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@@ -0,0 +1 @@ +/ax-platform-0.3.1.tar.gz diff --git a/python-ax-platform.spec b/python-ax-platform.spec new file mode 100644 index 0000000..442092c --- /dev/null +++ b/python-ax-platform.spec @@ -0,0 +1,699 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ax-platform +Version: 0.3.1 +Release: 1 +Summary: Adaptive Experimentation +License: MIT +URL: https://github.com/facebook/Ax +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ff/dd/dea40f9e710c1c588ff9cc804c8f12c8497d064149c29eca3f4e2983f236/ax-platform-0.3.1.tar.gz +BuildArch: noarch + +Requires: python3-botorch +Requires: python3-jinja2 +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-ipywidgets +Requires: python3-plotly +Requires: python3-typeguard +Requires: python3-beautifulsoup4 +Requires: python3-black +Requires: python3-flake8 +Requires: python3-hypothesis +Requires: python3-Jinja2 +Requires: python3-pyfakefs +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-sphinx +Requires: python3-sphinx-autodoc-typehints +Requires: python3-torchvision +Requires: python3-nbconvert +Requires: python3-jupyter-client +Requires: python3-yappi +Requires: python3-SQLAlchemy +Requires: python3-jupyter +Requires: python3-beautifulsoup4 +Requires: python3-black +Requires: python3-flake8 +Requires: python3-hypothesis +Requires: python3-Jinja2 +Requires: python3-pyfakefs +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-sphinx +Requires: python3-sphinx-autodoc-typehints +Requires: python3-torchvision +Requires: python3-nbconvert +Requires: python3-jupyter-client +Requires: python3-yappi +Requires: python3-SQLAlchemy +Requires: python3-jupyter +Requires: python3-tensorboard +Requires: python3-torchvision +Requires: python3-torchx +Requires: python3-psycopg2 +Requires: python3-ray +Requires: python3-tabulate +Requires: python3-tensorboardX +Requires: python3-matplotlib +Requires: python3-pyro-ppl +Requires: python3-pytorch-lightning +Requires: python3-beautifulsoup4 +Requires: python3-black +Requires: python3-flake8 +Requires: python3-hypothesis +Requires: python3-Jinja2 +Requires: python3-pyfakefs +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-sphinx +Requires: python3-sphinx-autodoc-typehints +Requires: python3-torchvision +Requires: python3-nbconvert +Requires: python3-jupyter-client +Requires: python3-yappi +Requires: python3-SQLAlchemy +Requires: python3-jupyter +Requires: python3-tensorboard +Requires: python3-torchvision +Requires: python3-torchx +Requires: python3-tensorboard +Requires: python3-torchvision +Requires: python3-torchx + +%description +<img width="300" src="https://ax.dev/img/ax_logo_lockup.svg" alt="Ax Logo" /> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://github.com/facebook/Ax/actions?query=workflow%3A%22Build+and+Test+Workflow%22) +[](https://codecov.io/gh/facebook/Ax) +[](LICENSE) + +Ax is an accessible, general-purpose platform for understanding, managing, +deploying, and automating adaptive experiments. + +Adaptive experimentation is the machine-learning guided process of iteratively +exploring a (possibly infinite) parameter space in order to identify optimal +configurations in a resource-efficient manner. Ax currently supports Bayesian +optimization and bandit optimization as exploration strategies. Bayesian +optimization in Ax is powered by [BoTorch](https://github.com/facebookexternal/botorch), +a modern library for Bayesian optimization research built on PyTorch. + +For full documentation and tutorials, see the [Ax website](https://ax.dev) + +## Why Ax? + +* **Versatility**: Ax supports different kinds of experiments, from dynamic ML-assisted A/B testing, to hyperparameter optimization in machine learning. +* **Customization**: Ax makes it easy to add new modeling and decision algorithms, enabling research and development with minimal overhead. +* **Production-completeness**: Ax comes with storage integration and ability to fully save and reload experiments. +* **Support for multi-modal and constrained experimentation**: Ax allows for running and combining multiple experiments (e.g. simulation with a real-world "online" A/B test) and for constrained optimization (e.g. improving classification accuracy without significant increase in resource-utilization). +* **Efficiency in high-noise setting**: Ax offers state-of-the-art algorithms specifically geared to noisy experiments, such as simulations with reinforcement-learning agents. +* **Ease of use**: Ax includes 3 different APIs that strike different balances between lightweight structure and flexibility. Using the most concise Loop API, a whole optimization can be done in just one function call. The Service API integrates easily with external schedulers. The most elaborate Developer API affords full algorithm customization and experiment introspection. + +## Getting Started + +To run a simple optimization loop in Ax (using the +[Booth response surface](https://www.sfu.ca/~ssurjano/booth.html) as the +artificial evaluation function): + +```python +>>> from ax import optimize +>>> best_parameters, best_values, experiment, model = optimize( + parameters=[ + { + "name": "x1", + "type": "range", + "bounds": [-10.0, 10.0], + }, + { + "name": "x2", + "type": "range", + "bounds": [-10.0, 10.0], + }, + ], + # Booth function + evaluation_function=lambda p: (p["x1"] + 2*p["x2"] - 7)**2 + (2*p["x1"] + p["x2"] - 5)**2, + minimize=True, + ) + +# best_parameters contains {'x1': 1.02, 'x2': 2.97}; the global min is (1, 3) +``` + +## Installation + +### Requirements +You need Python 3.8 or later to run Ax. + +The required Python dependencies are: + +* [botorch](https://www.botorch.org) +* jinja2 +* pandas +* scipy +* sklearn +* plotly >=2.2.1 + +### Stable Version + +#### Installing via pip +We recommend installing Ax via pip (even if using Conda environment): + +``` +conda install pytorch torchvision -c pytorch # OSX only (details below) +pip install ax-platform +``` + +Installation will use Python wheels from PyPI, available for [OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files). + +*Note*: Make sure the `pip` being used to install `ax-platform` is actually the one from the newly created Conda environment. +If you're using a Unix-based OS, you can use `which pip` to check. + +*Recommendation for MacOS users*: PyTorch is a required dependency of BoTorch, and can be automatically installed via pip. +However, **we recommend you [install PyTorch manually](https://pytorch.org/get-started/locally/#anaconda-1) before installing Ax, using the Anaconda package manager**. +Installing from Anaconda will link against MKL (a library that optimizes mathematical computation for Intel processors). +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, installing PyTorch from pip does not link against MKL. + +If you need CUDA on MacOS, you will need to build PyTorch from source. Please consult the PyTorch installation instructions above. + +#### Optional Dependencies + +To use Ax with a notebook environment, you will need Jupyter. Install it first: +``` +pip install jupyter +``` + +If you want to store the experiments in MySQL, you will need SQLAlchemy: +``` +pip install SQLAlchemy +``` + +### Latest Version + +#### Installing from Git + +You can install the latest (bleeding edge) version from Git. + +First, see recommendation for installing PyTorch for MacOS users above. + +At times, the bleeding edge for Ax can depend on bleeding edge versions of BoTorch (or GPyTorch). We therefore recommend installing those from Git as well: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform +``` + +#### Optional Dependencies + +If using Ax in Jupyter notebooks: + +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[notebook] +``` + +To support plotly-based plotting in newer Jupyter notebook versions + +``` +pip install "notebook>=5.3" "ipywidgets==7.5" +``` + +[See Plotly repo's README](https://github.com/plotly/plotly.py#jupyter-notebook-support) for details and JupyterLab instructions. + +If storing Ax experiments via SQLAlchemy in MySQL or SQLite: +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[mysql] +``` + +## Join the Ax Community + +### Getting help + +Please open an issue on our [issues page](https://github.com/facebook/Ax/issues) with any questions, feature requests or bug reports! If posting a bug report, please include a minimal reproducible example (as a code snippet) that we can use to reproduce and debug the problem you encountered. + +### Contributing + +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + +When contributing to Ax, we recommend cloning the [repository](https://github.com/facebook/Ax) and installing all optional dependencies: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +git clone https://github.com/facebook/ax.git --depth 1 +cd ax +pip install -e .[notebook,mysql,dev] +``` + +See recommendation for installing PyTorch for MacOS users above. + +The above example limits the cloned directory size via the +[`--depth`](https://git-scm.com/docs/git-clone#Documentation/git-clone.txt---depthltdepthgt) +argument to `git clone`. If you require the entire commit history you may remove this +argument. + +## License + +Ax is licensed under the [MIT license](./LICENSE). + + + + +%package -n python3-ax-platform +Summary: Adaptive Experimentation +Provides: python-ax-platform +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ax-platform +<img width="300" src="https://ax.dev/img/ax_logo_lockup.svg" alt="Ax Logo" /> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://github.com/facebook/Ax/actions?query=workflow%3A%22Build+and+Test+Workflow%22) +[](https://codecov.io/gh/facebook/Ax) +[](LICENSE) + +Ax is an accessible, general-purpose platform for understanding, managing, +deploying, and automating adaptive experiments. + +Adaptive experimentation is the machine-learning guided process of iteratively +exploring a (possibly infinite) parameter space in order to identify optimal +configurations in a resource-efficient manner. Ax currently supports Bayesian +optimization and bandit optimization as exploration strategies. Bayesian +optimization in Ax is powered by [BoTorch](https://github.com/facebookexternal/botorch), +a modern library for Bayesian optimization research built on PyTorch. + +For full documentation and tutorials, see the [Ax website](https://ax.dev) + +## Why Ax? + +* **Versatility**: Ax supports different kinds of experiments, from dynamic ML-assisted A/B testing, to hyperparameter optimization in machine learning. +* **Customization**: Ax makes it easy to add new modeling and decision algorithms, enabling research and development with minimal overhead. +* **Production-completeness**: Ax comes with storage integration and ability to fully save and reload experiments. +* **Support for multi-modal and constrained experimentation**: Ax allows for running and combining multiple experiments (e.g. simulation with a real-world "online" A/B test) and for constrained optimization (e.g. improving classification accuracy without significant increase in resource-utilization). +* **Efficiency in high-noise setting**: Ax offers state-of-the-art algorithms specifically geared to noisy experiments, such as simulations with reinforcement-learning agents. +* **Ease of use**: Ax includes 3 different APIs that strike different balances between lightweight structure and flexibility. Using the most concise Loop API, a whole optimization can be done in just one function call. The Service API integrates easily with external schedulers. The most elaborate Developer API affords full algorithm customization and experiment introspection. + +## Getting Started + +To run a simple optimization loop in Ax (using the +[Booth response surface](https://www.sfu.ca/~ssurjano/booth.html) as the +artificial evaluation function): + +```python +>>> from ax import optimize +>>> best_parameters, best_values, experiment, model = optimize( + parameters=[ + { + "name": "x1", + "type": "range", + "bounds": [-10.0, 10.0], + }, + { + "name": "x2", + "type": "range", + "bounds": [-10.0, 10.0], + }, + ], + # Booth function + evaluation_function=lambda p: (p["x1"] + 2*p["x2"] - 7)**2 + (2*p["x1"] + p["x2"] - 5)**2, + minimize=True, + ) + +# best_parameters contains {'x1': 1.02, 'x2': 2.97}; the global min is (1, 3) +``` + +## Installation + +### Requirements +You need Python 3.8 or later to run Ax. + +The required Python dependencies are: + +* [botorch](https://www.botorch.org) +* jinja2 +* pandas +* scipy +* sklearn +* plotly >=2.2.1 + +### Stable Version + +#### Installing via pip +We recommend installing Ax via pip (even if using Conda environment): + +``` +conda install pytorch torchvision -c pytorch # OSX only (details below) +pip install ax-platform +``` + +Installation will use Python wheels from PyPI, available for [OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files). + +*Note*: Make sure the `pip` being used to install `ax-platform` is actually the one from the newly created Conda environment. +If you're using a Unix-based OS, you can use `which pip` to check. + +*Recommendation for MacOS users*: PyTorch is a required dependency of BoTorch, and can be automatically installed via pip. +However, **we recommend you [install PyTorch manually](https://pytorch.org/get-started/locally/#anaconda-1) before installing Ax, using the Anaconda package manager**. +Installing from Anaconda will link against MKL (a library that optimizes mathematical computation for Intel processors). +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, installing PyTorch from pip does not link against MKL. + +If you need CUDA on MacOS, you will need to build PyTorch from source. Please consult the PyTorch installation instructions above. + +#### Optional Dependencies + +To use Ax with a notebook environment, you will need Jupyter. Install it first: +``` +pip install jupyter +``` + +If you want to store the experiments in MySQL, you will need SQLAlchemy: +``` +pip install SQLAlchemy +``` + +### Latest Version + +#### Installing from Git + +You can install the latest (bleeding edge) version from Git. + +First, see recommendation for installing PyTorch for MacOS users above. + +At times, the bleeding edge for Ax can depend on bleeding edge versions of BoTorch (or GPyTorch). We therefore recommend installing those from Git as well: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform +``` + +#### Optional Dependencies + +If using Ax in Jupyter notebooks: + +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[notebook] +``` + +To support plotly-based plotting in newer Jupyter notebook versions + +``` +pip install "notebook>=5.3" "ipywidgets==7.5" +``` + +[See Plotly repo's README](https://github.com/plotly/plotly.py#jupyter-notebook-support) for details and JupyterLab instructions. + +If storing Ax experiments via SQLAlchemy in MySQL or SQLite: +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[mysql] +``` + +## Join the Ax Community + +### Getting help + +Please open an issue on our [issues page](https://github.com/facebook/Ax/issues) with any questions, feature requests or bug reports! If posting a bug report, please include a minimal reproducible example (as a code snippet) that we can use to reproduce and debug the problem you encountered. + +### Contributing + +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + +When contributing to Ax, we recommend cloning the [repository](https://github.com/facebook/Ax) and installing all optional dependencies: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +git clone https://github.com/facebook/ax.git --depth 1 +cd ax +pip install -e .[notebook,mysql,dev] +``` + +See recommendation for installing PyTorch for MacOS users above. + +The above example limits the cloned directory size via the +[`--depth`](https://git-scm.com/docs/git-clone#Documentation/git-clone.txt---depthltdepthgt) +argument to `git clone`. If you require the entire commit history you may remove this +argument. + +## License + +Ax is licensed under the [MIT license](./LICENSE). + + + + +%package help +Summary: Development documents and examples for ax-platform +Provides: python3-ax-platform-doc +%description help +<img width="300" src="https://ax.dev/img/ax_logo_lockup.svg" alt="Ax Logo" /> + +<hr/> + +[](https://opensource.fb.com/support-ukraine) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://pypi.org/project/ax-platform/) +[](https://github.com/facebook/Ax/actions?query=workflow%3A%22Build+and+Test+Workflow%22) +[](https://codecov.io/gh/facebook/Ax) +[](LICENSE) + +Ax is an accessible, general-purpose platform for understanding, managing, +deploying, and automating adaptive experiments. + +Adaptive experimentation is the machine-learning guided process of iteratively +exploring a (possibly infinite) parameter space in order to identify optimal +configurations in a resource-efficient manner. Ax currently supports Bayesian +optimization and bandit optimization as exploration strategies. Bayesian +optimization in Ax is powered by [BoTorch](https://github.com/facebookexternal/botorch), +a modern library for Bayesian optimization research built on PyTorch. + +For full documentation and tutorials, see the [Ax website](https://ax.dev) + +## Why Ax? + +* **Versatility**: Ax supports different kinds of experiments, from dynamic ML-assisted A/B testing, to hyperparameter optimization in machine learning. +* **Customization**: Ax makes it easy to add new modeling and decision algorithms, enabling research and development with minimal overhead. +* **Production-completeness**: Ax comes with storage integration and ability to fully save and reload experiments. +* **Support for multi-modal and constrained experimentation**: Ax allows for running and combining multiple experiments (e.g. simulation with a real-world "online" A/B test) and for constrained optimization (e.g. improving classification accuracy without significant increase in resource-utilization). +* **Efficiency in high-noise setting**: Ax offers state-of-the-art algorithms specifically geared to noisy experiments, such as simulations with reinforcement-learning agents. +* **Ease of use**: Ax includes 3 different APIs that strike different balances between lightweight structure and flexibility. Using the most concise Loop API, a whole optimization can be done in just one function call. The Service API integrates easily with external schedulers. The most elaborate Developer API affords full algorithm customization and experiment introspection. + +## Getting Started + +To run a simple optimization loop in Ax (using the +[Booth response surface](https://www.sfu.ca/~ssurjano/booth.html) as the +artificial evaluation function): + +```python +>>> from ax import optimize +>>> best_parameters, best_values, experiment, model = optimize( + parameters=[ + { + "name": "x1", + "type": "range", + "bounds": [-10.0, 10.0], + }, + { + "name": "x2", + "type": "range", + "bounds": [-10.0, 10.0], + }, + ], + # Booth function + evaluation_function=lambda p: (p["x1"] + 2*p["x2"] - 7)**2 + (2*p["x1"] + p["x2"] - 5)**2, + minimize=True, + ) + +# best_parameters contains {'x1': 1.02, 'x2': 2.97}; the global min is (1, 3) +``` + +## Installation + +### Requirements +You need Python 3.8 or later to run Ax. + +The required Python dependencies are: + +* [botorch](https://www.botorch.org) +* jinja2 +* pandas +* scipy +* sklearn +* plotly >=2.2.1 + +### Stable Version + +#### Installing via pip +We recommend installing Ax via pip (even if using Conda environment): + +``` +conda install pytorch torchvision -c pytorch # OSX only (details below) +pip install ax-platform +``` + +Installation will use Python wheels from PyPI, available for [OSX, Linux, and Windows](https://pypi.org/project/ax-platform/#files). + +*Note*: Make sure the `pip` being used to install `ax-platform` is actually the one from the newly created Conda environment. +If you're using a Unix-based OS, you can use `which pip` to check. + +*Recommendation for MacOS users*: PyTorch is a required dependency of BoTorch, and can be automatically installed via pip. +However, **we recommend you [install PyTorch manually](https://pytorch.org/get-started/locally/#anaconda-1) before installing Ax, using the Anaconda package manager**. +Installing from Anaconda will link against MKL (a library that optimizes mathematical computation for Intel processors). +This will result in up to an order-of-magnitude speed-up for Bayesian optimization, as at the moment, installing PyTorch from pip does not link against MKL. + +If you need CUDA on MacOS, you will need to build PyTorch from source. Please consult the PyTorch installation instructions above. + +#### Optional Dependencies + +To use Ax with a notebook environment, you will need Jupyter. Install it first: +``` +pip install jupyter +``` + +If you want to store the experiments in MySQL, you will need SQLAlchemy: +``` +pip install SQLAlchemy +``` + +### Latest Version + +#### Installing from Git + +You can install the latest (bleeding edge) version from Git. + +First, see recommendation for installing PyTorch for MacOS users above. + +At times, the bleeding edge for Ax can depend on bleeding edge versions of BoTorch (or GPyTorch). We therefore recommend installing those from Git as well: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform +``` + +#### Optional Dependencies + +If using Ax in Jupyter notebooks: + +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[notebook] +``` + +To support plotly-based plotting in newer Jupyter notebook versions + +``` +pip install "notebook>=5.3" "ipywidgets==7.5" +``` + +[See Plotly repo's README](https://github.com/plotly/plotly.py#jupyter-notebook-support) for details and JupyterLab instructions. + +If storing Ax experiments via SQLAlchemy in MySQL or SQLite: +``` +pip install git+https://github.com/facebook/Ax.git#egg=ax-platform[mysql] +``` + +## Join the Ax Community + +### Getting help + +Please open an issue on our [issues page](https://github.com/facebook/Ax/issues) with any questions, feature requests or bug reports! If posting a bug report, please include a minimal reproducible example (as a code snippet) that we can use to reproduce and debug the problem you encountered. + +### Contributing + +See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. + +When contributing to Ax, we recommend cloning the [repository](https://github.com/facebook/Ax) and installing all optional dependencies: + +``` +pip install git+https://github.com/cornellius-gp/linear_operator.git +pip install git+https://github.com/cornellius-gp/gpytorch.git +export ALLOW_LATEST_GPYTORCH_LINOP=true +pip install git+https://github.com/pytorch/botorch.git +export ALLOW_BOTORCH_LATEST=true +git clone https://github.com/facebook/ax.git --depth 1 +cd ax +pip install -e .[notebook,mysql,dev] +``` + +See recommendation for installing PyTorch for MacOS users above. + +The above example limits the cloned directory size via the +[`--depth`](https://git-scm.com/docs/git-clone#Documentation/git-clone.txt---depthltdepthgt) +argument to `git clone`. If you require the entire commit history you may remove this +argument. + +## License + +Ax is licensed under the [MIT license](./LICENSE). + + + + +%prep +%autosetup -n ax-platform-0.3.1 + +%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-ax-platform -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.1-1 +- Package Spec generated @@ -0,0 +1 @@ +5c77534503acbf05f1451c3c8c259fad ax-platform-0.3.1.tar.gz |