%global _empty_manifest_terminate_build 0

Name:		python-gpytorch
Version:	1.11
Release:	1
Summary:	A highly efficient implementation of Gaussian Processes in PyTorch
License:	MIT
URL:		https://github.com/cornellius-gp/gpytorch
Source0:	https://github.com/cornellius-gp/gpytorch/archive/refs/tags/v%{version}.tar.gz#/%{name}-%{version}.tar.gz

Requires:	python3-pytorch

%description
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, 
flexible, and modular Gaussian process models with ease.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference); 
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility 
(3) easy integration with deep learning frameworks.


%package -n python3-gpytorch
Summary:	A highly efficient implementation of Gaussian Processes in PyTorch
Provides:	python-gpytorch

BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-setuptools_scm
BuildRequires:	python3-pbr
BuildRequires:	python3-pip
BuildRequires:	python3-wheel

%description -n python3-gpytorch
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, 
flexible, and modular Gaussian process models with ease.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference); 
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility 
(3) easy integration with deep learning frameworks.

%package help
Summary:	Development documents and examples for python-gpytorch
Provides:	python3-gpytorch-doc

%description help
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, 
flexible, and modular Gaussian process models with ease.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference); 
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility 
(3) easy integration with deep learning frameworks.

%prep
%autosetup -p1 -n gpytorch-%{version}

%build
export SETUPTOOLS_SCM_PRETEND_VERSION=%{version}
%pyproject_build

%install
%pyproject_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/doclist.lst .

%files -n python3-gpytorch
%doc *.md
%license LICENSE
%{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 1.11-1
- Package init