Brief Reminder: We are building Vizstack: a suite of programming tools that visualize code structure & execution — for better understanding & simpler debugging in existing languages. Consider the last time you encountered an unfamilar codebase or had to debug a nasty algorithm. We believe that this code complexity can be tamed with better developer tools. We believe the time is right to reinvent the programming experience, so that developers can build bigger, faster, safer, and more intricate software.
To learn more, visit the home page.
We finished building our first prototypes of our neural network interpretability tool. This a great use case for many components of the Vizstack ecosystem: core, vz-logger, and nodal.
This week, we used our development time to tackle some high value action items:
- Implemented port routing and alleys
- Visualized tensors in graph
- Designed interaction strategies
- Built demo for AI research colleagues
- Added edge routing for ports
- Improved layout of graphs
Before, the layout of graphs was messy, and there was no edge routing to make the edges look neat:
Now, we've improved our graph layout algorithm and added curved edge routing. This looks much better for debugging neural networks!
repo: https://github.com/vizstack/vz-pytorch
Edge routing refers to the process of transforming edges in a graph after the layout process has completed so that they avoid obstacles and generally make the graph look cleaner. In particular, we chose to implement orthogonal edge routing, since having edges made up of only horizontal or vertical segments makes graphs feel more inviting to explore, which is especially critical in large graphs.
Before:
With Curves:
With Ports:
