| Build-in Optimization Components | Optimization Algorithms | Optimization Services |
|
|
* [Tencent Inc.](https://www.tencent.com/en-us/)
* [Alibaba Group](https://www.alibabagroup.com/en-US/)
* [Kuaishou Technology](https://www.kuaishou.com/en)
## **Contributing**
OpenBox has a frequent release cycle. Please let us know if you encounter a bug by
[filling an issue](https://github.com/PKU-DAIR/open-box/issues/new/choose).
We appreciate all contributions. If you are planning to contribute any bug-fixes,
please create a [pull request](https://github.com/PKU-DAIR/open-box/pulls).
If you plan to contribute new features, new modules, etc. please first open an issue or reuse an existing issue,
and discuss the feature with us.
To learn more about making a contribution to OpenBox, please refer to our
[How-to contribution page](https://github.com/PKU-DAIR/open-box/blob/master/CONTRIBUTING.md).
We appreciate all contributions and thank all the contributors!
## **Feedback**
* [File an issue](https://github.com/PKU-DAIR/open-box/issues) on GitHub
* Email us via [*Yang Li*](https://thomas-young-2013.github.io/),
*shenyu@pku.edu.cn* or *jianghuaijun@pku.edu.cn*
* [Q&A] Join the QQ group: 227229622
## **Related Projects**
Targeting at openness and advancing AutoML ecosystems, we had also released few other open-source projects.
* [MindWare](https://github.com/PKU-DAIR/mindware): an open source system that provides end-to-end ML model training
and inference capabilities.
* [SGL](https://github.com/PKU-DAIR/SGL): a scalable graph learning toolkit for extremely large graph datasets.
* [HyperTune](https://github.com/PKU-DAIR/HyperTune): a large-scale multi-fidelity hyper-parameter tuning system.
## **Related Publications**
**OpenBox: A Generalized Black-box Optimization Service.**
Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu,
Zhi Yang, Ce Zhang, Bin Cui; KDD 2021, CCF-A.
https://arxiv.org/abs/2106.00421
**MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements.**
Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui; AAAI 2021, CCF-A.
https://arxiv.org/abs/2012.03011
**Transfer Learning based Search Space Design for Hyperparameter Tuning.**
Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02511
**TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02663
**PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm.**
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui;
WWW 2022, CCF-A, 🏆 Best Student Paper Award.
https://arxiv.org/abs/2203.00638
**Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui; VLDB 2022, CCF-A.
https://arxiv.org/abs/2201.06834
## **License**
The entire codebase is under [MIT license](LICENSE).
%package -n python3-openbox
Summary: Efficient and generalized blackbox optimization (BBO) system
Provides: python-openbox
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-openbox
[](
https://github.com/PKU-DAIR/open-box/blob/master/LICENSE)
[](
https://github.com/PKU-DAIR/open-box/issues?q=is%3Aissue+is%3Aopen)
[](
https://github.com/PKU-DAIR/open-box/pulls?q=is%3Apr+is%3Aopen)
[](
https://github.com/PKU-DAIR/open-box/releases)
[](https://github.com/PKU-DAIR/open-box/actions/workflows/test.yml)
[](
https://open-box.readthedocs.io/)
[OpenBox Documentation](https://open-box.readthedocs.io)
| [OpenBox中文文档](https://open-box.readthedocs.io/zh_CN/latest/)
| [中文README](https://github.com/PKU-DAIR/open-box/blob/master/README_zh_CN.md)
## OpenBox: Generalized and Efficient Blackbox Optimization System
**OpenBox** is an efficient and generalized blackbox optimization (BBO) system, which supports the following
characteristics: 1) **BBO with multiple objectives and constraints**, 2) **BBO with transfer learning**, 3)
**BBO with distributed parallelization**, 4) **BBO with multi-fidelity acceleration** and 5) **BBO with early stops**.
OpenBox is designed and developed by the AutoML team from the [DAIR Lab](http://net.pku.edu.cn/~cuibin/) at Peking
University, and its goal is to make blackbox optimization easier to apply both in industry and academia, and help
facilitate data science.
## Software Artifacts
#### Standalone Python package.
Users can install the released package and use it with Python.
#### Distributed BBO service.
We adopt the "BBO as a service" paradigm and implement OpenBox as a managed general service for black-box optimization.
Users can access this service via REST API conveniently, and do not need to worry about other issues such as environment
setup, software maintenance, programming, and optimization of the execution. Moreover, we also provide a Web UI,
through which users can easily track and manage the tasks.
## Design Goal
The design of OpenBox follows the following principles:
+ **Ease of use**: Minimal user effort, and user-friendly visualization for tracking and managing BBO tasks.
+ **Consistent performance**: Host state-of-the-art optimization algorithms; Choose the proper algorithm automatically.
+ **Resource-aware management**: Give cost-model-based advice to users, e.g., minimal workers or time-budget.
+ **Scalability**: Scale to dimensions on the number of input variables, objectives, tasks, trials, and parallel
evaluations.
+ **High efficiency**: Effective use of parallel resources, system optimization with transfer-learning and
multi-fidelities, etc.
+ **Fault tolerance**, **extensibility**, and **data privacy protection**.
## Links
+ [Documentations](https://open-box.readthedocs.io/en/latest/) |
[中文文档](https://open-box.readthedocs.io/zh_CN/latest/)
+ [Examples](https://github.com/PKU-DAIR/open-box/tree/master/examples)
+ [Pypi package](https://pypi.org/project/openbox/)
+ Conda package: [to appear soon]()
+ Blog post: [to appear soon]()
## News
+ **OpenBox** based solutions achieved the **First Place** of
[ACM CIKM 2021 AnalyticCup](https://www.cikm2021.org/analyticup)
(Track - Automated Hyperparameter Optimization of Recommendation System).
+ **OpenBox** team won the **Top Prize (special prize)** in the open-source innovation competition at
[2021 CCF ChinaSoft](http://chinasoft.ccf.org.cn/papers/chinasoft.html) conference.
+ [**Pasca**](https://github.com/PKU-DAIR/SGL), which adopts Openbox to support neural architecture search
functionality, won the **Best Student Paper Award at WWW'22**.
## OpenBox Capabilities in a Glance
| Build-in Optimization Components | Optimization Algorithms | Optimization Services |
|
|
* [Tencent Inc.](https://www.tencent.com/en-us/)
* [Alibaba Group](https://www.alibabagroup.com/en-US/)
* [Kuaishou Technology](https://www.kuaishou.com/en)
## **Contributing**
OpenBox has a frequent release cycle. Please let us know if you encounter a bug by
[filling an issue](https://github.com/PKU-DAIR/open-box/issues/new/choose).
We appreciate all contributions. If you are planning to contribute any bug-fixes,
please create a [pull request](https://github.com/PKU-DAIR/open-box/pulls).
If you plan to contribute new features, new modules, etc. please first open an issue or reuse an existing issue,
and discuss the feature with us.
To learn more about making a contribution to OpenBox, please refer to our
[How-to contribution page](https://github.com/PKU-DAIR/open-box/blob/master/CONTRIBUTING.md).
We appreciate all contributions and thank all the contributors!
## **Feedback**
* [File an issue](https://github.com/PKU-DAIR/open-box/issues) on GitHub
* Email us via [*Yang Li*](https://thomas-young-2013.github.io/),
*shenyu@pku.edu.cn* or *jianghuaijun@pku.edu.cn*
* [Q&A] Join the QQ group: 227229622
## **Related Projects**
Targeting at openness and advancing AutoML ecosystems, we had also released few other open-source projects.
* [MindWare](https://github.com/PKU-DAIR/mindware): an open source system that provides end-to-end ML model training
and inference capabilities.
* [SGL](https://github.com/PKU-DAIR/SGL): a scalable graph learning toolkit for extremely large graph datasets.
* [HyperTune](https://github.com/PKU-DAIR/HyperTune): a large-scale multi-fidelity hyper-parameter tuning system.
## **Related Publications**
**OpenBox: A Generalized Black-box Optimization Service.**
Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu,
Zhi Yang, Ce Zhang, Bin Cui; KDD 2021, CCF-A.
https://arxiv.org/abs/2106.00421
**MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements.**
Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui; AAAI 2021, CCF-A.
https://arxiv.org/abs/2012.03011
**Transfer Learning based Search Space Design for Hyperparameter Tuning.**
Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02511
**TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02663
**PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm.**
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui;
WWW 2022, CCF-A, 🏆 Best Student Paper Award.
https://arxiv.org/abs/2203.00638
**Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui; VLDB 2022, CCF-A.
https://arxiv.org/abs/2201.06834
## **License**
The entire codebase is under [MIT license](LICENSE).
%package help
Summary: Development documents and examples for openbox
Provides: python3-openbox-doc
%description help
[](
https://github.com/PKU-DAIR/open-box/blob/master/LICENSE)
[](
https://github.com/PKU-DAIR/open-box/issues?q=is%3Aissue+is%3Aopen)
[](
https://github.com/PKU-DAIR/open-box/pulls?q=is%3Apr+is%3Aopen)
[](
https://github.com/PKU-DAIR/open-box/releases)
[](https://github.com/PKU-DAIR/open-box/actions/workflows/test.yml)
[](
https://open-box.readthedocs.io/)
[OpenBox Documentation](https://open-box.readthedocs.io)
| [OpenBox中文文档](https://open-box.readthedocs.io/zh_CN/latest/)
| [中文README](https://github.com/PKU-DAIR/open-box/blob/master/README_zh_CN.md)
## OpenBox: Generalized and Efficient Blackbox Optimization System
**OpenBox** is an efficient and generalized blackbox optimization (BBO) system, which supports the following
characteristics: 1) **BBO with multiple objectives and constraints**, 2) **BBO with transfer learning**, 3)
**BBO with distributed parallelization**, 4) **BBO with multi-fidelity acceleration** and 5) **BBO with early stops**.
OpenBox is designed and developed by the AutoML team from the [DAIR Lab](http://net.pku.edu.cn/~cuibin/) at Peking
University, and its goal is to make blackbox optimization easier to apply both in industry and academia, and help
facilitate data science.
## Software Artifacts
#### Standalone Python package.
Users can install the released package and use it with Python.
#### Distributed BBO service.
We adopt the "BBO as a service" paradigm and implement OpenBox as a managed general service for black-box optimization.
Users can access this service via REST API conveniently, and do not need to worry about other issues such as environment
setup, software maintenance, programming, and optimization of the execution. Moreover, we also provide a Web UI,
through which users can easily track and manage the tasks.
## Design Goal
The design of OpenBox follows the following principles:
+ **Ease of use**: Minimal user effort, and user-friendly visualization for tracking and managing BBO tasks.
+ **Consistent performance**: Host state-of-the-art optimization algorithms; Choose the proper algorithm automatically.
+ **Resource-aware management**: Give cost-model-based advice to users, e.g., minimal workers or time-budget.
+ **Scalability**: Scale to dimensions on the number of input variables, objectives, tasks, trials, and parallel
evaluations.
+ **High efficiency**: Effective use of parallel resources, system optimization with transfer-learning and
multi-fidelities, etc.
+ **Fault tolerance**, **extensibility**, and **data privacy protection**.
## Links
+ [Documentations](https://open-box.readthedocs.io/en/latest/) |
[中文文档](https://open-box.readthedocs.io/zh_CN/latest/)
+ [Examples](https://github.com/PKU-DAIR/open-box/tree/master/examples)
+ [Pypi package](https://pypi.org/project/openbox/)
+ Conda package: [to appear soon]()
+ Blog post: [to appear soon]()
## News
+ **OpenBox** based solutions achieved the **First Place** of
[ACM CIKM 2021 AnalyticCup](https://www.cikm2021.org/analyticup)
(Track - Automated Hyperparameter Optimization of Recommendation System).
+ **OpenBox** team won the **Top Prize (special prize)** in the open-source innovation competition at
[2021 CCF ChinaSoft](http://chinasoft.ccf.org.cn/papers/chinasoft.html) conference.
+ [**Pasca**](https://github.com/PKU-DAIR/SGL), which adopts Openbox to support neural architecture search
functionality, won the **Best Student Paper Award at WWW'22**.
## OpenBox Capabilities in a Glance
| Build-in Optimization Components | Optimization Algorithms | Optimization Services |
|
|
* [Tencent Inc.](https://www.tencent.com/en-us/)
* [Alibaba Group](https://www.alibabagroup.com/en-US/)
* [Kuaishou Technology](https://www.kuaishou.com/en)
## **Contributing**
OpenBox has a frequent release cycle. Please let us know if you encounter a bug by
[filling an issue](https://github.com/PKU-DAIR/open-box/issues/new/choose).
We appreciate all contributions. If you are planning to contribute any bug-fixes,
please create a [pull request](https://github.com/PKU-DAIR/open-box/pulls).
If you plan to contribute new features, new modules, etc. please first open an issue or reuse an existing issue,
and discuss the feature with us.
To learn more about making a contribution to OpenBox, please refer to our
[How-to contribution page](https://github.com/PKU-DAIR/open-box/blob/master/CONTRIBUTING.md).
We appreciate all contributions and thank all the contributors!
## **Feedback**
* [File an issue](https://github.com/PKU-DAIR/open-box/issues) on GitHub
* Email us via [*Yang Li*](https://thomas-young-2013.github.io/),
*shenyu@pku.edu.cn* or *jianghuaijun@pku.edu.cn*
* [Q&A] Join the QQ group: 227229622
## **Related Projects**
Targeting at openness and advancing AutoML ecosystems, we had also released few other open-source projects.
* [MindWare](https://github.com/PKU-DAIR/mindware): an open source system that provides end-to-end ML model training
and inference capabilities.
* [SGL](https://github.com/PKU-DAIR/SGL): a scalable graph learning toolkit for extremely large graph datasets.
* [HyperTune](https://github.com/PKU-DAIR/HyperTune): a large-scale multi-fidelity hyper-parameter tuning system.
## **Related Publications**
**OpenBox: A Generalized Black-box Optimization Service.**
Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu,
Zhi Yang, Ce Zhang, Bin Cui; KDD 2021, CCF-A.
https://arxiv.org/abs/2106.00421
**MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements.**
Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui; AAAI 2021, CCF-A.
https://arxiv.org/abs/2012.03011
**Transfer Learning based Search Space Design for Hyperparameter Tuning.**
Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02511
**TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui; KDD 2022, CCF-A.
https://arxiv.org/abs/2206.02663
**PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm.**
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui;
WWW 2022, CCF-A, 🏆 Best Student Paper Award.
https://arxiv.org/abs/2203.00638
**Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale.**
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui; VLDB 2022, CCF-A.
https://arxiv.org/abs/2201.06834
## **License**
The entire codebase is under [MIT license](LICENSE).
%prep
%autosetup -n openbox-0.8.1
%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-openbox -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot