PyTorch Mobile Runtime for Android | Brad Heintz

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 Android.

In this talk, PyTorch Partner Engineer Brad Heintz walks through steps for setting up the PyTorch Runtime for Android projects. For more information on PyTorch Mobile, visit:

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