# 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)
## 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.
## 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-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:
```bash
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 3.10/3.11/3.12.
## 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