revamps for Pytorch 1.0 with design changes and added Static graph support

1 min read

The Pytorch team updated their official website “” for PyTorch 1.0 yesterday. The new update comprises minor changes to the overall look and feel of the website. In addition to that, more information has been added under the tutorials section for converting your PyTorch models to a static graph.

PyTorch is a Python-based scientific computing package which uses the power of graphics processing units. It is also one of the preferred deep learning research platforms built to offer maximum flexibility and speed.

Key Updates

Design Changes

The layout of the webpage is still the same. But color changes have been made with additional tabs included on top of the webpage.


Previously, there were only five tabs, namely, get started, about, support, discuss and docs. Now, there are eight tabs included namely, Get Started, Features, Ecosystem, Blog, Tutorials, Docs, Resources, and Github.


Updated Tutorials

With new tutorial tab, additional information has been provided for users to convert their models into a static graph, which is a feature in the upcoming PyTorch 1.0 version.

Added static graph support

One of the main differences between TensorFlow and PyTorch is that TensorFlow uses static computational graphs while PyTorch uses dynamic computational graphs. In TensorFlow we first set up the computational graph, then execute the same graph many times.

There has been an additional section under tutorials on static graphs. This implementation makes use of basic TensorFlow operations to set up a computational graph, then executes the graph many times to actually train a fully-connected ReLU network.

For more details on the changes, visit the official PyTorch website.

Read Next

What is PyTorch and how does it work?

Can a production ready PyTorch 1.0 give TensorFlow a tough time?

PyTorch 0.3.0 releases, ending stochastic functions