August 26, 2022
Introducing nvFuser, a deep learning compiler for PyTorch
nvFuser is a Deep Learning Compiler for NVIDIA GPUs that automatically just-in-time compiles fast and flexible kernels to reliably accelerate users’ networks. It provides significant speedups for deep learning networks running on Volta and later CUDA accelerators by generating fast custom “fusion” kernels at runtime. nvFuser is specifically designed to meet the unique requirements of the PyTorch community, and it supports diverse network architectures and programs with dynamic inputs of varyi...
August 18, 2022
Easily list and initialize models with new APIs in TorchVision
TorchVision now supports listing and initializing all available built-in models and weights by name. This new API builds upon the recently introduced Multi-weight support API, is currently in Beta, and it addresses a long-standing request from the community.
July 19, 2022
What Every User Should Know About Mixed Precision Training in PyTorch
Efficient training of modern neural networks often relies on using lower precision data types. Peak float16 matrix multiplication and convolution performance is 16x faster than peak float32 performance on A100 GPUs. And since the float16 and bfloat16 data types are only half the size of float32 they can double the performance of bandwidth-bound kernels and reduce the memory required to train a network, allowing for larger models, larger batches, or larger inputs. Using a module like torch.amp...
July 15, 2022
Case Study: PathAI Uses PyTorch to Improve Patient Outcomes with AI-powered Pathology
PathAI is the leading provider of AI-powered technology tools and services for pathology (the study of disease). Our platform was built to enable substantial improvements to the accuracy of diagnosis and the measurement of therapeutic efficacy for complex diseases, leveraging modern approaches in machine learning like image segmentation, graph neural networks, and multiple instance learning.
