Straggler mitigation for iterative machine learning via task preemption
US11562270B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 2, 2020 |
| Grant date | Jan 24, 2023 |
| Priority date | — |
| Expiry date | Jul 16, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can run preemptable tasks distributed according to a distributed environment, wherein each task of a plurality of preemptable tasks has been assigned two or more of the training data samples to process during each iteration. Embodiments of the present invention can, upon verifying that a preemption condition for each iteration is satisfied: preempt any task of the preemptable tasks that have started processing training data samples assigned to it, and update the cognitive model based on outputs obtained from completed tasks, including outputs obtained from both the preempted tasks and completed tasks that have finished processing all training data samples as assigned to it.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.