NVIDIA Explains PyTorch Performance Tuning with TensorRT 11.1
The post gives a conceptual framework for judging whether a compiled engine is genuinely better, which matters when automated tuning trades accuracy for speed without a clear method to evaluate that trade-off. Engineers deploying PyTorch models with TensorRT can use Global Performance Tuner in the 11.1 release to automate tuning instead of hand-tuning configurations.
NVIDIA published a blog post outlining a framework for reasoning about performance optimization of PyTorch models. It addresses why individually promising optimization decisions can fail end to end and how accuracy should constrain performance tuning. The post introduces the Global Performance Tuner, an automatic performance tuning feature released in TensorRT 11.1, and explains the concepts through an example and a mathematical framework.
This blog post from @nvidia is intended to develop a clearer way of reasoning about performance optimization for PyTorch models and other representations.
It explores questions such as: What makes one compiled engine better than another? Why can individually promising decisions fail end to end? How should accuracy constrain performance optimization?
It introduces the Global Performance Tuner, a feature for automatic performance tuning released in TensorRT 11.1, and explores these questions through an intuitive example and a mathematical framework.
Read the full blog post here: https://t.co/KyfM4tzxaw
- kimmonismus15:27原文
