Confidential ML Utilities

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.

⚠️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.

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():
    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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.