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Utilities for confidential machine learning

Project description

Confidential ML Utilities

python codecov CodeQL Component Governance PyPI version Python versions PyPI - Downloads code style: black license: MIT

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.


This package has been deprecated as of May 2021. Please install pip install shrike and use shrike.compliant_logging instead. More details:


For more detailed examples and API reference, see the docs page.

Minimal use case:

from confidential_ml_utils import DataCategory, enable_confidential_logging, prefix_stack_trace
import logging

@prefix_stack_trace(allow_list=["FileNotFoundError", "SystemExit", "TypeError"])
def main():

    log = logging.getLogger(__name__)"Hi there", category=DataCategory.PUBLIC)

if __name__ == "__main__":


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

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. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.

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