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DMTK

4
19
+ 1
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Pipelines

27
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DMTK vs Pipelines: What are the differences?

Developers describe DMTK 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. On the other hand, Pipelines is detailed as "Machine Learning Pipelines for Kubeflow". Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

DMTK and Pipelines can be primarily classified as "Machine Learning" tools.

DMTK and Pipelines are both open source tools. It seems that DMTK with 2.69K GitHub stars and 595 forks on GitHub has more adoption than Pipelines with 944 GitHub stars and 247 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 Pipelines?

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

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What tools integrate with DMTK?
What tools integrate with Pipelines?
    No integrations found
    What are some alternatives to DMTK and Pipelines?
    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