Containerization for Heterogeneous and Hybrid Parallelism
Abstract This paper investigates the viability and efficiency of parameter search for machine learning (ML) pipelines through containerization in diverse computational environments. We leverage parallelization by executing each test in the grid search as an independent container, allowing for scalable and efficient parameter tuning. Our study
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