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Add links to the docs site in the README #1042

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23 changes: 22 additions & 1 deletion README.md
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# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;RAFT: Reusable Accelerated Functions and Tools</div>

[![Build Status](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/raft/job/branches/job/raft-branch-pipeline/badge/icon)](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/raft/job/branches/job/raft-branch-pipeline/)

## Resources

- [RAFT Reference Documentation](https://docs.rapids.ai/api/raft/stable/): API Documentation.
- [RAFT Getting Started](./docs/source/quick_start.md): Getting started with RAFT.
- [Build and Install RAFT](./docs/source/build.md): Instructions for installing and building RAFT.
- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
- [GitHub repository](https://github.com/rapidsai/raft): Download the RAFT source code.
- [Issue tracker](https://github.com/rapidsai/raft/issues): Report issues or request features.

## Overview

RAFT contains fundamental widely-used algorithms and primitives for data science and machine learning. The algorithms are CUDA-accelerated and form building-blocks for rapidly composing analytics.

By taking a primitives-based approach to algorithm development, RAFT
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## Installing

RAFT itself can be installed through conda, [Cmake Package Manager (CPM)](https://github.com/cpm-cmake/CPM.cmake), or by building the repository from source. Please refer to the [build instructions](docs/source/build.md) for more a comprehensive guide on building RAFT and using it in downstream projects.
RAFT itself can be installed through conda, [Cmake Package Manager (CPM)](https://github.com/cpm-cmake/CPM.cmake), pip, or by building the repository from source. Please refer to the [build instructions](docs/source/build.md) for more a comprehensive guide on building RAFT and using it in downstream projects.

### Conda

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After installing RAFT, `find_package(raft COMPONENTS nn distance)` can be used in your CUDA/C++ cmake build to compile and/or link against needed dependencies in your raft target. `COMPONENTS` are optional and will depend on the packages installed.

### Pip

pylibraft and raft-dask both have experimental packages that can be [installed through pip](https://rapids.ai/pip.html#install):
```bash
pip install pylibraft-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
pip install raft-dask-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
```

### Cmake & CPM

RAFT uses the [RAPIDS-CMake](https://github.com/rapidsai/rapids-cmake) library, which makes it simple to include in downstream cmake projects. RAPIDS CMake provides a convenience layer around CPM.
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