Fig 1.
(A) An SoS script integrating a Python script that converts a Microsoft Excel file to a CSV file, and an R script that plots data from the CSV file. (B) An SoS script with headers for executing two scripts as separate steps of a workflow. The section headers are numbered with optional descriptions. (C) An SoS script with headers for executing two scripts as separate workflows. (D) An SoS script that uses a global parameter and string interpolation to unify filenames. (E) An SoS script that accepts a parameter from the command line and defines input and output targets for each step. (F) An SoS script with input and output targets derived from a global parameter. SoS provides a list of format options to aid in the formatting of filenames (e.g., ‘:n’ in this example removes extensions from filenames).
Fig 2.
Shown are abstracted versions of implementations of high-dimensional regression workflows in SoS demonstrating the (A) process-oriented, (B) outcome-oriented, and (C) mixed workflow styles. These workflow style definitions are inspired by Leipzig [37], who classified workflow systems as either “explicit convention” (process-oriented) or “implicit convention” (outcome-oriented) frameworks. The workflow logics are primarily reflected by the SoS section names and step statements “input,” “output,” and “depends.” Full versions of these workflows are available online [28].
Fig 3.
This diagram illustrates SoS remote task execution managed on a local machine whose file system is not shared with the remote host. 1) SoS translates the local task to a remote task via file-path mapping. 2) At the same time, SoS transfers files necessary for the computation to the remote host. 3) SoS then uses preconfigured templates to send commands to the remote job-management system to submit jobs, if applicable, or to directly execute jobs on the remote host. 4) Output files are generated at the end of computation on the remote host. 5) Results are automatically transferred back to the local host, thus completing the computational task on the local machine without direct interaction with remote hosts. Of note, different tasks can be sent to multiple remote hosts simultaneously. The local machine only provides the computational resources necessary to manage and monitor these tasks.