Many ML practitioners have some intuition about how to make models run fast, but fewer know how to analyze why a model is slow. In Part 1 of this series, you will learn how to systematically analyze inference performance and identify where the time is spent.
Before you profile a workload, it helps to understand how Neuron integrates with PyTorch.
The Neuron backend uses PyTorch's PrivateUse1 extension point. This mechanism lets an accelerator integrate with the framework without adding a dedicated dispatch key such as cuda. The torch_neuronx package works with upstream PyTorch to register Neuron as a native backend for PyTorch code. For details about this extension mechanism, see Facilitating New Backend Integration by PrivateUse1.
