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@@ -0,0 +1 @@ +/bayesmark-0.0.8.tar.gz diff --git a/python-bayesmark.spec b/python-bayesmark.spec new file mode 100644 index 0000000..21ede43 --- /dev/null +++ b/python-bayesmark.spec @@ -0,0 +1,108 @@ +%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 @@ -0,0 +1 @@ +4ce91a49e53a50acb90e4b1d81507047 bayesmark-0.0.8.tar.gz |