File R-mlr3torch.spec of Package R-mlr3torch

# Automatically generated by CRAN2OBS
# 
# Spec file for package mlr3torch 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2025 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  mlr3torch 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.2.1 
Release:        0 
Summary:        Deep Learning with 'mlr3' 
Group:          Development/Libraries/Other 
License:        LGPL (>= 3) 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         mlr3torch_0.2.1.tar.gz 
Requires:       R-base 
Requires:	R-mlr3
Requires:	R-mlr3pipelines
Requires:	R-torch
Requires:	R-backports
Requires:	R-checkmate
Requires:	R-data.table
Requires:	R-lgr
Requires:	R-mlr3misc
Requires:	R-paradox
Requires:	R-R6
Requires:	R-withr
Requires:	R-evaluate
Requires:	R-future
Requires:	R-future.apply
Requires:	R-mlbench
Requires:	R-mlr3measures
Requires:	R-parallelly
Requires:	R-palmerpenguins
Requires:	R-uuid
Requires:	R-cli
Requires:	R-digest
Requires:	R-Rcpp
Requires:	R-rlang
Requires:	R-bit64
Requires:	R-magrittr
Requires:	R-coro
Requires:	R-callr
Requires:	R-glue
Requires:	R-desc
Requires:	R-safetensors
Requires:	R-jsonlite
Requires:	R-scales
Requires:	R-bit
Requires:	R-processx
Requires:	R-globals
Requires:	R-listenv
Requires:	R-PRROC
Requires:	R-farver
Requires:	R-labeling
Requires:	R-lifecycle
Requires:	R-RColorBrewer
Requires:	R-viridisLite
Requires:	R-ps
 
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse} 
# # Three others commonly needed 
# BuildRequires:  tex(ae.sty) 
# BuildRequires:  tex(fancyvrb.sty) 
# BuildRequires:  tex(inconsolata.sty) 
# BuildRequires:  tex(natbib.sty) 
# %else 
# BuildRequires:  texlive 
# %endif 
# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
BuildRequires: 	R-mlr3
BuildRequires: 	R-mlr3pipelines
BuildRequires: 	R-torch
BuildRequires: 	R-backports
BuildRequires: 	R-checkmate
BuildRequires: 	R-data.table
BuildRequires: 	R-lgr
BuildRequires: 	R-mlr3misc
BuildRequires: 	R-paradox
BuildRequires: 	R-R6
BuildRequires: 	R-withr
BuildRequires: 	R-evaluate
BuildRequires: 	R-future
BuildRequires: 	R-future.apply
BuildRequires: 	R-mlbench
BuildRequires: 	R-mlr3measures
BuildRequires: 	R-parallelly
BuildRequires: 	R-palmerpenguins
BuildRequires: 	R-uuid
BuildRequires: 	R-cli
BuildRequires: 	R-digest
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-rlang
BuildRequires: 	R-bit64
BuildRequires: 	R-magrittr
BuildRequires: 	R-coro
BuildRequires: 	R-callr
BuildRequires: 	R-glue
BuildRequires: 	R-desc
BuildRequires: 	R-safetensors
BuildRequires: 	R-jsonlite
BuildRequires: 	R-scales
BuildRequires: 	R-bit
BuildRequires: 	R-processx
BuildRequires: 	R-globals
BuildRequires: 	R-listenv
BuildRequires: 	R-PRROC
BuildRequires: 	R-farver
BuildRequires: 	R-labeling
BuildRequires: 	R-lifecycle
BuildRequires: 	R-RColorBrewer
BuildRequires: 	R-viridisLite
BuildRequires: 	R-ps
 
Suggests:	R-callr
Suggests:	R-curl
Suggests:	R-future
Suggests:	R-ggplot2
Suggests:	R-igraph
Suggests:	R-jsonlite
Suggests:	R-knitr
Suggests:	R-mlr3tuning
Suggests:	R-progress
Suggests:	R-rmarkdown
Suggests:	R-viridis
Suggests:	R-visNetwork
Suggests:	R-testthat
Suggests:	R-tfevents
Suggests:	R-torchvision
Suggests:	R-waldo
%description 
Deep Learning library that extends the mlr3 framework by building upon 
the 'torch' package. It allows to conveniently build, train, and 
evaluate deep learning models without having to worry about low level 
details. Custom architectures can be created using the graph language 
defined in 'mlr3pipelines'. 
 
%prep 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
 
 
%build 
 
%install 
mkdir -p %{buildroot}%{rlibdir} 
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} 
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) 
rm -f %{buildroot}%{rlibdir}/R.css 
%fdupes -s %{buildroot}%{rlibdir} 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%{rlibdir}/%{packname}/COPYRIGHTS
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/WORDLIST
%{rlibdir}/%{packname}/col_info
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog