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English(EN) Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

Atompack存储格式加速原子机器学习训练数据读取

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Atompack，这是一种专为原子机器学习训练数据集设计的新型存储格式和分发层。该格式针对读密集型工作负载进行了优化，在这种工作负载中，训练管道以随机顺序重复访问完整的分子记录。Atompack展示了显著的性能提升，在随机读取方面比ASE LMDB等现有解决方案快96倍，并且生成的工件体积小79%。 AI

影响 优化机器学习训练的数据访问，可能加快模型开发速度并降低大型数据集的存储成本。

排序理由 该集群描述了一种新的机器学习训练数据集存储格式和分发层，该格式和分发层在研究论文中提出。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Atompack存储格式加速原子机器学习训练数据读取

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该集群描述了一种新的机器学习训练数据集存储格式和分发层，该格式和分发层在研究论文中提出。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Atompack：面向读密集型原子机器学习训练数据集的存储与分发层

    Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts. This workload differs from interactive scienti…

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