Getting cuGraph Packages#

Start by reading the RAPIDS Instalation guide and checkout the RAPIDS install selector for a pick list of install options.

There are 4 ways to get cuGraph packages:

  1. Quick start with Docker Repo

  2. Conda Installation

  3. Pip Installation

  4. Build from Source


Docker#

The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a RAPIDS container, please see the Docker Hub page for rapidsai/base, choosing a tag based on the NVIDIA CUDA version you’re running. Also, the rapidsai/notebooks container provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.


Conda#

It is easy to install cuGraph using conda. You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

cuGraph Conda packages

  • cugraph - this will also import:

    • pylibcugraph

    • libcugraph

  • cugraph-service-client

  • cugraph-service-server

  • cugraph-dgl

  • cugraph-pyg

  • cugraph-equivariant

  • nx-cugraph

Replace the package name in the example below to the one you want to install.

Install and update cuGraph using the conda command:

conda install -c rapidsai -c conda-forge -c nvidia cugraph cuda-version=12.0

Alternatively, use cuda-version=11.8 for packages supporting CUDA 11.

Note: This conda installation only applies to Linux and Python versions 3.9/3.10/3.11.


PIP#

cuGraph, and all of RAPIDS, is available via pip.

pip install cugraph-cu12 --extra-index-url=https://pypi.nvidia.com

Replace -cu12 with -cu11 for packages supporting CUDA 11.

Also available:

  • cugraph-dgl-cu12

  • cugraph-pyg-cu12

  • cugraph-equivariant-cu12

  • nx-cugraph-cu12