Getting cuGraph Packages#
There are 4 ways to get cuGraph packages:
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.
cuGraph Conda packages
cugraph - this will also import:
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=https://pypi.ngc.nvidia.com
pip packages for other packages are being worked and should be available in early 2023