JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Morell, José Ángel; Camero, Andrés; Alba, Enrique

doi:10.1109/access.2019.2950287

Cited by 9 publications

(5 citation statements)

References 56 publications

(78 reference statements)

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“…JSDoop 58 is a high-performance volunteer-based web computing library introduced in [ 84 ] that splits a problem into tasks and distributes the computation using various queues. TensorFlow.js is used as a proof-of-concept to train a recurrent neural network that produces or predicts the following letter of an input text.…”

Section: Front-end Deep Learning Web Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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Section: Front-end Deep Learning Web Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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“…JSDoop [10] is a library for distributed collaborative high-performance computing in web browsers, based on the MapReduce paradigm as Madoop. Both JSDoop clients and servers are implemented in JavaScript.…”

Section: B Browser-based Volunteer Computingmentioning

confidence: 99%

“…Although VC comes with peculiar technological challenges (e.g., managing nodes with heterogeneous hardware and software, high dynamicity of the environment, asynchronism), this paradigm provides researchers with lower-cost computing power and reduced energy consumption. To alleviate some intrinsic limitations of VC systems and encourage joining volunteer networks, the paradigm of Browser-Based Volunteer Computing (BBVC) [9] gained popularity, also thanks to improvements in the processing capacity of web browsers and the release of powerful software libraries (e.g., WebGL, and TensorFlow.js) [10]. BBVC provides access to the volunteer network using web applications, which execute volunteer jobs in the background and transparently from the user's perspective.…”

Section: Introductionmentioning

confidence: 99%

A Volunteer Computing Architecture for Computational Workflows on Decentralized Web

et al. 2022

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The amount of accessible computational devices over the Internet offers an enormous but latent computational power. Nonetheless, the complexity of orchestrating and managing such devices requires dedicated architectures and tools and hinders the exploitation of this vast processing capacity. Over the last years, the paradigm of (Browser-based) Volunteer Computing emerged as a unique approach to harnessing such computational capabilities, leveraging the idea of voluntarily offering resources. This article proposes VFuse, a groundbreaking architecture to exploit the Browser-based Volunteer Computing paradigm via a ready-to-access volunteer network. VFuse offers a modern multi-language programming environment for developing scientific workflows using WebAssembly technology without requiring the user any local installation or configuration. We equipped our architecture with a secure and transparent rewarding mechanism based on blockchain technology (Ethereum) and distributed P2P file system (IPFS). Further, the use of Non-Fungible Tokens provides a unique, secure, and transparent methodology for recognizing the users' participation in the network. We developed a prototype of the proposed architecture and four example applications implemented with our system. All code and examples are publicly available on GitHub.

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“…Little work exists on building a solution to run distributed DL training on VC systems [11], [12], [14]. Morell et al built JSDoop [11], a browser-based VC system for training an RNN model to predict text. Our work uses grid-based VC systems.…”

Section: A Volunteer Computingmentioning

confidence: 99%

“…In Section II, we elaborate on the limited work [11]- [14] that exists on running distributed DL training using a VC-like paradigm. Our main contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

Distributed Deep Learning Using Volunteer Computing-Like Paradigm

Atre,

Jha,

Rao

2021

Preprint

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Use of Deep Learning (DL) in commercial applications such as image classification, sentiment analysis and speech recognition is increasing. When training DL models with large number of parameters and/or large datasets, cost and speed of training can become prohibitive. Distributed DL training solutions that split a training job into subtasks and execute them over multiple nodes can decrease training time. However, the cost of current solutions, built predominantly for cluster computing systems, can still be an issue. In contrast to cluster computing systems, Volunteer Computing (VC) systems can lower the cost of computing, but applications running on VC systems have to handle fault tolerance, variable network latency and heterogeneity of compute nodes, and the current solutions are not designed to do so. We design a distributed solution that can run DL training on a VC system by using a data parallel approach. We implement a novel asynchronous SGD scheme called VC-ASGD suited for VC systems. In contrast to traditional VC systems that lower cost by using untrustworthy volunteer devices, we lower cost by leveraging preemptible computing instances on commercial cloud platforms. By using preemptible instances that require applications to be fault tolerant, we lower cost by 70-90% and improve data security.

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JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Cited by 9 publications

References 56 publications

Front-end deep learning web apps development and deployment: a review

Front-end deep learning web apps development and deployment: a review

A Volunteer Computing Architecture for Computational Workflows on Decentralized Web

Distributed Deep Learning Using Volunteer Computing-Like Paradigm

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