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:

Quick start with Docker Repo
Conda Installation
Pip Installation
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 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.

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

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.

PIP#

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

pip install cugraph-cu11 --extra-index-url=https://pypi.ngc.nvidia.com

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