[R-pkg-devel] CRAN Submission xgboost 1.7.11.1

[jiaming yuan]

Hi CRAN,

May I ask if someone has tried to reproduce the openblas test environment from CRAN? We are trying to resolve the test failures of XGBoost but so far no one has managed to reproduce them locally. https://github.com/dmlc/xgboost/issues/11431

Would be great if you can share some guidance on how to reproduce that exact environment.

Cheers
Jiaming
________________________________
From: jiaming yuan <jm.yuan using outlook.com>
Sent: Friday, May 9, 2025 12:04:45 PM
To: Uwe Ligges <ligges using statistik.tu-dortmund.de>; CRAN <cran-submissions using r-project.org>
Cc: CRAN Package Submission Form <cransubmit using xmbombadil.wu.ac.at>
Subject: Re: CRAN Submission xgboost 1.7.11.1

Hi,

Others kindly provided help to reproduce the failure but so far no one has managed to do so.

Please see  https://github.com/dmlc/xgboost/issues/11431#issuecomment-2864947065  and related discussions the thread. Would be great if you can share something more precise.


Cheers
Jiaming

________________________________
From: Uwe Ligges <ligges using statistik.tu-dortmund.de>
Sent: Wednesday, May 7, 2025 9:48:56 PM
To: jiaming yuan <jm.yuan using outlook.com>; CRAN <cran-submissions using r-project.org>
Cc: CRAN Package Submission Form <cransubmit using xmbombadil.wu.ac.at>
Subject: Re: CRAN Submission xgboost 1.7.11.1

Note this is relevant, as most Linux clusters will have admis who will
link scientifiv software aagainst OpenBLAS for faster matriox operations.

Best,
Uwe Ligges

On 07.05.2025 15:48, Uwe Ligges wrote:
> I think any Liniux system with R linked against the system's default
> OpenBLAS installation will show this issue.
> I'd try it with a standard Ubuntu or Debian with OpenBLAS installed and
> link agasinst it.
>
> Best,
> Uwe Ligges
>
>
>
> On 03.05.2025 08:29, jiaming yuan wrote:
>> Thank you for reaching out. We can't really dive into it unless
>> there's an easier way to reproduce the environment (like a container
>> or using some deterministic package managers). It's very unlikely that
>> we can try to build that environment on our own then try to fix all
>> errors and verify all fixes.
>>
>>
>>
>> ________________________________
>> From: Uwe Ligges <ligges using statistik.tu-dortmund.de>
>> Sent: Friday, May 2, 2025 8:00:17 PM
>> To: Jiaming Yuan <jm.yuan using outlook.com>; CRAN <cran-submissions using r-
>> project.org>
>> Cc: CRAN Package Submission Form <cransubmit using xmbombadil.wu.ac.at>
>> Subject: Re: CRAN Submission xgboost 1.7.11.1
>>
>> Thanks, we see you removed lots of tests. Is this really sensible and
>> are you sure that users with OpenBLAS (as most Linux users and cluster
>> admins will use) will get correct results? Sensibly relaxing numerical
>> assumptions may be a better way to tweak the tests.?
>>
>> Best,
>> Uwe Ligges
>>
>>
>> On 01.05.2025 12:58, CRAN Package Submission Form via CRAN-submissions
>> wrote:
>>> [This was generated from CRAN.R-project.org/submit.html]
>>>
>>> The following package was uploaded to CRAN:
>>> ===========================================
>>>
>>> Package Information:
>>> Package: xgboost
>>> Version: 1.7.11.1
>>> Title: Extreme Gradient Boosting
>>> Author(s): Tianqi Chen [aut], Tong He [aut], Michael Benesty [aut],
>>> Vadim
>>> Khotilovich [aut], Yuan Tang [aut]
>>> (<https://orcid.org/0000-0001-5243-233X>), Hyunsu Cho [aut],
>>> Kailong Chen [aut], Rory Mitchell [aut], Ignacio Cano [aut],
>>> Tianyi Zhou [aut], Mu Li [aut], Junyuan Xie [aut], Min Lin
>>> [aut], Yifeng Geng [aut], Yutian Li [aut], Jiaming Yuan [aut,
>>> cre], XGBoost contributors [cph] (base XGBoost implementation)
>>> Maintainer: Jiaming Yuan <jm.yuan using outlook.com>
>>> Depends: R (>= 3.3.0)
>>> Suggests: knitr, rmarkdown, ggplot2 (>= 1.0.1), DiagrammeR (>= 0.9.0),
>>> Ckmeans.1d.dp (>= 3.3.1), vcd (>= 1.3), cplm, e1071, caret,
>>> testthat, lintr, igraph (>= 1.0.1), float, crayon, titanic
>>> Description: Extreme Gradient Boosting, which is an efficient
>>> implementation of the gradient boosting framework from Chen &
>>> Guestrin (2016) <doi:10.1145/2939672.2939785>. This package
>>> is its R interface. The package includes efficient linear
>>> model solver and tree learning algorithms. The package can
>>> automatically do parallel computation on a single machine
>>> which could be more than 10 times faster than existing
>>> gradient boosting packages. It supports various objective
>>> functions, including regression, classification and ranking.
>>> The package is made to be extensible, so that users are also
>>> allowed to define their own objectives easily.
>>> License: Apache License (== 2.0) | file LICENSE
>>> Imports: Matrix (>= 1.1-0), methods, data.table (>= 1.9.6), jsonlite
>>> (>= 1.0),
>>>
>>>
>>> The maintainer confirms that he or she
>>> has read and agrees to the CRAN policies.
>>>
>>> =================================================
>>>
>>> Original content of DESCRIPTION file:
>>>
>>> Package: xgboost
>>> Type: Package
>>> Title: Extreme Gradient Boosting
>>> Version: 1.7.11.1
>>> Date: 2025-05-01
>>> Authors using R: c(
>>> person("Tianqi", "Chen", role = c("aut"),
>>> email = "tianqi.tchen using gmail.com"),
>>> person("Tong", "He", role = c("aut"),
>>> email = "hetong007 using gmail.com"),
>>> person("Michael", "Benesty", role = c("aut"),
>>> email = "michael using benesty.fr"),
>>> person("Vadim", "Khotilovich", role = c("aut"),
>>> email = "khotilovich using gmail.com"),
>>> person("Yuan", "Tang", role = c("aut"),
>>> email = "terrytangyuan using gmail.com",
>>> comment = c(ORCID = "0000-0001-5243-233X")),
>>> person("Hyunsu", "Cho", role = c("aut"),
>>> email = "chohyu01 using cs.washington.edu"),
>>> person("Kailong", "Chen", role = c("aut")),
>>> person("Rory", "Mitchell", role = c("aut")),
>>> person("Ignacio", "Cano", role = c("aut")),
>>> person("Tianyi", "Zhou", role = c("aut")),
>>> person("Mu", "Li", role = c("aut")),
>>> person("Junyuan", "Xie", role = c("aut")),
>>> person("Min", "Lin", role = c("aut")),
>>> person("Yifeng", "Geng", role = c("aut")),
>>> person("Yutian", "Li", role = c("aut")),
>>> person("Jiaming", "Yuan", role = c("aut", "cre"),
>>> email = "jm.yuan using outlook.com"),
>>> person("XGBoost contributors", role = c("cph"),
>>> comment = "base XGBoost implementation")
>>> )
>>> Maintainer: Jiaming Yuan <jm.yuan using outlook.com>
>>> Description: Extreme Gradient Boosting, which is an efficient
>>> implementation
>>> of the gradient boosting framework from Chen & Guestrin (2016)
>>> <doi:10.1145/2939672.2939785>.
>>> This package is its R interface. The package includes efficient linear
>>> model solver and tree learning algorithms. The package can automatically
>>> do parallel computation on a single machine which could be more than 10
>>> times faster than existing gradient boosting packages. It supports
>>> various objective functions, including regression, classification and
>>> ranking.
>>> The package is made to be extensible, so that users are also allowed
>>> to define
>>> their own objectives easily.
>>> License: Apache License (== 2.0) | file LICENSE
>>> URL: https://github.com/dmlc/xgboost
>>> BugReports: https://github.com/dmlc/xgboost/issues
>>> NeedsCompilation: yes
>>> VignetteBuilder: knitr
>>> Suggests: knitr, rmarkdown, ggplot2 (>= 1.0.1), DiagrammeR (>= 0.9.0),
>>> Ckmeans.1d.dp (>= 3.3.1), vcd (>= 1.3), cplm, e1071, caret,
>>> testthat, lintr, igraph (>= 1.0.1), float, crayon, titanic
>>> Depends: R (>= 3.3.0)
>>> Imports: Matrix (>= 1.1-0), methods, data.table (>= 1.9.6), jsonlite
>>> (>= 1.0),
>>> RoxygenNote: 7.3.2
>>> Encoding: UTF-8
>>> SystemRequirements: GNU make, C++17
>>> Packaged: 2025-05-01 10:56:14 UTC; jiamingy
>>> Author: Tianqi Chen [aut],
>>> Tong He [aut],
>>> Michael Benesty [aut],
>>> Vadim Khotilovich [aut],
>>> Yuan Tang [aut] (<https://orcid.org/0000-0001-5243-233X>),
>>> Hyunsu Cho [aut],
>>> Kailong Chen [aut],
>>> Rory Mitchell [aut],
>>> Ignacio Cano [aut],
>>> Tianyi Zhou [aut],
>>> Mu Li [aut],
>>> Junyuan Xie [aut],
>>> Min Lin [aut],
>>> Yifeng Geng [aut],
>>> Yutian Li [aut],
>>> Jiaming Yuan [aut, cre],
>>> XGBoost contributors [cph] (base XGBoost implementation)
>>>
>>
>>
>>
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>>
>



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