Pyro is a deep probabilistic programming library built on PyTorch, providing a unified framework for Bayesian inference, variational inference, and Monte Carlo methods. Its architecture centers on composable effect handlers (poutine) that enable tracing, conditioning, replay, and reparameterization of probabilistic programs. The library includes a rich set of distributions, bijective transforms for normalizing flows, automatic guide construction, and a variety of inference algorithms (SVI, HMC, NUTS, SVGD, SMC). It also offers domain-specific contrib modules for Gaussian processes, time series forecasting, epidemiology, optimal experimental design, and multi-object tracking. While primarily a software library, Pyro has strong research roots, with many features inspired by recent papers and experimental modules that may evolve. The codebase is well-structured, extensively tested, and actively maintained, though some contrib modules carry instability warnings and a few internal patches to PyTorch may require attention.