DMTK vs Keras

Overview

DMTK

Stacks4

Followers18

Votes0

GitHub Stars2.7K

Forks559

Keras

Stacks1.1K

Followers1.1K

Votes22

DMTK vs Keras: 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; Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.

DMTK and Keras 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, Keras provides the following key features:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

DMTK and Keras are both open source tools. Keras with 42.5K GitHub stars and 16.2K forks on GitHub appears to be more popular than DMTK with 2.69K GitHub stars and 595 GitHub forks.

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Advice on DMTK, Keras

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.64k views4.64k

Comments

Fabian

Software Developer at DCSIL

Feb 11, 2021

Decided

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

55.4k views55.4k

Comments

Detailed Comparison

DMTK	Keras
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.	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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.	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU
Statistics
GitHub Stars 2.7K	GitHub Stars -
GitHub Forks 559	GitHub Forks -
Stacks 4	Stacks 1.1K
Followers 18	Followers 1.1K
Votes 0	Votes 22
Pros & Cons
No community feedback yet	Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug
Integrations
No integrations available	TensorFlow scikit-learn Python

What are some alternatives to DMTK, Keras?

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.

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.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.