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The `ray-pipeline` library provides facilities for defining and running parallel pipelines on top of [Ray](https://ray.io). The goal of this project is to unify pipeline workflows across multiple platforms such as [scikit-learn](https://scikit-learn.org/) and [Apache Spark](https://spark.apache.org/), while providing nearly optimal scale-out parallelism on pipelined computations.
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## Release status
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This project is under active development. Keep an eye on this page for our first public release!
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See the [design document](https://docs.google.com/document/d/1t1K8N07TcbBKBgrcI6jf9tPow00cOKE9whnEVxOd4-U/edit) for more information on our design goals.
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## Example notebooks
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**TODO:** Add instructions for running the notebooks in the `notebooks` directory.
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## Building
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**TODO:** Add instructions for building from source once `setup.py` has been cleaned up.
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## Contributing
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**TODO:** Create a `CONTRIBUTING.md` file and add high-level instructions here
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