meta-pytorch/opacus

Health v2 · maintenance only

Adoption not part of the score

1952 stars · 398 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Opacus is a PyTorch library for training neural networks with differential privacy via DP-SGD, requiring minimal code changes through its PrivacyEngine API. It supports fast per-sample gradient computation, privacy budget tracking, and is extensible for differential privacy research.

Use cases

• train pytorch models with differential privacy
• add dp-sgd to my neural network training
• track privacy budget epsilon during model training
• fine-tune transformers with differential privacy
• research differentially private deep learning algorithms
• reduce memory usage of dp-sgd with ghost clipping

When to choose

• you train models in PyTorch and need formal differential privacy guarantees
• you want minimal code changes and online privacy budget tracking
• you are a DP researcher needing an extensible, modular implementation

When to avoid

• you use TensorFlow, JAX, or non-PyTorch frameworks
• you need privacy guarantees for inference or data release rather than training
• your model architecture is incompatible with per-sample gradient computation

Facets

library · maturity active

machine-learning deep-learning privacy llm-training machine-learning privacy deep-learning large-language-models python cross-platform differential-privacy dp-sgd pytorch privacy-preserving-machine-learning gradient-clipping privacy-budget lora gpu

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markdown · JSON · MCP: product_card(name="meta-pytorch/opacus")