May 01, 2019
PyTorch adds new dev tools as it hits production scale
This is a partial re-post of the original blog post on the Facebook AI Blog. The full post can be viewed here
April 29, 2019
Stochastic Weight Averaging in PyTorch
In this blogpost we describe the recently proposed Stochastic Weight Averaging (SWA) technique [1, 2], and its new implementation in torchcontrib. SWA is a simple procedure that improves generalization in deep learning over Stochastic Gradient Descent (SGD) at no additional cost, and can be used as a drop-in replacement for any other optimizer in PyTorch. SWA has a wide range of applica...
May 02, 2018
The road to 1.0: production ready PyTorch
We would like to give you a preview of the roadmap for PyTorch 1.0 , the next release of PyTorch. Over the last year, we’ve had 0.2, 0.3 and 0.4 transform PyTorch from a [Torch+Chainer]-like interface into something cleaner, adding double-backwards, numpy-like functions, advanced indexing and removing Variable boilerplate. At this time, we’re confident that the API is in a reasonable and stable state to confidently release a 1.0.
April 22, 2018
PyTorch 0.4.0 Migration Guide
Welcome to the migration guide for PyTorch 0.4.0. In this release we introduced many exciting new features and critical bug fixes, with the goal of providing users a better and cleaner interface. In this guide, we will cover the most important changes in migrating existing code from previous versions:
March 05, 2018
Tensor Comprehensions in PyTorch
Tensor Comprehensions (TC) is a tool that lowers the barrier for writing high-performance code. It generates GPU code from a simple high-level language and autotunes the code for specific input sizes.
January 19, 2018
PyTorch, a year in....
Today marks 1 year since PyTorch was released publicly. It’s been a wild ride — our quest to build a flexible deep learning research platform. Over the last year, we’ve seen an amazing community of people using, contributing to and evangelizing PyTorch — thank you for the love.
June 27, 2017
PyTorch Internals Part II - The Build System
In the first post I explained how we generate a torch.Tensor object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:
May 11, 2017
A Tour of PyTorch Internals (Part I)
The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:
