A Machine Learning Gateway for Scientific Workflow Design
A Machine Learning Gateway for Scientific Workflow Design
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DOI：
10.1155/2020/8867380
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
B. Broll;U. Timalsina;P. Völgyesi;T. Budavári;Á. Lédeczi;M. A. Sanchez
中科院分区：
文献类型：
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作者：
B. Broll;U. Timalsina;P. Völgyesi;T. Budavári;Á. Lédeczi;M. A. Sanchez
本文介绍了DeepForge，这是科学计算深度学习的网关。DeepForge提供了一个易于使用，但功能强大的视觉/文本界面，以促进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，内置的版本控制和多用户协作支持，DeepForge提高了可重复性和易于访问性，并实现了机器学习管道的远程执行。该工具目前支持TensorFlow/Keras，但其可扩展的架构可以轻松集成其他平台。
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.