DMTK vs Leaf: 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, Leaf is detailed as "Machine learning framework in Rust". Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.

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

DMTK and Leaf are both open source tools. It seems that Leaf with 5.4K GitHub stars and 270 forks on GitHub has more adoption than DMTK with 2.69K GitHub stars and 595 GitHub forks.