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Many hyperparameter search\nalgorithms have been proposed for improving the efficiency of model selection,\nhowever their adaptation to the distributed compute environment is often\nad-hoc. We propose Tune, a unified framework for model selection and training\nthat provides a narrow-waist interface between training scripts and search\nalgorithms. We show that this interface meets the requirements for a broad\nrange of hyperparameter search algorithms, allows straightforward scaling of\nsearch to large clusters, and simplifies algorithm implementation. We\ndemonstrate the implementation of several state-of-the-art hyperparameter\nsearch algorithms in Tune. 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