Gluon vs Propel: What are the differences?

Developers describe Gluon as "Deep Learning API from AWS and Microsoft". A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. On the other hand, Propel is detailed as "Machine learning for JavaScript". Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

Gluon and Propel can be categorized as "Machine Learning" tools.

Some of the features offered by Gluon are:

• Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
• Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
• Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.

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

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

Propel is an open source tool with 2.81K GitHub stars and 81 GitHub forks. Here's a link to Propel's open source repository on GitHub.