DMTK

4
18
+ 1
0
Gluon

19
54
+ 1
2
Add tool

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.

Pros of DMTK
Pros of Gluon
    No pros available

    Sign up to add or upvote prosMake informed product decisions

    Sign up to add or upvote consMake informed product decisions

    What is DMTK?

    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.

    What is Gluon?

    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.
    What companies use DMTK?
    What companies use Gluon?
      No companies found

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with DMTK?
      What tools integrate with Gluon?
        No integrations found
        What are some alternatives to DMTK and Gluon?
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        PyTorch
        PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
        CUDA
        A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
        See all alternatives
        Interest over time