DMLC/XGBoost

Open source

Scalable, Portable Gradient Boosting

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Top financial contributors

Organizations

1
NVIDIA

$9,900 USD since Mar 2019

2
Intel

$7,200 USD since Feb 2020

3
Financial Technology company

$3,600 USD since May 2020

4
Koffie Labs, Inc

$1,800 USD since May 2021

5
Upstart

$100 USD since Oct 2021

6
inaccel

$50 USD since Jul 2019

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Triplebyte

$50 USD since Feb 2020

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Salesforce

$5 USD since Jan 2020

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Real Targeted Traffic

$4 USD since Oct 2021

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Targeted Web Traffic

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Individuals

1
Hyunsu Cho

$3,041.91 USD since Feb 2019

2
Incognito

$430 USD since Dec 2019

3
Lee Drake

$280 USD since Aug 2019

4
Jeff Kaplan

$165 USD since Mar 2019

5
Ryan Holbrook

$135 USD since Sep 2019

6
Keisuke OGAKI

$120 USD since Nov 2019

7
Arnesh Sahay

$120 USD since Apr 2020

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David J Slate

$100 USD since Mar 2020

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Paul Kaefer

$95 USD since Apr 2020

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Nickalus Redell

$80 USD since Nov 2019

DMLC/XGBoost is all of us

Our contributors 34

Thank you for supporting DMLC/XGBoost.

Hyunsu Cho

Admin

$3,042 USD

Nan Zhu

Admin

NVIDIA

Sponsor (monthly)

$9,900 USD

Intel

Sponsor (yearly)

$7,200 USD

Koffie Labs, Inc

Sponsor (monthly)

$1,800 USD

incognito

$430 USD

Lee Drake

Backer

$280 USD

XGBoost has become central to my business, I am...

Jeff Kaplan

Backer

$165 USD

Budget


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$
Today’s balance

$1,961.86 USD

Total raised

$23,877.91 USD

Total disbursed

$21,916.05 USD

Estimated annual budget

$7,982.29 USD

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News from DMLC/XGBoost

Updates on our activities and progress.

End-of-year CI budget report

First of all, thanks all who have contributed to help XGBoost development going. I'd like to post the end-of-year summary of how your donation has been used.I had estimated the cloud cost to be 1000 USD/month, but thanks to active contri...
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Published on December 31, 2019 by Hyunsu Cho

About


DMLC/XGBoost is one of the most popular machine learning library for gradient boosting. It has grown from a research project incubated in academia to the most widely used gradient boosting framework in production environment. On one side, with the growth of volume and variety of data in the production environment, users are putting accordingly growing expectation to XGBoost in terms of more functions, scalability and robustness. On the other side, as an open source project which develops in a fast pace, XGBoost has been receiving contributions from many individuals and organizations around the world. Given the high expectation from the users and the increasing channels of contribution to the project, delivering the high quality software presents a challenge to the project maintainers.

A robust and efficient continuous integration (CI) infrastructure is one of the most critical solutions to address the above challenge. A CI service will monitor a open-source repository and run a suite of integration tests for every incoming contribution. This way, the CI ensures that every proposed change in the codebase is compatible with existing functionalities. Furthermore, XGBoost can enable more thorough tests with a powerful CI infrastructure to cover cases which are closer to the production environment.