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: 1 :caption: Contents: how-it-works installation supported-algorithms benchmarks