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English(EN) FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost

FreeScale 方法大幅削减推荐模型训练成本

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇新论文介绍了一种名为 FreeScale 的方法，旨在提高序列推荐模型分布式训练的效率。FreeScale 通过采用负载均衡的输入样本以及通信与计算重叠，解决了由“掉队者”和通信缓慢引起的计算瓶颈。该技术还利用了 SM-Free 方法来管理 GPU 资源竞争，据称在 256 个 H100 GPU 上将计算气泡减少了 90% 以上。 AI

影响 优化了推荐模型的分布式训练，有望降低计算成本和训练时间。

排序理由 介绍分布式训练新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

FreeScale 方法大幅削减推荐模型训练成本

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介绍分布式训练新方法的学术论文。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade, Yanli Zhao, Liang Luo, Jennifer Cao, Lisen Deng, Siqiao Chen, Chenyu Zhao, Tristan Rice, Daniel Johnson, Min Si, Tiantu Xu, Yi Zhang, Siqi Yan, Chuanhao Zhuge, Min Ni, Bi Xue, Qunshu Zhang, Shen Li ·

    FreeScale：以最小的扩展成本实现序列推荐模型的分布式训练

    arXiv:2604.24073v1 Announce Type: new Abstract: Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent he…

  2. arXiv cs.LG TIER_1 English(EN) · Shen Li ·

    FreeScale：以最小的扩展成本实现序列推荐模型的分布式训练

    Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent heterogeneity in data characteristics frequently r…

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

    FreeScale：以最小的扩展成本实现序列推荐模型的分布式训练

    Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent heterogeneity in data characteristics frequently r…

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