Learn How To Contribute To RAPIDS

CONTRIBUTE

RAPIDS Community

RAPIDS is for everyone: users, adopters, and contributors. If you’re a data scientist, researcher, engineer, or developer using pandas, Dask, scikit-learn, or Spark on CPUs, our RAPIDS projects are almost drop in replacements that can speed up your end-to-end workflow up to 50x.

Open Source

RAPIDS is open sourced under the Apache 2.0 license and is intended to be improved and extended upon by help from the community. While significant time and effort have been invested into making the platform to date, we need active contributors to help build its future.

Become a Contributor

Contributors include anyone who helps improve any of our projects, whether by contributing code or reporting issues. We particularly love hearing about how you have used RAPIDS, so give us a mention on Twitter. So get started with RAPIDS, fork our repositories from GitHub, start building, and open a pull request. You can also join us on Google Groups or Slack to ask questions and let us know what you’re working on, too.

Community Projects

The RAPIDS team is developing, contributing, and collaborating closely with numerous open-source projects including Apache Arrow, Numba, XGBoost, Apache Spark, sci-kit learn, and more. Our goal is to upstream all code contributions to ensure that all the components of the GPU-accelerated data science ecosystem work smoothly together.

RAPIDS + Dask

Dask is an open source project providing advanced parallelism for analytics that enables performance at scale. RAPIDS is actively contributing to Dask, and it integrates with both RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning.
Learn more on our Dask page

RAPIDS + XGBoost

XGBoost is a well-known gradient boosted decision trees (GBDT) machine learning package used to tackle regression, classification, and ranking problems. The RAPIDS team works closely with the Distributed Machine Learning Common (DMLC) XGBoost organization to upstream code and ensure that all components of the GPU-accelerated analytics ecosystem work smoothly together.
Learn more on our XGBoost page

RAPIDS + Spark

The RAPIDS team is working with the community to build a distributed, open source XGBoost4J-Spark + RAPIDS package. More details coming soon.

Contributors

anaconda
blazingdb
gunrock
nvidia
quantsight
walmart labs

Open Source

Apache Arrow
Dask
GoAi
Numba
scikitlearn
XGboost