Propel vs Manifold: What are the differences?

Propel: Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript; Manifold: A model-agnostic visual debugging tool for machine learning. Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.

Propel and Manifold belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Propel are:

• Run anywhere, in the browser or natively from Node
• Target multiple GPUs and make TCP connections
• PhD optional

On the other hand, Manifold provides the following key features:

• Performance Comparison View
• Feature Attribution View
• Histogram / heatmap

Propel and Manifold are both open source tools. Propel with 2.79K GitHub stars and 80 forks on GitHub appears to be more popular than Manifold with 778 GitHub stars and 58 GitHub forks.