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feat: add 2023-10-21 and 2023-11-04 videos (#30)
Signed-off-by: Gaius <[email protected]>
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title: Dragonfly & Nydus 在 AI 场景下数据分发实践 | ||
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演讲者: [戚文博](https://github.com/gaius-qi) | ||
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> 视频发布于 2023-10-21. | ||
Dragonfly 基于 P2P 技术提供高效、稳定和安全的文件分发和镜像加速,旨在成为云原生架构中的最佳实践和标准解决方案。 | ||
自 17 年开源以来,Dragonfly 被许多大规模互联网公司选用并投入生产使用,并在 18 年 10 月正式成为 CNCF Sandbox 项目。 | ||
2020 年 4 月成为 CNCF Incubating 项目。在本次演讲中,将简要介绍 Dragonfly,并介绍最新版本的特性,重点介绍在 AI 模型分发场景下使用 Dragonfly 和 Nydus 的最佳实践。 | ||
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<iframe height="480" width="720" src="//player.bilibili.com/player.html?aid=277565457&bvid=BV1Xw411F7QC&cid=1309732627&p=1" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe> | ||
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title: JuiceFS + Dragonfly + Fluid 加速 AI 场景数据分发 | ||
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演讲者: [戚文博](https://github.com/gaius-qi) | ||
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> 视频发布于 2023-11-04. | ||
数据分发的效率对于 AI 研究至关重要,它直接影响到模型训练速度和模型部署性能。在跨多个团队之间共享数据、模型或实验结果时, | ||
传统的分发方式可能会受到带宽和安全性的限制,导致协作变得复杂和低效。 | ||
与此同时,AI 研究社区越来越倾向于将其工作负载部署在 Kubernetes上,Kubernetes 提供了弹性、可扩展和资源管理的优势, | ||
有助于简化和优化 AI 模型的训练和部署流程。这种趋势使得 AI 项目能够更好地应对数据传输和处理方面的挑战。 | ||
在这一背景下,Dragonfly 云原生的镜像/文件分发系统应运而生,旨在解决云原生领域以 Kubernetes 为核心的应用镜像分发问题, | ||
在 AI 场景中有着广泛的应用。 JuiceFS 用户,在将数据存储到对象存储上时,增加了 Draonfly 以及 Fluid组件后,可以加快数据的写入和读取速度。 | ||
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<iframe height="480" width="720" src="//player.bilibili.com/player.html?aid=620522308&bvid=BV1d84y1D7uB&cid=1323298468&p=1" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe> | ||
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