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


The RAPIDS Docker containers contain all RAPIDS packages, including all from cuGraph, as well as all required supporting packages. To download a container, please see the Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This 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.


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

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 cudatoolkit=11.8

Note: This conda installation only applies to Linux and Python versions 3.8/3.10.


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

pip install cugraph-cu11 --extra-index-url=

pip packages for other packages are being worked and should be available in early 2023