June 28, 2022

PyTorch 1.12: TorchArrow, Functional API for Modules and nvFuser, are now available

We are excited to announce the release of PyTorch 1.12 (release note)! This release is composed of over 3124 commits, 433 contributors. Along with 1.12, we are releasing beta versions of AWS S3 Integration, PyTorch Vision Models on Channels Last on CPU, Empowering PyTorch on Intel® Xeon® Scalable processors with Bfloat16 and FSDP API. We want to sincerely thank our dedicated community for your contributions.

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June 28, 2022

New library updates in PyTorch 1.12

We are bringing a number of improvements to the current PyTorch libraries, alongside the PyTorch 1.12 release. These updates demonstrate our focus on developing common and extensible APIs across all domains to make it easier for our community to build ecosystem projects on PyTorch.

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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!

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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.

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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.

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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...

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