Skip to content

Latest commit

 

History

History
67 lines (44 loc) · 2.08 KB

getting_cugraph.md

File metadata and controls

67 lines (44 loc) · 2.08 KB
Raw

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