From 96181315c12f3636e9178e461926316765696e78 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Thu, 18 May 2023 03:03:42 +0000 Subject: automatic import of python-confidential-ml-utils --- .gitignore | 1 + python-confidential-ml-utils.spec | 267 ++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 269 insertions(+) create mode 100644 python-confidential-ml-utils.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..2c1bdf7 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/confidential-ml-utils-0.9.1.tar.gz diff --git a/python-confidential-ml-utils.spec b/python-confidential-ml-utils.spec new file mode 100644 index 0000000..a5577df --- /dev/null +++ b/python-confidential-ml-utils.spec @@ -0,0 +1,267 @@ +%global _empty_manifest_terminate_build 0 +Name: python-confidential-ml-utils +Version: 0.9.1 +Release: 1 +Summary: Utilities for confidential machine learning +License: MIT +URL: https://github.com/Azure/confidential-ml-utils +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/62/30/66e35c79036a1304ee7b888e66649b4015ab9cf05e06227ad5d4674def8b/confidential-ml-utils-0.9.1.tar.gz +BuildArch: noarch + + +%description +# Confidential ML Utilities + +[![python](https://github.com/Azure/confidential-ml-utils/workflows/python/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3Apython) +[![codecov](https://codecov.io/gh/Azure/confidential-ml-utils/branch/main/graph/badge.svg?token=TEWT51C5FK)](https://codecov.io/gh/Azure/confidential-ml-utils) +[![CodeQL](https://github.com/Azure/confidential-ml-utils/workflows/CodeQL/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3ACodeQL) +[![Component Governance](https://dev.azure.com/msdata/Vienna/_apis/build/status/aml-ds/Azure.confidential-ml-utils%20Component%20Governance?branchName=main)](https://dev.azure.com/msdata/Vienna/_build/latest?definitionId=13909&branchName=main) +[![PyPI version](https://badge.fury.io/py/confidential-ml-utils.svg)](https://badge.fury.io/py/confidential-ml-utils) +[![Python versions](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/confidential-ml-utils)](https://pypi.org/project/confidential-ml-utils/) +[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![license: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](LICENSE) + +Confidential ML is the practice of training machine learning models without +seeing the training data. It is needed in many enterprises to satisfy the +strict compliance and privacy guarantees they provide to their customers. This +repository contains a set of utilities for confidential ML, with a special +emphasis on using PyTorch in +[Azure Machine Learning pipelines](https://github.com/Azure/azureml-examples). + +## ⚠️Deprecation +**This package has been deprecated as of May 2021.** Please install `pip install shrike` and use `shrike.compliant_logging` instead. More details: https://github.com/Azure/shrike + +## Using + +For more detailed examples and API reference, see the +[docs page](https://azure.github.io/confidential-ml-utils/logging/). + +Minimal use case: + +```python +from confidential_ml_utils import DataCategory, enable_confidential_logging, prefix_stack_trace +import logging + + +@prefix_stack_trace(allow_list=["FileNotFoundError", "SystemExit", "TypeError"]) +def main(): + enable_confidential_logging() + + log = logging.getLogger(__name__) + log.info("Hi there", category=DataCategory.PUBLIC) + +if __name__ == "__main__": + main() +``` + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.opensource.microsoft.com. + +When you submit a pull request, a CLA bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., status check, +comment). Simply follow the instructions provided by the bot. You will only need +to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information see the +[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or +contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any +additional questions or comments. + + + + +%package -n python3-confidential-ml-utils +Summary: Utilities for confidential machine learning +Provides: python-confidential-ml-utils +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-confidential-ml-utils +# Confidential ML Utilities + +[![python](https://github.com/Azure/confidential-ml-utils/workflows/python/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3Apython) +[![codecov](https://codecov.io/gh/Azure/confidential-ml-utils/branch/main/graph/badge.svg?token=TEWT51C5FK)](https://codecov.io/gh/Azure/confidential-ml-utils) +[![CodeQL](https://github.com/Azure/confidential-ml-utils/workflows/CodeQL/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3ACodeQL) +[![Component Governance](https://dev.azure.com/msdata/Vienna/_apis/build/status/aml-ds/Azure.confidential-ml-utils%20Component%20Governance?branchName=main)](https://dev.azure.com/msdata/Vienna/_build/latest?definitionId=13909&branchName=main) +[![PyPI version](https://badge.fury.io/py/confidential-ml-utils.svg)](https://badge.fury.io/py/confidential-ml-utils) +[![Python versions](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/confidential-ml-utils)](https://pypi.org/project/confidential-ml-utils/) +[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![license: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](LICENSE) + +Confidential ML is the practice of training machine learning models without +seeing the training data. It is needed in many enterprises to satisfy the +strict compliance and privacy guarantees they provide to their customers. This +repository contains a set of utilities for confidential ML, with a special +emphasis on using PyTorch in +[Azure Machine Learning pipelines](https://github.com/Azure/azureml-examples). + +## ⚠️Deprecation +**This package has been deprecated as of May 2021.** Please install `pip install shrike` and use `shrike.compliant_logging` instead. More details: https://github.com/Azure/shrike + +## Using + +For more detailed examples and API reference, see the +[docs page](https://azure.github.io/confidential-ml-utils/logging/). + +Minimal use case: + +```python +from confidential_ml_utils import DataCategory, enable_confidential_logging, prefix_stack_trace +import logging + + +@prefix_stack_trace(allow_list=["FileNotFoundError", "SystemExit", "TypeError"]) +def main(): + enable_confidential_logging() + + log = logging.getLogger(__name__) + log.info("Hi there", category=DataCategory.PUBLIC) + +if __name__ == "__main__": + main() +``` + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.opensource.microsoft.com. + +When you submit a pull request, a CLA bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., status check, +comment). Simply follow the instructions provided by the bot. You will only need +to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information see the +[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or +contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any +additional questions or comments. + + + + +%package help +Summary: Development documents and examples for confidential-ml-utils +Provides: python3-confidential-ml-utils-doc +%description help +# Confidential ML Utilities + +[![python](https://github.com/Azure/confidential-ml-utils/workflows/python/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3Apython) +[![codecov](https://codecov.io/gh/Azure/confidential-ml-utils/branch/main/graph/badge.svg?token=TEWT51C5FK)](https://codecov.io/gh/Azure/confidential-ml-utils) +[![CodeQL](https://github.com/Azure/confidential-ml-utils/workflows/CodeQL/badge.svg)](https://github.com/Azure/confidential-ml-utils/actions?query=workflow%3ACodeQL) +[![Component Governance](https://dev.azure.com/msdata/Vienna/_apis/build/status/aml-ds/Azure.confidential-ml-utils%20Component%20Governance?branchName=main)](https://dev.azure.com/msdata/Vienna/_build/latest?definitionId=13909&branchName=main) +[![PyPI version](https://badge.fury.io/py/confidential-ml-utils.svg)](https://badge.fury.io/py/confidential-ml-utils) +[![Python versions](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/confidential-ml-utils)](https://pypi.org/project/confidential-ml-utils/) +[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![license: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](LICENSE) + +Confidential ML is the practice of training machine learning models without +seeing the training data. It is needed in many enterprises to satisfy the +strict compliance and privacy guarantees they provide to their customers. This +repository contains a set of utilities for confidential ML, with a special +emphasis on using PyTorch in +[Azure Machine Learning pipelines](https://github.com/Azure/azureml-examples). + +## ⚠️Deprecation +**This package has been deprecated as of May 2021.** Please install `pip install shrike` and use `shrike.compliant_logging` instead. More details: https://github.com/Azure/shrike + +## Using + +For more detailed examples and API reference, see the +[docs page](https://azure.github.io/confidential-ml-utils/logging/). + +Minimal use case: + +```python +from confidential_ml_utils import DataCategory, enable_confidential_logging, prefix_stack_trace +import logging + + +@prefix_stack_trace(allow_list=["FileNotFoundError", "SystemExit", "TypeError"]) +def main(): + enable_confidential_logging() + + log = logging.getLogger(__name__) + log.info("Hi there", category=DataCategory.PUBLIC) + +if __name__ == "__main__": + main() +``` + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.opensource.microsoft.com. + +When you submit a pull request, a CLA bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., status check, +comment). Simply follow the instructions provided by the bot. You will only need +to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information see the +[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or +contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any +additional questions or comments. + + + + +%prep +%autosetup -n confidential-ml-utils-0.9.1 + +%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-confidential-ml-utils -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot - 0.9.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..535d9f8 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +b80e6536f75ff3cfca0650f164b2df81 confidential-ml-utils-0.9.1.tar.gz -- cgit v1.2.3