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+%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