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DMTK
DMTK

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TensorFlow
TensorFlow

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1.1K
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62
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DMTK vs TensorFlow: 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; TensorFlow: Open Source Software Library for Machine Intelligence. 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.

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

DMTK is an open source tool with 2.68K GitHub stars and 596 GitHub forks. Here's a link to DMTK's open source repository on GitHub.

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 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.
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        What are some alternatives to DMTK and TensorFlow?
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        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.
        ML Kit
        ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
        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.
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        Decisions about DMTK and TensorFlow
        Conor Myhrvold
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 509.8K views
        atUber TechnologiesUber Technologies
        TensorFlow
        TensorFlow
        Keras
        Keras
        PyTorch
        PyTorch

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

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        Reviews of DMTK and TensorFlow
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        How developers use DMTK and TensorFlow
        Avatar of Eliana Abraham
        Eliana Abraham uses TensorFlowTensorFlow

        Machine Learning in EECS 445

        Avatar of Taylor Host
        Taylor Host uses TensorFlowTensorFlow

        Pilot integration for retraining.

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