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Opacus

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About

Opacus is a library that enables the training of PyTorch models while implementing differential privacy. It efficiently supports various PyTorch model types with minimal code changes and allows users to monitor their privacy budgets in real time. With features like scalable vectorized gradient computation, built on PyTorch, and open-source extensibility, Opacus facilitates differential privacy research and contributions from the community.

Platform
Web
Keywords
machine learningopen sourcepytorchdifferential privacyprivacy engine
Task
model training
Features

real-time privacy budget tracking

open-source and modular api

minimal modification required for existing networks

supports most pytorch models

scalable vectorized gradient computation

FAQs
What is Opacus?

Opacus is a library that enables training PyTorch models with differential privacy, supporting minimal code changes and online privacy budget tracking.

Is Opacus open-source? What is the license?

Yes! Opacus is open-source and licensed under the Apache 2.0 license.

How can I report a bug or ask a question?

You can report bugs on GitHub issues and ask questions in the dedicated PyTorch Discussion Forum.

I'd like to contribute to Opacus. How can I do that?

You can contribute to Opacus by submitting pull requests on GitHub following the Opacus contribution guide.

How do I attach the privacy engine?

Training with Opacus requires instantiating a PrivacyEngine and attaching it to your optimizer. Details are provided in the documentation.

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