DMTK vs Gluon: 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; Gluon: Deep Learning API from AWS and Microsoft. A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
DMTK and Gluon can be primarily classified as "Machine Learning" tools.
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, Gluon provides the following key features:
- Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
- Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
- Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
DMTK is an open source tool with 2.69K GitHub stars and 595 GitHub forks. Here's a link to DMTK's open source repository on GitHub.