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Fluid: Resource-aware Hyperparameter Tuning Engine
2021
Conference on Machine Learning and Systems
Current hyperparameter tuning solutions lack complementary execution engines to efficiently leverage distributed computation, thus ignoring the possibility of intra-and inter-GPU sharing, which exhibits poor resource usage. In this paper, we present Fluid, a generalized hyperparameter tuning execution engine, that coordinates between hyperparameter tuning jobs and cluster resources. Fluid schedules evaluation trials in such jobs using a waterfilling approach to make the best use of resources
dblp:conf/mlsys/YuLC21
fatcat:4nvwv4dbavbbzaef5qc3sedigi