DMTK vs DeepSpeed

DeepSpeed vs DMTK: What are the differences?

Developers describe DeepSpeed as "A deep learning optimization library that makes distributed training easy, efficient, and effective (By Microsoft)". It is a deep learning optimization library that makes distributed training easy, efficient, and effective. It can train DL models with over a hundred billion parameters on the current generation of GPU clusters while achieving over 5x in system performance compared to the state-of-art. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category. On the other hand, DMTK is detailed 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.

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

Some of the features offered by DeepSpeed are:

• Distributed Training with Mixed Precision
• Model Parallelism
• Memory and Bandwidth Optimizations

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

• 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.

DeepSpeed and DMTK are both open source tools. DMTK with 2.75K GitHub stars and 602 forks on GitHub appears to be more popular than DeepSpeed with 1.98K GitHub stars and 134 GitHub forks.