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<p>We propose MetaStore, a scalable system to analyze meta-data produced during the process of training deep learning models. This meta-data, if analyzed appropriately, could potentially solve various data problems that impact the performance of these models. In this work, we mainly focus on solving the challenges associated with analyzing gradients, one type of meta-data known for its size and complexity. Each single gradient has a size as large as the number of parameters of the neural net – often measured in the tens of millions. This makes it extremely challenging to efficiently collect, store, and analyze a large number of gradients. MetaStore solves these problems with three key ideas. First, MetaStore addresses the gradient collection and storage challenges based on our observation that storing certain compact intermediate results produced in the back propagation process, namely, the prefix and suffix gradients, is sufficient to exactly restore the original huge gradient. Furthermore, MetaStore offers a rich set of analytics operators for users to analyze gradients. Rather than first having to restore the original gradients and then run analytics on top of this decompressed view, MetaStore directly executes these operators on the compact prefix and suffix structures, making gradient-based analytics efficient and scalable. Third, MetaStore features an optimizer that effectively solves a given data problem by automatically learning a custom model, using the output of the analytics operators as features. This optimizer thus enables users to use MetaStore to solve data problems without having to worry about complex technical problems. Our experiments on popular deep learning models such as VGG, BERT, and ResNet and benchmark image and text datasets demonstrate that MetaStore outperforms strong baseline methods from 4 to 678x in storage costs and from 2 to 1000x in running time. Moreover, on important data problems such as mislabel detection and out-of-distribution (OOD) detection, the MetaStore optimizer outperforms the baselines in accuracy by 70 percentage points.</p>

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MetaStore: A system for effectively analyzing deep learning meta-data at scale

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Linan Zheng,
  • Huayi Zhang,
  • Samuel Madden,
  • Elke Rundensteiner,
  • Lei Cao

摘要

We propose MetaStore, a scalable system to analyze meta-data produced during the process of training deep learning models. This meta-data, if analyzed appropriately, could potentially solve various data problems that impact the performance of these models. In this work, we mainly focus on solving the challenges associated with analyzing gradients, one type of meta-data known for its size and complexity. Each single gradient has a size as large as the number of parameters of the neural net – often measured in the tens of millions. This makes it extremely challenging to efficiently collect, store, and analyze a large number of gradients. MetaStore solves these problems with three key ideas. First, MetaStore addresses the gradient collection and storage challenges based on our observation that storing certain compact intermediate results produced in the back propagation process, namely, the prefix and suffix gradients, is sufficient to exactly restore the original huge gradient. Furthermore, MetaStore offers a rich set of analytics operators for users to analyze gradients. Rather than first having to restore the original gradients and then run analytics on top of this decompressed view, MetaStore directly executes these operators on the compact prefix and suffix structures, making gradient-based analytics efficient and scalable. Third, MetaStore features an optimizer that effectively solves a given data problem by automatically learning a custom model, using the output of the analytics operators as features. This optimizer thus enables users to use MetaStore to solve data problems without having to worry about complex technical problems. Our experiments on popular deep learning models such as VGG, BERT, and ResNet and benchmark image and text datasets demonstrate that MetaStore outperforms strong baseline methods from 4 to 678x in storage costs and from 2 to 1000x in running time. Moreover, on important data problems such as mislabel detection and out-of-distribution (OOD) detection, the MetaStore optimizer outperforms the baselines in accuracy by 70 percentage points.