
layout: blog_detail
title: 'How Computational Graphs are Constructed in PyTorch'
author: Preferred Networks
featured-img: 'assets/images/augmented_computational_graph.png'

In the previous post we went over the theoretical foundations of automatic differentiation and reviewed the implementation in PyTorch. In this post, we will be showing the parts of PyTorch involved in creating the graph and executing it. In order to understand the following contents, please read @ezyang’s wonderful blog post about PyTorch internals.
Autograd components
First of all, let’s look at where the different components of autograd live: