Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow language to extend interoperability
Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow language to extend interoperability
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DOI：
10.1016/j.acags.2023.100126
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发表时间：
2023-06-15
影响因子：
3.4
通讯作者：
Ma, Xiaogang
中科院分区：
文献类型：
--
作者：
Kale, Amruta;Sun, Ziheng;Ma, Xiaogang
近年来，工作流管理平台在人工智能(AI)领域得到了越来越多的关注。传统上，研究人员以手工、繁琐的方式自我管理他们的工作流程，严重依赖他们的记忆。由于人工智能模型的复杂性和不可预测性，他们经常难以跟踪和管理工作流的所有数据、步骤和历史。人工智能工作流耗时、冗余且容易出错，尤其是在涉及大数据的情况下。让这些工作流更易于管理的一个常见策略是使用工作流管理系统，我们推荐Geoweaver，这是一个开源的工作流管理系统，使用户能够在一个地方创建、修改和重用AI工作流。为了使我们在Geoweaver中的工作可以被其他工作流管理系统重用，我们创建了一个附加功能geoweaver_CWL，这是一个可以自动将Geoweaver AI工作流转换成通用工作流语言(CWL)格式的Python包。它将允许研究人员轻松地共享、交换、修改、重新使用其他符合CWL的软件中的现有工作流并构建新的工作流。我们对Geoweaver创建的现有工作流程进行了用户研究，以收集建议并填补我们的包和Geoweaver之间的空白。评估证实，geoweaver_CWL可以带来一个精通的人工智能流程，同时披露进一步扩展的机会。Geoweaver_cwl包在https://pypi.org/project/g eoweaver-cwl/0.0.1/上公开发布。
Recently, workflow management platforms are gaining more attention in the artificial intelligence (AI) community. Traditionally, researchers self-managed their workflows in a manual and tedious way that heavily relies on their memory. Due to the complexity and unpredictability of AI models, they often struggled to track and manage all the data, steps, and history of the workflow. AI workflows are time-consuming, redundant, and errorprone, especially when big data is involved. A common strategy to make these workflows more manageable is to use a workflow management system, and we recommend Geoweaver, an open-source workflow management system that enables users to create, modify and reuse AI workflows all in one place. To make our work in Geoweaver reusable by the other workflow management systems, we created an add-on functionality geoweaver_cwl, a Python package that automatically converts Geoweaver AI workflows into the Common Workflow Language (CWL) format. It will allow researchers to easily share, exchange, modify, reuse, and build a new workflow from existing ones in other CWL-compliant software. A user study was conducted with the existing workflows created by Geoweaver to collect suggestions and fill in the gaps between our package and Geoweaver. The evaluation confirms that geoweaver_cwl can lead to a well-versed AI process while disclosing opportunities for further extensions. The geoweaver_cwl package is publicly released online at https://pypi.org/project/g eoweaver-cwl/0.0.1/.