs/cugraph/source | rapidsai_public_repos/cugraph/docs/cugraph/source/installation/index.rst | ============
Installation
============
.. toctree::
:maxdepth: 3
getting_cugraph
source_build
| 0 |
rapidsai_public_repos/cugraph | rapidsai_public_repos/cugraph/ci/notebook_list.py | # Copyright (c) 2021-2023, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | 0 |
rapidsai_public_repos/cugraph | rapidsai_public_repos/cugraph/ci/test_wheel_pylibcugraph.sh | #!/bin/bash
# Copyright (c) 2023, NVIDIA CORPORATION.
set -eoxu pipefail
./ci/test_wheel.sh pylibcugraph python/pylibcugraph
| 0 |
rapidsai_public_repos/cugraph | rapidsai_public_repos/cugraph/ci/test_python.sh | #!/bin/bash
# Copyright (c) 2022-2023, NVIDIA CORPORATION.
set -euo pipefail
. /opt/conda/etc/profile.d/conda.sh
rapids-logger "Generate Python testing dependencies"
rapids-dependency-file-generator \
--output conda \
--file_key test_python \
--matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY... | 0 |
rapidsai_public_repos/cugraph | rapidsai_public_repos/cugraph/ci/build_wheel_pylibcugraph.sh | #!/bin/bash
# Copyright (c) 2023, NVIDIA CORPORATION.
set -euo pipefail
export SKBUILD_CONFIGURE_OPTIONS="-DDETECT_CONDA_ENV=OFF -DCUGRAPH_BUILD_WHEELS=ON -DFIND_CUGRAPH_CPP=OFF -DCPM_cugraph-ops_SOURCE=${GITHUB_WORKSPACE}/cugraph-ops/"
./ci/build_wheel.sh pylibcugraph python/pylibcugraph
| 0 |
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