Bug#1008572: ITP: xgboost-predictor-java -- Java implementation of XGBoost predictor for online prediction tasks
On Tue, 2022-03-29 at 21:09 +0200, Pierre Gruet wrote: > > > > > This team is dedicated to hardware acceleration, > > machine learning, and deep learning. See > > debian...@lists.debian.org > > > > Now subscribed! > > By the way, does the team have some policy? Or does it inherit its > policy from Debian Science or whatever? > Many packages in the team were moved from the science team. Simply following the science team policy is good enough.
Bug#1008572: ITP: xgboost-predictor-java -- Java implementation of XGBoost predictor for online prediction tasks
Hi Mo, Le 28/03/2022 à 22:39, M. Zhou a écrit : Hi Pierre, The original C++/Python implementation xgboost is maintained by deep learning team: https://salsa.debian.org/deeplearning-team/xgboost I have assigned the whole debian science team with maintainer access (max role) to deep learning team. You may choose to maintain the package there if you like. Thanks for reading my bug report and taking time to write this; yes, I think it is a relevant place to maintain xgboost-predictor-java. I will take this into account while packaging! This team is dedicated to hardware acceleration, machine learning, and deep learning. See debian...@lists.debian.org Now subscribed! By the way, does the team have some policy? Or does it inherit its policy from Debian Science or whatever? Best regards, -- Pierre
Bug#1008572: ITP: xgboost-predictor-java -- Java implementation of XGBoost predictor for online prediction tasks
Hi Pierre, The original C++/Python implementation xgboost is maintained by deep learning team: https://salsa.debian.org/deeplearning-team/xgboost I have assigned the whole debian science team with maintainer access (max role) to deep learning team. You may choose to maintain the package there if you like. This team is dedicated to hardware acceleration, machine learning, and deep learning. See debian...@lists.debian.org On Mon, 2022-03-28 at 21:36 +0200, Pierre Gruet wrote: > Package: wnpp > Severity: wishlist > Owner: Pierre Gruet > User: debian-scie...@lists.debian.org > Usertags: field..science > X-Debbugs-Cc: debian-de...@lists.debian.org, > debian-scie...@lists.debian.org > > * Package name : xgboost-predictor-java > Version : 0.3.1 > Upstream Author : KOMIYA Atsushi > * URL : > https://github.com/komiya-atsushi/xgboost-predictor-java > * License : Apache-2.0 > Programming Lang: Java > Description : Java implementation of XGBoost predictor for > online prediction tasks > > XGBoost is an optimized distributed gradient boosting library > designed to be > highly efficient, flexible and portable. It implements machine > learning > algorithms under the Gradient Boosting framework. XGBoost provides a > parallel > tree boosting (also known as GBDT, GBM) that solve many data science > problems > in a fast and accurate way. The same code runs on major distributed > environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve > problems beyond > billions of examples. > > This is the Java implementation of XGBoost. Right now it is needed as > a > dependency of gatk, but it should be useful more broadly for > scientists or > people from machine learning. > It will be team-maintained in Debian Science team. >
Bug#1008572: ITP: xgboost-predictor-java -- Java implementation of XGBoost predictor for online prediction tasks
Package: wnpp Severity: wishlist Owner: Pierre Gruet User: debian-scie...@lists.debian.org Usertags: field..science X-Debbugs-Cc: debian-de...@lists.debian.org, debian-scie...@lists.debian.org * Package name: xgboost-predictor-java Version : 0.3.1 Upstream Author : KOMIYA Atsushi * URL : https://github.com/komiya-atsushi/xgboost-predictor-java * License : Apache-2.0 Programming Lang: Java Description : Java implementation of XGBoost predictor for online prediction tasks XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples. This is the Java implementation of XGBoost. Right now it is needed as a dependency of gatk, but it should be useful more broadly for scientists or people from machine learning. It will be team-maintained in Debian Science team.
