%global _empty_manifest_terminate_build 0
Name:		python-bayesmark
Version:	0.0.8
Release:	1
Summary:	Bayesian optimization benchmark system
License:	Apache v2
URL:		https://github.com/uber/bayesmark/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/fe/35/5b3ad7f835676f53ed219cfe70e2f190cadbec21a9570e794489f721c00f/bayesmark-0.0.8.tar.gz
BuildArch:	noarch


%description
This project provides a benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks.
This project is experimental and the APIs are not considered stable.
This Bayesian optimization (BO) benchmark framework requires a few easy steps for setup. It can be run either on a local machine (in serial) or prepare a *commands file* to run on a cluster as parallel experiments (dry run mode).
Only ``Python>=3.6`` is officially supported, but older versions of Python likely work as well.
The core package itself can be installed with:
   pip install bayesmark
However, to also require installation of all the "built in" optimizers for evaluation, run:
   pip install bayesmark[optimizers]
It is also possible to use the same pinned dependencies we used in testing by `installing from the repo <#install-in-editable-mode>`_.
Building an environment to run the included notebooks can be done with:
   pip install bayesmark[notebooks]
Or, ``bayesmark[optimizers,notebooks]`` can be used.
A quick example of running the benchmark is `here <#example>`_. The instructions are used to generate results as below:

%package -n python3-bayesmark
Summary:	Bayesian optimization benchmark system
Provides:	python-bayesmark
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-bayesmark
This project provides a benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks.
This project is experimental and the APIs are not considered stable.
This Bayesian optimization (BO) benchmark framework requires a few easy steps for setup. It can be run either on a local machine (in serial) or prepare a *commands file* to run on a cluster as parallel experiments (dry run mode).
Only ``Python>=3.6`` is officially supported, but older versions of Python likely work as well.
The core package itself can be installed with:
   pip install bayesmark
However, to also require installation of all the "built in" optimizers for evaluation, run:
   pip install bayesmark[optimizers]
It is also possible to use the same pinned dependencies we used in testing by `installing from the repo <#install-in-editable-mode>`_.
Building an environment to run the included notebooks can be done with:
   pip install bayesmark[notebooks]
Or, ``bayesmark[optimizers,notebooks]`` can be used.
A quick example of running the benchmark is `here <#example>`_. The instructions are used to generate results as below:

%package help
Summary:	Development documents and examples for bayesmark
Provides:	python3-bayesmark-doc
%description help
This project provides a benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks.
This project is experimental and the APIs are not considered stable.
This Bayesian optimization (BO) benchmark framework requires a few easy steps for setup. It can be run either on a local machine (in serial) or prepare a *commands file* to run on a cluster as parallel experiments (dry run mode).
Only ``Python>=3.6`` is officially supported, but older versions of Python likely work as well.
The core package itself can be installed with:
   pip install bayesmark
However, to also require installation of all the "built in" optimizers for evaluation, run:
   pip install bayesmark[optimizers]
It is also possible to use the same pinned dependencies we used in testing by `installing from the repo <#install-in-editable-mode>`_.
Building an environment to run the included notebooks can be done with:
   pip install bayesmark[notebooks]
Or, ``bayesmark[optimizers,notebooks]`` can be used.
A quick example of running the benchmark is `here <#example>`_. The instructions are used to generate results as below:

%prep
%autosetup -n bayesmark-0.0.8

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
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}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-bayesmark -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.8-1
- Package Spec generated