Amazon Web Services has published a comprehensive tutorial on profiling deep learning workloads on Trainium, its custom AI chip designed for training and inference. The guide, published on the Hugging Face Blog, walks developers through system-level performance analysis using TorchNeuron, AWS's native PyTorch backend for Trainium, enabling engineers to identify exactly where computational time is spent during model execution.
The tutorial introduces Neuron Explorer, a browser-based visualization tool that displays hardware timelines, device traces, and per-engine breakdowns alongside numerical profiling data from torch.profiler. AWS demonstrates the approach with RMSNorm, comparing three different implementations—naive PyTorch operations, a fused built-in kernel, and torch.compile optimized code—to show how various approaches affect performance on Trainium hardware.
TorchNeuron integrates with PyTorch through the PrivateUse1 extension mechanism, allowing developers to use standard PyTorch workflows with minimal code changes while leveraging Trainium's specialized hardware capabilities. The profiling infrastructure captures multiple data streams including device instructions and runtime traces, helping teams optimize their models for inference and training workloads at scale.
Key Points
AWS published a TorchNeuron profiling tutorial enabling developers to systematically identify performance bottlenecks on Trainium hardware
Neuron Explorer provides browser-based visualization of hardware execution traces, device timelines, and framework dispatch information
TorchNeuron enables standard PyTorch code to run on Trainium with minimal changes through the PrivateUse1 backend integration
Practical examples compare RMSNorm implementations to demonstrate profiling methodology across naive operations, fused kernels, and torch.compile approaches