July 20, 2022 - This recipe explains how to use PyTorch profiler and measure the time and memory consumption of the model’s operators.

1 week ago - This tutorial describes how to use PyTorch Profiler with DeepSpeed.

May 29, 2026 - No prerequisites apart from basic PyTorch. Treat this as a leisurely read with some "Aha!" moments. The structure of the post is intentionally question-led: we open a trace, ask "wait, why is that happening?", and chase the answer until something clicks. By the end you should know: how to set up torch.profiler and what it actually hands back,

This tutorial seeks to teach users about using profiling tools such as nvsys, rocprof, and the torch profiler in a simple transformers training loop. We will cover how to use the PyTorch profiler to identify performance bottlenecks, understand ...

This recipe explains how to use PyTorch profiler and measure the time and memory consumption of the model’s operators.

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1import torch 2import habana_frameworks.torch.core as htcore 3 4activities = [torch.profiler.ProfilerActivity.CPU] 5 6#CUDA: 7#device = torch.device('cuda:0') 8#activities.append(torch.profiler.ProfilerActivity.CUDA) 9 10#HPU: 11device = torch.device('hpu') 12activities.append(torch.profiler.ProfilerActivity.HPU) 13 14with torch.profiler.profile( 15 schedule=torch.profiler.schedule(wait=0, warmup=20, active=5, repeat=1), 16 activities=activities, 17 on_trace_ready=torch.profiler.tensorboard_trace_handler('./profile_logs')) as profiler: 18 for i in range(100): 19 input = torch.tensor([[i]*10]*10, dtype=torch.float32, device=device) 20 result = torch.matmul(input, input) 21 result.to('cpu') 22 htcore.mark_step() 23 profiler.step()

Usually the first step in performance optimization is to do profiling, e.g. to identify performance hotspots of a workload. This gist tells basic knowledge of performance profiling on PyTorch, you will get: ... This tutorial takes one of my recent projects - pssp-transformer as an example to guide you through path of PyTorch CPU peformance optimization.

The profiler will skip the first skip_first steps, then wait for wait steps, then do the warmup for the next warmup steps, then do the active recording for the next active steps and then repeat the cycle starting with wait steps.

March 26, 2024 - Profiling a Training Task with PyTorch Profiler and viewing it on Tensorboard This post briefly and with an example shows how to profile a training task of a model with the help of PyTorch profiler …

This profiler will record training_step_and_backward, training_step, backward, validation_step, test_step, and predict_step by default. The output below shows the profiling for the action training_step_and_backward. The user can provide PyTorchProfiler(record_functions={...}) to extend the scope of profiled functions.

July 20, 2022 - This tutorial demonstrates how to use TensorBoard plugin with PyTorch Profiler to detect performance bottlenecks of the model.

Visualizing PyTorch Profiler Output in TensorBoard, PyTorch Documentation, 2024 - A practical tutorial explaining how to leverage TensorBoard for visual analysis of profiler traces, facilitating bottleneck identification.

July 20, 2022 - Capture PyTorch Python code in ... of the code · Build out a small class that will serve as a simple performance “profiler”, collecting runtime statistics about each part of the model from actual runs....

This tutorial demonstrates how to use TensorBoard plugin with PyTorch Profiler to detect performance bottlenecks of the model.

March 25, 2021 - Review PyTorch Profiler documentation, give Profiler a try and let us know about your experience. Provide your feedback on PyTorch Discussion Forum or file issues on PyTorch GitHub. Access comprehensive developer documentation for PyTorch · View Docs › · Get in-depth tutorials for beginners and advanced developers ·

PyTorch includes a profiler API that is useful to identify the time and memory costs of various PyTorch operations in your code.

May 29, 2026 - I think this could serve as a great tutorial for anyone beginning their journey into profiling, optmization, or even kernel developement. Link: Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Any feedback, criticisms, or observations are very welcome!