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A Machine Learning Gateway for Scientific Workflow Design
Scientific Programming Pub Date : 2020-09-29 , DOI: 10.1155/2020/8867380
Brian Broll 1 , Umesh Timalsina 1 , Péter Völgyesi 1 , Tamás Budavári 2 , Ákos Lédeczi 1 , Manuel E. Acacio Sanchez
Affiliation  

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.

中文翻译:

用于科学工作流设计的机器学习网关

该论文介绍了 DeepForge,这是一种通向科学计算深度学习的门户。DeepForge 提供了一个易于使用但功能强大的视觉/文本界面,以方便新手和专家快速开发深度学习模型。利用基于云的基础设施、内置版本控制和多用户协作支持,DeepForge 提高了可重复性和易用性,并支持机器学习管道的远程执行。该工具目前支持 TensorFlow/Keras,但其可扩展架构可以轻松集成其他平台。
更新日期:2020-09-29
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