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* Initial commit to support multi-node multi-gpu xgboost using dask * Fixed NCCL initialization by not ignoring the opg parameter. - it now crashes on NCCL initialization, but at least we're attempting it properly * At the root node, perform a rabit::Allreduce to get initial sum_gradient across workers * Synchronizing in a couple of more places. - now the workers don't go down, but just hang - no more "wild" values of gradients - probably needs syncing in more places * Added another missing max-allreduce operation inside BuildHistLeftRight * Removed unnecessary collective operations. * Simplified rabit::Allreduce() sync of gradient sums. * Removed unnecessary rabit syncs around ncclAllReduce. - this improves performance _significantly_ (7x faster for overall training, 20x faster for xgboost proper) * pulling in latest xgboost * removing changes to updater_quantile_hist.cc * changing use_nccl_opg initialization, removing unnecessary if statements * added definition for opaque ncclUniqueId struct to properly encapsulate GetUniqueId * placing struct defintion in guard to avoid duplicate code errors * addressing linting errors * removing * removing additional arguments to AllReduer initialization * removing distributed flag * making comm init symmetric * removing distributed flag * changing ncclCommInit to support multiple modalities * fix indenting * updating ncclCommInitRank block with necessary group calls * fix indenting * adding print statement, and updating accessor in vector * improving print statement to end-line * generalizing nccl_rank construction using rabit * assume device_ordinals is the same for every node * test, assume device_ordinals is identical for all nodes * test, assume device_ordinals is unique for all nodes * changing names of offset variable to be more descriptive, editing indenting * wrapping ncclUniqueId GetUniqueId() and aesthetic changes * adding synchronization, and tests for distributed * adding to tests * fixing broken #endif * fixing initialization of gpu histograms, correcting errors in tests * adding to contributors list * adding distributed tests to jenkins * fixing bad path in distributed test * debugging * adding kubernetes for distributed tests * adding proper import for OrderedDict * adding urllib3==1.22 to address ordered_dict import error * added sleep to allow workers to save their models for comparison * adding name to GPU contributors under docs
91 lines
5.6 KiB
Markdown
91 lines
5.6 KiB
Markdown
Contributors of DMLC/XGBoost
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============================
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XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
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Committers
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----------
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Committers are people who have made substantial contribution to the project and granted write access to the project.
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* [Tianqi Chen](https://github.com/tqchen), University of Washington
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- Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
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* [Tong He](https://github.com/hetong007), Amazon AI
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- Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
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* [Vadim Khotilovich](https://github.com/khotilov)
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- Vadim contributes many improvements in R and core packages.
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* [Bing Xu](https://github.com/antinucleon)
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- Bing is the original creator of XGBoost Python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
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* [Michael Benesty](https://github.com/pommedeterresautee)
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- Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
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* [Yuan Tang](https://github.com/terrytangyuan), Ant Financial
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- Yuan is a software engineer in Ant Financial. He contributed mostly in R and Python packages.
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* [Nan Zhu](https://github.com/CodingCat), Uber
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- Nan is a software engineer in Uber. He contributed mostly in JVM packages.
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* [Sergei Lebedev](https://github.com/superbobry), Criteo
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- Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
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* [Hongliang Liu](https://github.com/phunterlau)
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* [Scott Lundberg](http://scottlundberg.com/), University of Washington
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- Scott is a Ph.D. student at University of Washington. He is the creator of SHAP, a unified approach to explain the output of machine learning models such as decision tree ensembles. He also helps maintain the XGBoost Julia package.
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* [Rory Mitchell](https://github.com/RAMitchell), University of Waikato
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- Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration.
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* [Hyunsu Cho](http://hyunsu-cho.io/), Amazon AI
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- Hyunsu is an applied scientist in Amazon AI. He is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater.
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* [Jiaming](https://github.com/trivialfis)
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- Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.
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Become a Committer
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------------------
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XGBoost is a opensource project and we are actively looking for new committers who are willing to help maintaining and lead the project.
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Committers comes from contributors who:
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* Made substantial contribution to the project.
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* Willing to spent time on maintaining and lead the project.
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New committers will be proposed by current committer members, with support from more than two of current committers.
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List of Contributors
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--------------------
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* [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
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- To contributors: please add your name to the list when you submit a patch to the project:)
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* [Kailong Chen](https://github.com/kalenhaha)
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- Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
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* [Skipper Seabold](https://github.com/jseabold)
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- Skipper is the major contributor to the scikit-learn module of XGBoost.
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* [Zygmunt Zając](https://github.com/zygmuntz)
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- Zygmunt is the master behind the early stopping feature frequently used by kagglers.
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* [Ajinkya Kale](https://github.com/ajkl)
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* [Boliang Chen](https://github.com/cblsjtu)
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* [Yangqing Men](https://github.com/yanqingmen)
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- Yangqing is the creator of XGBoost java package.
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* [Engpeng Yao](https://github.com/yepyao)
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* [Giulio](https://github.com/giuliohome)
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- Giulio is the creator of Windows project of XGBoost
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* [Jamie Hall](https://github.com/nerdcha)
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- Jamie is the initial creator of XGBoost scikit-learn module.
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* [Yen-Ying Lee](https://github.com/white1033)
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* [Masaaki Horikoshi](https://github.com/sinhrks)
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- Masaaki is the initial creator of XGBoost Python plotting module.
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* [daiyl0320](https://github.com/daiyl0320)
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- daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
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* [Huayi Zhang](https://github.com/irachex)
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* [Johan Manders](https://github.com/johanmanders)
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* [yoori](https://github.com/yoori)
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* [Mathias Müller](https://github.com/far0n)
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* [Sam Thomson](https://github.com/sammthomson)
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* [ganesh-krishnan](https://github.com/ganesh-krishnan)
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* [Damien Carol](https://github.com/damiencarol)
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* [Alex Bain](https://github.com/convexquad)
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* [Baltazar Bieniek](https://github.com/bbieniek)
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* [Adam Pocock](https://github.com/Craigacp)
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* [Gideon Whitehead](https://github.com/gaw89)
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* [Yi-Lin Juang](https://github.com/frankyjuang)
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* [Andrew Hannigan](https://github.com/andrewhannigan)
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* [Andy Adinets](https://github.com/canonizer)
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* [Henry Gouk](https://github.com/henrygouk)
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* [Pierre de Sahb](https://github.com/pdesahb)
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* [liuliang01](https://github.com/liuliang01)
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- liuliang01 added support for the qid column for LibSVM input format. This makes ranking task easier in distributed setting.
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* [Andrew Thia](https://github.com/BlueTea88)
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- Andrew Thia implemented feature interaction constraints
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* [Wei Tian](https://github.com/weitian)
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* [Chen Qin](https://github.com/chenqin)
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* [Sam Wilkinson](https://samwilkinson.io)
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* [Matthew Jones](https://github.com/mt-jones)
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