Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios -- users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.
翻译:机器学习(ML)算法在帮助不同学科和机构的科学界解决大型和多样的数据问题方面呈日益增长的趋势。然而,许多可用的ML工具在方案上要求很高而且计算成本很高。MLExchange项目旨在建立一个合作平台,配备一个赋能工具,使没有深刻ML背景的科学家和设施用户能够在科学发现中使用ML和计算资源。在高层次上,我们正在瞄准一个完全的用户经验,通过网络应用程序管理和交换ML算法、工作流程和数据。由于每个组成部分都是一个独立的容器,整个平台或其个别服务可以很容易地部署在不同规模的服务器上,从个人设备(手机、智能电话等)到许多用户(同时)访问的高性能集群(HPC)不等。因此,MLExchange提供了使用假设的灵活度 -- -- 用户可以从远程服务器获得服务和资源,或者在其本地网络内运行整个平台或个人服务。