DMTK vs Paperspace: What are the differences?
DMTK: 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; Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
DMTK and Paperspace 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, Paperspace provides the following key features:
- Intelligent alert
- Two-factor authentication
- Share drives
DMTK is an open source tool with 2.72K GitHub stars and 598 GitHub forks. Here's a link to DMTK's open source repository on GitHub.