Utilities for confidential machine learning
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.
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
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()
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