高效的多模式管理

Nils Strassenburg, Dominic Kupfer, J. Kowal, T. Rabl
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引用次数: 1

摘要

深度学习模型被部署在越来越多的工业领域,如零售和汽车应用。一个模型的实例通常执行一个特定的任务,这就是大型软件系统并行使用多个模型的原因。假设生产软件中的所有模型都必须被管理,这就导致了管理相关模型集的问题,即多模型管理。现有的方法在此任务上表现不佳,因为它们是为保存单个大型模型而优化的,而不是同时保存一组相关模型。在本文中,我们通过提出三种优化方法来探索多模型管理的空间:(1)保存完整模型表示并最小化保存元数据量的基线方法。(2)一种更新方法,通过保存参数更新而不是完整模型来减少与基线相比的存储消耗。(3)不保存模型参数而保存模型来源数据的溯源方法。我们评估了管理汽车电池模型和图像分类模型的多模型管理用例的方法。我们的结果表明,在保存和恢复时间方面,基线比现有的方法要好一个数量级以上,而更复杂的方法可以将存储消耗减少多达99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Multi-Model Management
Deep learning models are deployed in an increasing number of industrial domains, such as retail and automotive applications. An instance of a model typically performs one specific task, which is why larger software systems use multiple models in parallel. Given that all models in production software have to be managed, this leads to the problem of managing sets of related models, i.e., multi-model management. Existing approaches perform poorly on this task because they are optimized for saving single large models but not for simultaneously saving a set of related models. In this paper, we explore the space of multi-model management by presenting three optimized approaches: (1) A baseline approach that saves full model representations and minimizes the amount of saved metadata. (2) An update approach that reduces the storage consumption compared to the baseline by saving parameter updates instead of full models. (3) A provenance approach that saves model provenance data instead of model parameters. We evaluate the approaches for the multi-model management use cases of managing car battery cell models and image classification models. Our results show that the baseline outperforms existing approaches for save and recover times by more than an order of magnitude and that more sophisticated approaches reduce the storage consumption by up to 99%.
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