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. As of PyTorch 1.3, PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android.
This is an early, experimental release that we will be building on in several areas over the coming months:
- Provide APIs that cover common preprocessing and integration tasks needed for incorporating ML in mobile applications
- Support for QNNPACK quantized kernel libraries and support for ARM CPUs
- 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)
- Further improvements to performance and coverage on mobile CPUs and GPUs