This course is built around expert sessions from PyTorch Conference 2022 and focuses on advanced system-level capabilities of PyTorch for modern deep learning workloads. It is designed for experienced developers and researchers working with large models, distributed training, and high-performance AI systems.

You will explore structured linear algebra using linear operators, enabling scalable and memory-efficient mathematical operations in PyTorch. The course dives into transformer acceleration techniques such as Better Transformer and xFormers, which significantly improve inference and training performance for large language models.

Advanced parallelism strategies are covered in depth, including distributed tensors, two-dimensional parallelism, and automated pipeline parallelism using PiPPy. You will also learn about Functorch and composable function transforms, which enable advanced differentiation, vectorization, and functional programming patterns in PyTorch.

The course concludes with modern deployment and privacy-aware training topics such as federated learning and device-side computation. By the end, learners will understand how to design scalable, efficient, and production-ready deep learning systems using PyTorch’s most advanced tools and research-backed features.