A Machine Learning Gateway for Scientific Workflow Design-Reference-Cited by-同舟云学术

A Machine Learning Gateway for Scientific Workflow Design

Published:2020-09-29 Issue: Volume:2020 Page:1-15
ISSN:1875-919X
Container-title:Scientific Programming
language:en
Short-container-title:Scientific Programming

Author:

Broll Brian¹^ORCID,Timalsina Umesh¹,Völgyesi Péter¹,Budavári Tamás²,Lédeczi Ákos¹,Acacio Sanchez Manuel E.

Affiliation:

1. Institute for Software Integrated Systems and Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA

2. Department of Applied Mathematics and Statistics, Department of Physics and Astronomy, and Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Abstract

The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms.

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

Link

http://downloads.hindawi.com/journals/sp/2020/8867380.pdf

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