automatic import of python-confidential-ml-utils

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+/confidential-ml-utils-0.9.1.tar.gz
diff --git a/python-confidential-ml-utils.spec b/python-confidential-ml-utils.spec
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+%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 <Python_Bot@openeuler.org> - 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