Opacus is a production-grade PyTorch library for training deep learning models with differential privacy (DP). It implements the DP-SGD algorithm with per-sample gradient computation, gradient clipping, and noise addition. The library provides a clean API via `PrivacyEngine.make_private()` that wraps any PyTorch model, optimizer, and data loader with differential privacy components. It supports multiple per-sample gradient backends (hooks, functorch, ExpandedWeights, ghost clipping), distributed training (DDP, FSDP, tensor parallelism), and several privacy accounting mechanisms (RDP, Gaussian DP, PRV). Opacus also includes a module validator that automatically detects and replaces incompatible layers (e.g., BatchNorm → GroupNorm, LSTM → DPLSTM), a batch memory manager for virtual batching, and comprehensive scheduling utilities. The codebase is well-tested, actively maintained with recent commits, and includes extensive documentation and tutorials. Although it carries some complexity from multiple backends and experimental features (research folder), the core abstractions are clean, modular, and reusable.