# Getting cuGraph Packages

Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install)
and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options.


There are 4 ways to get cuGraph packages:
1. [Quick start with Docker Repo](#docker)
2. [Conda Installation](#conda)
3. [Pip Installation](#pip)
4. [Build from Source](./source_build.md)


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## 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](https://hub.docker.com/r/rapidsai/base), choosing a tag based on the NVIDIA CUDA version you're running. Also, the [rapidsai/notebooks](https://hub.docker.com/r/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.

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## Conda

It is easy to install cuGraph using conda. You can get a minimal conda installation with [miniforge](https://github.com/conda-forge/miniforge).

cuGraph Conda packages
 * cugraph - this will also import:
   * pylibcugraph
   * libcugraph
 * cugraph-service-client
 * cugraph-service-server
 * cugraph-pyg
 * 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:

```bash
# CUDA 13
conda install -c rapidsai -c conda-forge -c nvidia cugraph cuda-version=13.0

# CUDA 12
conda install -c rapidsai -c conda-forge -c nvidia cugraph cuda-version=12.9
```

Note: This conda installation only applies to Linux and Python versions 3.10/3.11/3.12/3.13.

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

```shell
# CUDA 13
pip install cugraph-cu13 --extra-index-url=https://pypi.nvidia.com

# CUDA 12
pip install cugraph-cu12 --extra-index-url=https://pypi.nvidia.com
```

Also available:

* `cugraph-pyg-cu{12,13}`
* `nx-cugraph-cu{12, 13}`

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