June 27, 2022
How Computational Graphs are Executed in PyTorch
Welcome to the last entry into understanding the autograd engine of PyTorch series! If you haven’t read parts 1 & 2 check them now to understand how PyTorch creates the computational graph for the backward pass!
June 23, 2022
Geospatial deep learning with TorchGeo
TorchGeo is a PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
June 16, 2022
How Disney Improved Activity Recognition Through Multimodal Approaches with PyTorch
Introduction
May 18, 2022
Introducing Accelerated PyTorch Training on Mac
In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch training on Mac only leveraged the CPU, but with the upcoming PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.
March 16, 2022
Running PyTorch Models on Jetson Nano
Overview NVIDIA Jetson Nano, part of the Jetson family of products or Jetson modules, is a small yet powerful Linux (Ubuntu) based embedded computer with 2/4GB GPU. With it, you can run many PyTorch models efficiently. This document summarizes our experience of running different deep learning models using 3 different mechanisms on Jetson Nano:
March 14, 2022
Introducing PyTorch Fully Sharded Data Parallel (FSDP) API
Recent studies have shown that large model training will be beneficial for improving model quality. During the last 3 years, model size grew 10,000 times from BERT with 110M parameters to Megatron-2 with one trillion. However, training large AI models is not easy—aside from the need for large amounts of computing resources, software engineering complexity is also challenging. PyTorch has been working on building tools and infrastructure to make it easier.
