Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and language models. However, in this paper we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data centers. Recommendation models present unique challenges in training because they exercise not only compute but also memory capacity as well as memory and network bandwidth. As model size and complexity increase, efficiently scaling training becomes a challenge. To address it we design Zion - Facebook's next-generation large-memory training platform that consists of both CPUs and accelerators. Also, we discuss the design requirements of future scale-out training systems.

中文翻译：

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Facebook 数据中心的深度学习训练：纵向扩展和横向扩展系统的设计

大规模训练对于确保机器学习模型的高性能和准确性非常重要。在 Facebook，我们使用许多不同的模型，包括计算机视觉、视频和语言模型。然而，在本文中，我们专注于深度学习推荐模型 (DLRM)，它们占我们数据中心超过 50% 的训练需求。推荐模型在训练中提出了独特的挑战，因为它们不仅锻炼计算能力，还锻炼内存容量以及内存和网络带宽。随着模型大小和复杂性的增加，有效地扩展训练成为一个挑战。为了解决这个问题，我们设计了 Zion——Facebook 的下一代大内存训练平台，由 CPU 和加速器组成。此外，我们还讨论了未来横向扩展培训系统的设计要求。