Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.
TorchNeuron supports standard PyTorch workflows on Trainium, including eager execution, torch.compile, and standard PyTorch APIs, with minimal code changes.
Key points
- In Part 1 of this series, you will learn how to systematically analyze inference performance and identify where the time is spent.
- The walkthrough uses AWS Trainium, a purpose-built AI chip for deep learning training and inference workloads, and TorchNeuron, its open source native PyTorch backend.
- Additionally, TorchNeuron integrates with Neuron Kernel Interface (NKI) to enable custom operators and performance optimization for Trainium hardware.
- How to configure torch.profiler with the Neuron backend and capture a clean trace.
Sources (1)
- [1]Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.Hugging Face Blog · Oct 21, 04:04 PM
TorchNeuron supports standard PyTorch workflows on Trainium, including eager execution, torch.compile, and standard PyTorch APIs, with minimal code changes.
In Part 1 of this series, you will learn how to systematically analyze inference performance and identify where the time is spent.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 21, 2026Google Cloud Ships llm-d 1.0 With Cache-Aware Routing for TPU and GPU Fleets
- Oct 20, 2026Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
- Oct 20, 2026GPU-Accelerated Construction of the Gabriel Graph: Parallelization Strategies and Applications to Large-Margin Classification and Graph-Based Clustering
- Oct 20, 2026[AINews] DeepSeek DeepInfer: Open Source SOTA Inference running 155T toks/day/cluster making $22m/day at ~80% margin for a 81 day payback
- Oct 20, 2026MPS in PyTorch 2.15
- Oct 19, 2026How Deepgram optimized voice AI at scale with AWS Trainium
