path: root/python-dragonfly-opt.spec

%global _empty_manifest_terminate_build 0
Name:		python-dragonfly-opt
Version:	0.1.7
Release:	1
Summary:	please add a summary manually as the author left a blank one
License:	MIT
URL:		https://github.com/dragonfly/dragonfly/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d8/d4/4dc27b149e1c39a06d5f91e6d5aff84c285d28e8d93fcc780b2a40ea39ec/dragonfly-opt-0.1.7.tar.gz
BuildArch:	noarch


%description
Dragonfly is an open source python library for scalable Bayesian optimisation.
Bayesian optimisation is used for optimising black-box functions whose evaluations are
usually expensive. Beyond vanilla optimisation techniques, Dragonfly provides an array of tools to
scale up Bayesian optimisation to expensive large scale problems.
These include features/functionality that are especially suited for
high dimensional optimisation (optimising for a large number of variables),
parallel evaluations in synchronous or asynchronous settings (conducting multiple
evaluations in parallel), multi-fidelity optimisation (using cheap approximations
to speed up the optimisation process), and multi-objective optimisation (optimising
multiple functions simultaneously).
Dragonfly is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested
on Linux, macOS, and Windows platforms.
For documentation, installation, and a getting started guide, see our
[readthedocs page](https://dragonfly-opt.readthedocs.io). For more details, see
our [paper](https://arxiv.org/abs/1903.06694).
 
## Installation
See 
[here](https://dragonfly-opt.readthedocs.io/en/master/install/)
for detailed instructions on installing Dragonfly and its dependencies.
**Quick Installation:**
If you have done this kind of thing before, you should be able to install
Dragonfly via `pip`.
```bash
$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian
$ pip install numpy
$ pip install dragonfly-opt -v
```
**Testing the Installation**:
You can import Dragonfly in python to test if it was installed properly.
If you have installed via source, make sure that you move to a different directory 
 to avoid naming conflicts.
```bash
$ python
>>> from dragonfly import minimise_function
>>> # The first argument below is the function, the second is the domain, and the third is the budget.
>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  
>>> min_val, min_pt
(-0.32122746026750953, array([-0.7129672]))
```
Due to stochasticity in the algorithms, the above values for `min_val`, `min_pt` may be
different. If you run it for longer (e.g.
`min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)`),
you should get more consistent values for the minimum. 
If the installation fails or if there are warning messages, see detailed instructions
[here](https://dragonfly-opt.readthedocs.io/en/master/install/).
 
## Quick Start
Dragonfly can be
used directly in the command line by calling
[`dragonfly-script.py`](bin/dragonfly-script.py)
or be imported in python code via the `maximise_function` function in the main library
or in <em>ask-tell</em> mode.
To help get started, we have provided some examples in the
[`examples`](examples) directory.
See our readthedocs getting started pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))
for examples and use cases.
**Command line**:
Below is an example usage in the command line.
```bash
$ cd examples
$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt
```
**In Python code**:
The main APIs for Dragonfly are defined in
[`dragonfly/apis`](dragonfly/apis).
For their definitions and arguments, see
[`dragonfly/apis/opt.py`](dragonfly/apis/opt.py) and
[`dragonfly/apis/moo.py`](dragonfly/apis/moo.py).
You can import the main API in python code via,
```python
from dragonfly import minimise_function, maximise_function
func = lambda x: x ** 4 - x**2 + 0.1 * x
domain = [[-10, 10]]
max_capital = 100
min_val, min_pt, history = minimise_function(func, domain, max_capital)
print(min_val, min_pt)
max_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)
print(max_val, max_pt)
```
Here, `func` is the function to be maximised,
`domain` is the domain over which `func` is to be optimised,
and `max_capital` is the capital available for optimisation.
The domain can be specified via a JSON file or in code.
See
[here](examples/synthetic/branin/in_code_demo.py),
[here](examples/synthetic/hartmann6_4/in_code_demo.py),
[here](examples/synthetic/discrete_euc/in_code_demo_1.py),
[here](examples/synthetic/discrete_euc/in_code_demo_2.py),
[here](examples/synthetic/hartmann3_constrained/in_code_demo.py),
[here](examples/synthetic/park1_constrained/in_code_demo.py),
[here](examples/synthetic/borehole_constrained/in_code_demo.py),
[here](examples/synthetic/multiobjective_branin_currinexp/in_code_demo.py),
[here](examples/synthetic/multiobjective_hartmann/in_code_demo.py),
[here](examples/tree_reg/in_code_demo.py),
and
[here](examples/nas/demo_nas.py)
for more detailed examples.
**In Ask-Tell Mode**:
Ask-tell mode provides you more control over your experiments where you can supply past results
to our API in order to obtain a recommendation.
See the [following example](examples/detailed_use_cases/in_code_demo_ask_tell.py) for more details.
For a comprehensive list of uses cases, including multi-objective optimisation,
multi-fidelity optimisation, neural architecture search, and other optimisation
methods (besides Bayesian optimisation), see our readthe docs pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))).
&nbsp;
### Contributors
Kirthevasan Kandasamy: [github](https://github.com/kirthevasank),
[webpage](http://www.cs.cmu.edu/~kkandasa/)  
Karun Raju Vysyaraju: [github](https://github.com/karunraju),
[linkedin](https://www.linkedin.com/in/karunrajuvysyaraju)  
Anthony Yu: [github](https://github.com/anthonyhsyu),
[linkedin](https://www.linkedin.com/in/anthony-yu-5239a877/)  
Willie Neiswanger: [github](https://github.com/willieneis),
[webpage](http://www.cs.cmu.edu/~wdn/)  
Biswajit Paria: [github](https://github.com/biswajitsc),
[webpage](https://biswajitsc.github.io/)  
Chris Collins: [github](https://github.com/crcollins/),
[webpage](https://www.crcollins.com/)  
### Acknowledgements
Research and development of the methods in this package were funded by
DOE grant DESC0011114, NSF grant IIS1563887, the DARPA D3M program, and AFRL.
### Citation
If you use any part of this code in your work, please cite our
[JMLR paper](http://jmlr.org/papers/v21/18-223.html).
```
@article{JMLR:v21:18-223,
  author  = {Kirthevasan Kandasamy and Karun Raju Vysyaraju and Willie Neiswanger and Biswajit Paria and Christopher R. Collins and Jeff Schneider and Barnabas Poczos and Eric P. Xing},
  title   = {Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {81},
  pages   = {1-27},
  url     = {http://jmlr.org/papers/v21/18-223.html}
}
```
### License
This software is released under the MIT license. For more details, please refer
[LICENSE.txt](https://github.com/dragonfly/dragonfly/blob/master/LICENSE.txt).
For questions, please email kandasamy@cs.cmu.edu.
"Copyright 2018-2019 Kirthevasan Kandasamy"

%package -n python3-dragonfly-opt
Summary:	please add a summary manually as the author left a blank one
Provides:	python-dragonfly-opt
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-dragonfly-opt
Dragonfly is an open source python library for scalable Bayesian optimisation.
Bayesian optimisation is used for optimising black-box functions whose evaluations are
usually expensive. Beyond vanilla optimisation techniques, Dragonfly provides an array of tools to
scale up Bayesian optimisation to expensive large scale problems.
These include features/functionality that are especially suited for
high dimensional optimisation (optimising for a large number of variables),
parallel evaluations in synchronous or asynchronous settings (conducting multiple
evaluations in parallel), multi-fidelity optimisation (using cheap approximations
to speed up the optimisation process), and multi-objective optimisation (optimising
multiple functions simultaneously).
Dragonfly is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested
on Linux, macOS, and Windows platforms.
For documentation, installation, and a getting started guide, see our
[readthedocs page](https://dragonfly-opt.readthedocs.io). For more details, see
our [paper](https://arxiv.org/abs/1903.06694).
&nbsp;
## Installation
See 
[here](https://dragonfly-opt.readthedocs.io/en/master/install/)
for detailed instructions on installing Dragonfly and its dependencies.
**Quick Installation:**
If you have done this kind of thing before, you should be able to install
Dragonfly via `pip`.
```bash
$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian
$ pip install numpy
$ pip install dragonfly-opt -v
```
**Testing the Installation**:
You can import Dragonfly in python to test if it was installed properly.
If you have installed via source, make sure that you move to a different directory 
 to avoid naming conflicts.
```bash
$ python
>>> from dragonfly import minimise_function
>>> # The first argument below is the function, the second is the domain, and the third is the budget.
>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  
>>> min_val, min_pt
(-0.32122746026750953, array([-0.7129672]))
```
Due to stochasticity in the algorithms, the above values for `min_val`, `min_pt` may be
different. If you run it for longer (e.g.
`min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)`),
you should get more consistent values for the minimum. 
If the installation fails or if there are warning messages, see detailed instructions
[here](https://dragonfly-opt.readthedocs.io/en/master/install/).
&nbsp;
## Quick Start
Dragonfly can be
used directly in the command line by calling
[`dragonfly-script.py`](bin/dragonfly-script.py)
or be imported in python code via the `maximise_function` function in the main library
or in <em>ask-tell</em> mode.
To help get started, we have provided some examples in the
[`examples`](examples) directory.
See our readthedocs getting started pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))
for examples and use cases.
**Command line**:
Below is an example usage in the command line.
```bash
$ cd examples
$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt
```
**In Python code**:
The main APIs for Dragonfly are defined in
[`dragonfly/apis`](dragonfly/apis).
For their definitions and arguments, see
[`dragonfly/apis/opt.py`](dragonfly/apis/opt.py) and
[`dragonfly/apis/moo.py`](dragonfly/apis/moo.py).
You can import the main API in python code via,
```python
from dragonfly import minimise_function, maximise_function
func = lambda x: x ** 4 - x**2 + 0.1 * x
domain = [[-10, 10]]
max_capital = 100
min_val, min_pt, history = minimise_function(func, domain, max_capital)
print(min_val, min_pt)
max_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)
print(max_val, max_pt)
```
Here, `func` is the function to be maximised,
`domain` is the domain over which `func` is to be optimised,
and `max_capital` is the capital available for optimisation.
The domain can be specified via a JSON file or in code.
See
[here](examples/synthetic/branin/in_code_demo.py),
[here](examples/synthetic/hartmann6_4/in_code_demo.py),
[here](examples/synthetic/discrete_euc/in_code_demo_1.py),
[here](examples/synthetic/discrete_euc/in_code_demo_2.py),
[here](examples/synthetic/hartmann3_constrained/in_code_demo.py),
[here](examples/synthetic/park1_constrained/in_code_demo.py),
[here](examples/synthetic/borehole_constrained/in_code_demo.py),
[here](examples/synthetic/multiobjective_branin_currinexp/in_code_demo.py),
[here](examples/synthetic/multiobjective_hartmann/in_code_demo.py),
[here](examples/tree_reg/in_code_demo.py),
and
[here](examples/nas/demo_nas.py)
for more detailed examples.
**In Ask-Tell Mode**:
Ask-tell mode provides you more control over your experiments where you can supply past results
to our API in order to obtain a recommendation.
See the [following example](examples/detailed_use_cases/in_code_demo_ask_tell.py) for more details.
For a comprehensive list of uses cases, including multi-objective optimisation,
multi-fidelity optimisation, neural architecture search, and other optimisation
methods (besides Bayesian optimisation), see our readthe docs pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))).
&nbsp;
### Contributors
Kirthevasan Kandasamy: [github](https://github.com/kirthevasank),
[webpage](http://www.cs.cmu.edu/~kkandasa/)  
Karun Raju Vysyaraju: [github](https://github.com/karunraju),
[linkedin](https://www.linkedin.com/in/karunrajuvysyaraju)  
Anthony Yu: [github](https://github.com/anthonyhsyu),
[linkedin](https://www.linkedin.com/in/anthony-yu-5239a877/)  
Willie Neiswanger: [github](https://github.com/willieneis),
[webpage](http://www.cs.cmu.edu/~wdn/)  
Biswajit Paria: [github](https://github.com/biswajitsc),
[webpage](https://biswajitsc.github.io/)  
Chris Collins: [github](https://github.com/crcollins/),
[webpage](https://www.crcollins.com/)  
### Acknowledgements
Research and development of the methods in this package were funded by
DOE grant DESC0011114, NSF grant IIS1563887, the DARPA D3M program, and AFRL.
### Citation
If you use any part of this code in your work, please cite our
[JMLR paper](http://jmlr.org/papers/v21/18-223.html).
```
@article{JMLR:v21:18-223,
  author  = {Kirthevasan Kandasamy and Karun Raju Vysyaraju and Willie Neiswanger and Biswajit Paria and Christopher R. Collins and Jeff Schneider and Barnabas Poczos and Eric P. Xing},
  title   = {Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {81},
  pages   = {1-27},
  url     = {http://jmlr.org/papers/v21/18-223.html}
}
```
### License
This software is released under the MIT license. For more details, please refer
[LICENSE.txt](https://github.com/dragonfly/dragonfly/blob/master/LICENSE.txt).
For questions, please email kandasamy@cs.cmu.edu.
"Copyright 2018-2019 Kirthevasan Kandasamy"

%package help
Summary:	Development documents and examples for dragonfly-opt
Provides:	python3-dragonfly-opt-doc
%description help
Dragonfly is an open source python library for scalable Bayesian optimisation.
Bayesian optimisation is used for optimising black-box functions whose evaluations are
usually expensive. Beyond vanilla optimisation techniques, Dragonfly provides an array of tools to
scale up Bayesian optimisation to expensive large scale problems.
These include features/functionality that are especially suited for
high dimensional optimisation (optimising for a large number of variables),
parallel evaluations in synchronous or asynchronous settings (conducting multiple
evaluations in parallel), multi-fidelity optimisation (using cheap approximations
to speed up the optimisation process), and multi-objective optimisation (optimising
multiple functions simultaneously).
Dragonfly is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested
on Linux, macOS, and Windows platforms.
For documentation, installation, and a getting started guide, see our
[readthedocs page](https://dragonfly-opt.readthedocs.io). For more details, see
our [paper](https://arxiv.org/abs/1903.06694).
&nbsp;
## Installation
See 
[here](https://dragonfly-opt.readthedocs.io/en/master/install/)
for detailed instructions on installing Dragonfly and its dependencies.
**Quick Installation:**
If you have done this kind of thing before, you should be able to install
Dragonfly via `pip`.
```bash
$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian
$ pip install numpy
$ pip install dragonfly-opt -v
```
**Testing the Installation**:
You can import Dragonfly in python to test if it was installed properly.
If you have installed via source, make sure that you move to a different directory 
 to avoid naming conflicts.
```bash
$ python
>>> from dragonfly import minimise_function
>>> # The first argument below is the function, the second is the domain, and the third is the budget.
>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  
>>> min_val, min_pt
(-0.32122746026750953, array([-0.7129672]))
```
Due to stochasticity in the algorithms, the above values for `min_val`, `min_pt` may be
different. If you run it for longer (e.g.
`min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)`),
you should get more consistent values for the minimum. 
If the installation fails or if there are warning messages, see detailed instructions
[here](https://dragonfly-opt.readthedocs.io/en/master/install/).
&nbsp;
## Quick Start
Dragonfly can be
used directly in the command line by calling
[`dragonfly-script.py`](bin/dragonfly-script.py)
or be imported in python code via the `maximise_function` function in the main library
or in <em>ask-tell</em> mode.
To help get started, we have provided some examples in the
[`examples`](examples) directory.
See our readthedocs getting started pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))
for examples and use cases.
**Command line**:
Below is an example usage in the command line.
```bash
$ cd examples
$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt
```
**In Python code**:
The main APIs for Dragonfly are defined in
[`dragonfly/apis`](dragonfly/apis).
For their definitions and arguments, see
[`dragonfly/apis/opt.py`](dragonfly/apis/opt.py) and
[`dragonfly/apis/moo.py`](dragonfly/apis/moo.py).
You can import the main API in python code via,
```python
from dragonfly import minimise_function, maximise_function
func = lambda x: x ** 4 - x**2 + 0.1 * x
domain = [[-10, 10]]
max_capital = 100
min_val, min_pt, history = minimise_function(func, domain, max_capital)
print(min_val, min_pt)
max_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)
print(max_val, max_pt)
```
Here, `func` is the function to be maximised,
`domain` is the domain over which `func` is to be optimised,
and `max_capital` is the capital available for optimisation.
The domain can be specified via a JSON file or in code.
See
[here](examples/synthetic/branin/in_code_demo.py),
[here](examples/synthetic/hartmann6_4/in_code_demo.py),
[here](examples/synthetic/discrete_euc/in_code_demo_1.py),
[here](examples/synthetic/discrete_euc/in_code_demo_2.py),
[here](examples/synthetic/hartmann3_constrained/in_code_demo.py),
[here](examples/synthetic/park1_constrained/in_code_demo.py),
[here](examples/synthetic/borehole_constrained/in_code_demo.py),
[here](examples/synthetic/multiobjective_branin_currinexp/in_code_demo.py),
[here](examples/synthetic/multiobjective_hartmann/in_code_demo.py),
[here](examples/tree_reg/in_code_demo.py),
and
[here](examples/nas/demo_nas.py)
for more detailed examples.
**In Ask-Tell Mode**:
Ask-tell mode provides you more control over your experiments where you can supply past results
to our API in order to obtain a recommendation.
See the [following example](examples/detailed_use_cases/in_code_demo_ask_tell.py) for more details.
For a comprehensive list of uses cases, including multi-objective optimisation,
multi-fidelity optimisation, neural architecture search, and other optimisation
methods (besides Bayesian optimisation), see our readthe docs pages
([command line](https://dragonfly-opt.readthedocs.io/en/master/getting_started_cli/),
[Python](https://dragonfly-opt.readthedocs.io/en/master/getting_started_py/),
[Ask-Tell](https://dragonfly-opt.readthedocs.io/en/master/getting_started_ask_tell/))).
&nbsp;
### Contributors
Kirthevasan Kandasamy: [github](https://github.com/kirthevasank),
[webpage](http://www.cs.cmu.edu/~kkandasa/)  
Karun Raju Vysyaraju: [github](https://github.com/karunraju),
[linkedin](https://www.linkedin.com/in/karunrajuvysyaraju)  
Anthony Yu: [github](https://github.com/anthonyhsyu),
[linkedin](https://www.linkedin.com/in/anthony-yu-5239a877/)  
Willie Neiswanger: [github](https://github.com/willieneis),
[webpage](http://www.cs.cmu.edu/~wdn/)  
Biswajit Paria: [github](https://github.com/biswajitsc),
[webpage](https://biswajitsc.github.io/)  
Chris Collins: [github](https://github.com/crcollins/),
[webpage](https://www.crcollins.com/)  
### Acknowledgements
Research and development of the methods in this package were funded by
DOE grant DESC0011114, NSF grant IIS1563887, the DARPA D3M program, and AFRL.
### Citation
If you use any part of this code in your work, please cite our
[JMLR paper](http://jmlr.org/papers/v21/18-223.html).
```
@article{JMLR:v21:18-223,
  author  = {Kirthevasan Kandasamy and Karun Raju Vysyaraju and Willie Neiswanger and Biswajit Paria and Christopher R. Collins and Jeff Schneider and Barnabas Poczos and Eric P. Xing},
  title   = {Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {81},
  pages   = {1-27},
  url     = {http://jmlr.org/papers/v21/18-223.html}
}
```
### License
This software is released under the MIT license. For more details, please refer
[LICENSE.txt](https://github.com/dragonfly/dragonfly/blob/master/LICENSE.txt).
For questions, please email kandasamy@cs.cmu.edu.
"Copyright 2018-2019 Kirthevasan Kandasamy"

%prep
%autosetup -n dragonfly-opt-0.1.7

%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-dragonfly-opt -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.7-1
- Package Spec generated