TorchNeuron tutorial follows an operation from Python to Trainium
A new walkthrough combines timing tables and visual traces to investigate inference performance.
The October 21 Amazon tutorial examines inference, the execution of a trained model, on AWS Trainium. It compares an arithmetic implementation of RMSNorm, a normalization operation, with a built-in function and compiled code. Matching inputs and profiling schedules keep the examples comparable. The timing table locates costly operations; the visual trace follows them through framework dispatch, runtime scheduling and hardware execution.
Access has a prerequisite: the capture documentation requires enrollment in Native PyTorch private beta 3. A working model and installed Neuron SDK are also needed. For a complete CPU-to-device view, the profiler records both CPU and PrivateUse1 activities. Its example synchronizes Neuron before export because work is asynchronous: the model call can return while device execution continues. Without synchronization, the exported trace can be incomplete.
For local browser inspection, the Explorer guide forwards both interface and API ports through SSH and launches Explorer on EC2. Before browser upload, transfer the remote output files to your local computer. In Upload Profile, enter a profile name. For system or combined profiles, use Directory Upload to select the local directory, including ntrace.pb and trace_info.pb. Under My Uploads, refresh the processing status; once complete, click the profile name to open the interactive timeline.