What’s New in PyTorch Profiler 1.9? | PyTorch
pytorch.org · 2,312 words · saved by 1 readers
An open source machine learning framework that accelerates the path from research prototyping to production deployment.
PyTorch Profiler v1.9 has been released! The goal of this new release (previous PyTorch Profiler release ) is to provide you with new state-of-the-art tools to help diagnose and fix machine learning performance issues regardless of whether you are working on one or numerous machines. The objective is to target the execution steps that are the most costly in time and/or memory, and visualize the work load distribution between GPUs and CPUs. Here is a summary of the five major features being released: Distributed Training View : This helps you understand how much time and memory is consumed in y
related reading
- Making Deep Learning go Brrrr From First Principleshorace.io
- PyTorch Profiling 101 with Modded-NanoGPTblog.underfit.ai
- ⏱️ Performance Profiling - Composerdocs.mosaicml.com
- PyTorch internals : ezyang's blogblog.ezyang.com
- 🥽 Analyzing Tracesdocs.mosaicml.com
- Get Startedpytorch.org
- PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUssebastianraschka.com
- PiTorch: ML on Baremetal Raspberry Pis | projectsmasonjwang.com
- UW PLSE | How does torch.compile work?uwplse.org
- State of torch.compile for training (August 2025) : ezyang's blogblog.ezyang.com
- PyTorch (@PyTorch) / Xx.com
- PyTorch, a year in…. – PyTorchpytorch.org