首页> 美国卫生研究院文献>PLoS Clinical Trials >Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers
【2h】

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

机译:增强科学计算的可重复性:奇异容器的度量和注册表

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Here we present Singularity Hub, a framework to build and deploy Singularity containers for mobility of compute, and the singularity-python software with novel metrics for assessing reproducibility of such containers. Singularity containers make it possible for scientists and developers to package reproducible software, and Singularity Hub adds automation to this workflow by building, capturing metadata for, visualizing, and serving containers programmatically. Our novel metrics, based on custom filters of content hashes of container contents, allow for comparison of an entire container, including operating system, custom software, and metadata. First we will review Singularity Hub’s primary use cases and how the infrastructure has been designed to support modern, common workflows. Next, we conduct three analyses to demonstrate build consistency, reproducibility metric and performance and interpretability, and potential for discovery. This is the first effort to demonstrate a rigorous assessment of measurable similarity between containers and operating systems. We provide these capabilities within Singularity Hub, as well as the source software singularity-python that provides the underlying functionality. Singularity Hub is available at , and we are excited to provide it as an openly available platform for building, and deploying scientific containers.
机译:在这里,我们介绍了Singularity Hub(用于构建和部署用于计算移动性的Singularity容器的框架),以及具有新颖性指标的singularity-python软件,用于评估此类容器的可重复性。奇异容器使科学家和开发人员可以打包可复制的软件,而奇异枢纽通过以编程方式构建,捕获元数据,可视化和服务容器,从而为该工作流程增加了自动化。我们基于容器内容的内容散列的自定义过滤器的新颖指标允许对整个容器进行比较,包括操作系统,自定义软件和元数据。首先,我们将回顾Singularity Hub的主要用例,以及如何设计基础架构来支持现代通用工作流。接下来,我们进行三项分析,以证明构建一致性,可重复性指标,性能和可解释性以及发现潜力。这是首次尝试对容器和操作系统之间的可测量相似性进行严格评估。我们在Singularity Hub内提供这些功能,以及提供基础功能的源软件singularity-python。可在上找到Singularity Hub,我们很高兴将其作为可公开使用的平台来构建和部署科学容器。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号