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Chainer

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Propel

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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.

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What is Chainer?

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.

What is Propel?

Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.

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    What tools integrate with Chainer?
    What tools integrate with Propel?
    What are some alternatives to Chainer and Propel?
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    Theano
    Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
    Torch
    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
    See all alternatives