DMLC/XGBoost

Open source

Scalable, Portable Gradient Boosting

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+ 5
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Top financial contributors

Individuals

1
Hyunsu Cho

$3k USD since Feb 2019

2
Incognito

$380 USD since Dec 2019

3
Lee Drake

$230 USD since Aug 2019

4
Jeff Kaplan

$140 USD since Mar 2019

5
Ryan Holbrook

$110 USD since Sep 2019

6
David J Slate

$100 USD since Mar 2020

7
Keisuke OGAKI

$95 USD since Nov 2019

8
Nickalus Redell

$80 USD since Nov 2019

9
Arnesh Sahay

$70 USD since Apr 2020

10
Paul Kaefer

$70 USD since Apr 2020

Organizations

1
NVIDIA

$8k USD since Mar 2019

2
Intel

$7k USD since Feb 2020

3
Financial Technology company

$4k USD since May 2020

4
Koffie Labs, Inc

$300 USD since May 2021

5
inaccel

$50 USD since Jul 2019

6
Triplebyte

$50 USD since Feb 2020

7
Salesforce

$5 USD since Jan 2020

DMLC/XGBoost is all of us

Our contributors 31

Thank you for supporting DMLC/XGBoost.

Hyunsu Cho

Admin

$3,042 USD

Nan Zhu

Admin

NVIDIA

Sponsor (monthly)

$8,400 USD

Intel

Sponsor (yearly)

$7,200 USD

incognito

$380 USD

Koffie Labs, Inc

Sponsor (monthly)

$300 USD

Lee Drake

Backer

$230 USD

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

Jeff Kaplan

Backer

$140 USD

Budget


Transparent and open finances.

by Hyunsu ChoJune 10, 2021

-$1,056.96 USD
Paid
Receipt #42774
Monthly financial contribution to DMLC/XGBoost (Sponsor (...

from NVIDIA

+$300.00USD
Completed
Monthly financial contribution to DMLC/XGBoost (Backer)

from Keisuke OGAKI

+$5.00USD
Completed
$
Today’s balance

$4,441.23 USD

Total raised

$20,969.37 USD

Total disbursed

$16,528.14 USD

Estimated annual budget

$9,915.92 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...
Read more
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.