Profiling TorchNeuron (Part 1): system-level profiling on AWS Trainium
This Amazon post teaches how to profile PyTorch inference on AWS Trainium using the TorchNeuron backend, the key_averages() table and Neuron Explorer. It compares three RMSNorm implementations; both torch.rms_norm and torch.compile cut device time to roughly one-sixth of the naive version.
What the article says
On October 21, 2026, Amazon published Profiling TorchNeuron (Part 1) on the Hugging Face blog, a beginner-level guide to system-level profiling on AWS Trainium. It focuses on understanding why a model is slow, not just making it fast, using TorchNeuron, the open source native PyTorch backend for Trainium, which supports eager execution and torch.compile and integrates with the Neuron Kernel Interface.
The post explains how to configure the profiler with NeuronConfig, call torch.neuron.synchronize() in the loop, read the key_averages() table and open traces in Neuron Explorer. The experiment compares three RMSNorm versions: naive PyTorch ops (2,355 µs Neuron time, 6 calls), torch.rms_norm (403 µs, 1 call) and the naive version with torch.compile (376 µs, 1 call). The author concludes that fusion cuts device time to about one-sixth. Scripts are in the torch-neuronx examples, and Part 2 will cover device-level profiling.
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Summary written by AnnuIA in its own words from the official page of Hugging Face. The content and rights belong to the publisher: please read the original article.