ManimML: Communicating Machine Learning Architectures with Animation

TL;DR

ManimML is an open-source Python library that simplifies creating animations of machine learning architectures directly from code, making it easier for practitioners to visualize and communicate ML algorithms effectively.

Contribution

It introduces a familiar syntax for specifying neural networks that automatically generates animations, bridging the gap between ML development and visualization.

Findings

Enables easy animation of neural networks from existing architectures

Reduces the need for complex animation software skills

Facilitates better communication of ML concepts

Abstract

There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.