Learn How To Contribute To RAPIDS


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.

Community Onboarding

Anyone can join our community and contribute to to RAPIDS in a five step onboarding process:

1. Install RAPIDS using Docker or Conda, or try it instantly in Colabratory.

2. Join our community conversations on Twitter, Google Groups, and Slack.

3. Explore our docs, walk through videos, blog posts, tutorial notebooks, and our examples workflows.

4. Build your own RAPIDS powered data science workflows.

5. Contribute back by reviewing our contribution guidelines, filing issues or submitting pull requests, and don’t forget to ask and answer questions on Stack Overflow.

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.


BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning.
Learn more on our BlazingSQL page


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


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.


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Open Source

Apache Arrow