# 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 container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), 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.

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## Conda
It is easy to install cuGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download).

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:

```bash
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.

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## 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

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