Chainer vs DMTK

DMTK vs Chainer: What are the differences?

Developers describe DMTK as "Microsoft Distributed Machine Learning Tookit". DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. On the other hand, Chainer is detailed as "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.

DMTK and Chainer belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by DMTK are:

• DMTK Framework: a flexible framework that supports unified interface for data parallelization, hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.
• LightLDA, an extremely fast and scalable topic model algorithm, with a O(1) Gibbs sampler and an efficient distributed implementation.
• Distributed (Multisense) Word Embedding, a distributed version of (multi-sense) word embedding algorithm.

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

• Supports CUDA computation
• Runs on multiple GPUs
• Supports various network architectures

DMTK 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 DMTK with 2.71K GitHub stars and 595 GitHub forks.