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DMTK

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19
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Xcessiv

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DMTK vs Xcessiv: What are the differences?

What is 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.

What is Xcessiv? Fully managed web application for automated machine learning. A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

DMTK and Xcessiv 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, Xcessiv provides the following key features:

  • Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
  • Any model following the Scikit-learn API can be used as a base learner
  • Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches

DMTK and Xcessiv are both open source tools. DMTK with 2.69K GitHub stars and 595 forks on GitHub appears to be more popular than Xcessiv with 1.19K GitHub stars and 95 GitHub forks.

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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 Xcessiv?

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

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Jobs that mention DMTK and Xcessiv as a desired skillset
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San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
Pinterest
San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
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    What tools integrate with DMTK?
    What tools integrate with Xcessiv?
      No integrations found
      What are some alternatives to DMTK and Xcessiv?
      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.
      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.
      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.
      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