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.
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:
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: