Carbon-Aware Scheduling of Training Jobs: Emissions Reduction Without Throughput Loss
DOI:
https://doi.org/10.5281/zenodo.22660904Keywords:
Carbon-Aware Scheduling of Training Jobs; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Carbon-Aware Scheduling of Training Jobs is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for carbon-aware scheduling of
training jobs, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.
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