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Propel vs Chainer: What are the differences?
Propel: Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript; Chainer: A Powerful, Flexible, and Intuitive Framework for Neural Networks. It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
Propel and Chainer can be categorized as "Machine Learning" tools.
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, Chainer provides the following key features:
- Supports CUDA computation
- Runs on multiple GPUs
- Supports various network architectures
Propel and Chainer are both open source tools. It seems that Chainer with 4.98K GitHub stars and 1.32K forks on GitHub has more adoption than Propel with 2.8K GitHub stars and 80 GitHub forks.