Data as of Sep 25, 2026 · Based on 4,025,084 AI responses across 13,338 prompts · See how Parse measures this
Opacusopacus.aiOpacus is an open-source PyTorch library that enables training neural networks with differential privacy by wrapping the model, optimizer, and data loader with a PrivacyEngine to inject noise and enforce gradient clipping. It provides scalable per-sample gradient computation that is 10x faster than microbatching, supports most PyTorch models with minimal modification, and offers a modular API for differential privacy research. Developed by Meta Platforms, Inc., Opacus includes extensive docs, tutorials, an API reference, and a public GitHub repository for community contributions.
The market map · 5 of 61 labelled
Identity & Age Verification APIs →
MOSTLY AIPicked instead4 questions
Synthetic Data Vault (SDV)Picked instead3 questions
TonicPicked instead3 questions
Tonic FabricatePicked instead1 question
Applied IntuitionPicked instead1 questionNo contexts measured yet.
Excerpts where Opacus appeared in the AI's answer
Opacus (Meta's open-source PyTorch library) is the gold standard for adding rigorous mathematical privacy guarantees (DP-SGD) to model training.
Opacus (by PyTorch): A library that enables training PyTorch models with differential privacy.
Excerpts where Opacus appeared in the AI's answer
Opacus (by Meta) : While not a synthetic data platform itself, this PyTorch library is the gold-standard open-source engine
Opacus (by Meta): A high-speed PyTorch library that enables training generative deep learning models (like GANs or Transformers) using DP-SGD to safely output synthetic data.
Excerpts where Opacus appeared in the AI's answer
Opacus (by Meta): A popular library for training PyTorch machine learning models with Differential Privacy .
