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Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-593104
Jobflow: Computational Workflows Made Simple
- We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow workflows can be executed either locally or across distributed compute environmentsWe present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.…
Autor*innen: | Andrew S. RosenORCiD, Max GallantORCiD, Janine GeorgeORCiD, Janosh RiebesellORCiD, Hrushikesh SahasrabuddheORCiD, Jimmy-Xuan ShenORCiD, Mingjian WenORCiD, Matthew L. EvansORCiD, Guido Petretto, David WaroquiersORCiD, Gian-Marco RignaneseORCiD, Kristin A. PerssonORCiD, Anubhav JainORCiD, Alex M. GanoseORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Journal of Open Source Software |
Jahr der Erstveröffentlichung: | 2024 |
Organisationseinheit der BAM: | 6 Materialchemie |
6 Materialchemie / 6.0 Abteilungsleitung und andere | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | The Open Journal |
Jahrgang/Band: | 9 |
Ausgabe/Heft: | 93 |
Aufsatznummer: | 5995 |
Erste Seite: | 1 |
Letzte Seite: | 7 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Automation; Computational Materials Science; Computations; Software; Workflow |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
DOI: | 10.21105/joss.05995 |
URN: | urn:nbn:de:kobv:b43-593104 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 19.01.2024 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |