PyTorch Mobile

    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

    Learn more or get started on Android or iOS.