Introducing Panels: Custom Visualizations for Machine Learning
In the last three years since Comet was founded, our users and customers trained millions of models on anything from self-driving cars to speech recognition, and from Covid-19 protein prediction to satellite image object detection. Based on your requests we added more and more visualization capabilities including chart builders, confusion matrices, audio players and many more. But as the complexity of the use cases, requirements and customization grew, we realized that we needed a more scalable way to grow Comet’s functionality and provide users the ability to build anything they want!
Today, we are excited to announce The key principles in designing Comet Panels were the following: they want without the limitations of a GUI or a specific dependency Comet Panels, which has been under development and testing for the past year. Starting today, you can tap into the ecosystems of JS/HTML/CSS and create new widgets with custom styles and behaviors, or add new visualizations and charting types. Unlimited possibilities!
- Dynamic — Panels should be dynamic and update on new experiments and results when they arrive. There’s nothing worse than a stale and misleading visualization.
- Flexible — Users should be able to build and customize
- Reusable — teammates and community members should be able to share and reuse each other’s panels.
The Comet Panel Gallery
The heart of Panels lies in the Comet Panel Gallery. Check it out to see what others have built and shared with the community.
Comet experiments already store your hyper-parameters, metrics, model definition, trained models, dataset samples and much more. Panels build on top of that data to visualize and process anything you’d like.
You can create a new panel by going to the Panels Gallery and clicking on “Create New” on the top right corner. You should see a similar code editor:
The code editor is divided into three main panes. On the left we have a full code editor with code completion, syntax highlighting etc. On the right we have a live preview of our panel. On the bottom right we see our Console pane which shows errors and exceptions. You’ll also notice the tabs on the top that allow you to switch from code to description, HTML, CSS, resources and query.
To learn how to customize your machine learning visualizations, read the rest of the article on Comet.ml.
Originally published at https://www.comet.ml on August 5, 2020.
