Learn How To Contribute To RAPIDS

CONTRIBUTE

Community and Projects

The RAPIDS team is developing, contributing, and collaborating closely with numerous open-source projects including Apache Arrow, Numba, XGBoost, Apache Spark, scikit-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

RAPIDS + BlazingSQL

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

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

xgboost

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

Plotly

RAPIDS + Plotly Dash

Plotly’s Dash enables Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of RAPIDS-accelerated Python dataframes.
Learn more on our Plotly page

HPO

RAPIDS + HPO

Accelerate Hyperparameter Optimization (HPO) in the Cloud. The RAPIDS team works closely with major cloud providers and open source hyperparameter optimization solutions to ensure smooth integration and high performance, regardless of your deployment platform.
Learn more on our HPO page

cloud

RAPIDS + Cloud

RAPIDS’s GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables.
Learn more on our cloud page

merlin

RAPIDS + NVIDIA MERLIN

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems. Merlin leverages RAPIDs cuDF and dask cuDF for dataframe transformation during ETL and inference, as well as for the optimized dataloaders in TensorFlow, PyTorch or HugeCTR to accelerate deep learning training.
Learn more on our Merlin page

slurm

RAPIDS + HPC

RAPIDS works extremely well in traditional HPC environments where GPUs are often co-located with accelerated networking hardware such as InfiniBand.
Learn more on our HPC page

spark

RAPIDS + Spark

NVIDIA is bringing RAPIDS to Apache Spark to accelerate ETL workflows with GPUs.
Learn more on the RAPIDS for Apache Spark page

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 with Colaboratory .

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.

Contributors

anaconda
blazingsql
capitalOne
chainer
cuPY
deepwave-digital
gunrock
nvidia
quantsight
walmart Global Tech

Adopters

anyscale
booz-allen-hamilton
capitalOne
databricks
deepwave-digital
graphistry
h20ai
ibm
iguzaio
inria
kinetica
mapr
omnisci
plotly dash
preferred-networks
pytorch
ray
saturn-cloud
uber
ursa
walmart

Open Source

Apache Arrow
blazingsql
Dask
GoAi
nuclio
Numba
scikitlearn
XGboost