%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