Gluon vs Propel: What are the differences?
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.