  We introduce Opacus, a free, open-source PyTorch library for training deep
learning models with differential privacy (hosted at opacus.ai). Opacus is
designed for simplicity, flexibility, and speed. It provides a simple and
user-friendly API, and enables machine learning practitioners to make a
training pipeline private by adding as little as two lines to their code. It
supports a wide variety of layers, including multi-head attention, convolution,
LSTM, GRU (and generic RNN), and embedding, right out of the box and provides
the means for supporting other user-defined layers. Opacus computes batched
per-sample gradients, providing higher efficiency compared to the traditional
"micro batch" approach. In this paper we present Opacus, detail the principles
that drove its implementation and unique features, and benchmark it against
other frameworks for training models with differential privacy as well as
standard PyTorch.
