To learn more about the design and architecture, check out the API docs here, and get started with any of the supported techniques using the tutorials available here.
PyTorch mobile (experimental)
Running ML on edge devices is growing in importance as applications continue to demand lower latency. It is also a foundational element for privacy-preserving techniques such as federated learning. To enable more efficient on-device ML, PyTorch 1.3 now supports an end-to-end workflow from Python to deployment on iOS and Android.
This is an early, experimental release, optimized for end-to-end development. Coming releases will focus on:

Optimization for size: Build level optimization and selective compilation depending on the operators needed for user applications (i.e., you pay binary size for only the operators you need)
Performance: Further improvements to performance and coverage on mobile CPUs and GPUs
