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nx-cugraph

Description

RAPIDS nx-cugraph is a backend to NetworkX to run supported algorithms with GPU acceleration.

System Requirements

nx-cugraph requires the following:

  • NVIDIA GPU, Pascal architecture or later
  • CUDA 11.2, 11.4, 11.5, 11.8, or 12.0
  • Python versions 3.9, 3.10, or 3.11
  • NetworkX >= version 3.2

More details about system requirements can be found in the RAPIDS System Requirements documentation.

Installation

nx-cugraph can be installed using either conda or pip.

conda

conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph

pip

python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com

Notes:

  • Nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above.
  • Additional information relevant to installing any RAPIDS package can be found here.

Enabling nx-cugraph

NetworkX will use nx-cugraph as the graph analytics backend if any of the following are used:

NETWORKX_AUTOMATIC_BACKENDS environment variable.

The NETWORKX_AUTOMATIC_BACKENDS environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. Set NETWORKX_AUTOMATIC_BACKENDS=cugraph to use nx-cugraph to GPU accelerate supported APIs with no code changes. Example:

bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph 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 NETWORKX_AUTOMATIC_BACKENDS environment variable. This requires anyone running code that uses the backend= keyword argument to have the specified backend installed.

Example:

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:

import networkx as nx
import nx_cugraph as nxcg

G = nx.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

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 (many listed using pylibcugraph names), available today in pylibcugraph or implemented using CuPy, that are or will be supported in nx-cugraph.

feature/algo release/target version
analyze_clustering_edge_cut ?
analyze_clustering_modularity ?
analyze_clustering_ratio_cut ?
balanced_cut_clustering ?
betweenness_centrality 23.10
bfs ?
connected_components 23.12
core_number ?
degree_centrality 23.12
ecg ?
edge_betweenness_centrality 23.10
ego_graph ?
eigenvector_centrality 23.12
get_two_hop_neighbors ?
hits 23.12
in_degree_centrality 23.12
induced_subgraph ?
jaccard_coefficients ?
katz_centrality 23.12
k_core ?
k_truss_subgraph 23.12
leiden ?
louvain 23.10
node2vec ?
out_degree_centrality 23.12
overlap_coefficients ?
pagerank 23.12
personalized_pagerank ?
sorensen_coefficients ?
spectral_modularity_maximization ?
sssp 23.12
strongly_connected_components ?
triangle_count ?
uniform_neighbor_sample ?
uniform_random_walks ?
weakly_connected_components ?

To request nx-cugraph backend support for a NetworkX API that is not listed above, visit the cuGraph GitHub repo.