实现Pinterest基础模型的近线性训练可扩展性
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Pinterest Engineering Blog
Pinterest 工程博客
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Inventive engineers building the first visual discovery engine, 300 billion ideas and counting.
富有创造力的工程师们打造了首个视觉发现引擎，已收录 3000 亿个创意，且数量仍在不断增加。
Sheng Huang | Software Engineer, AI Platform; Pong Eksombatchai | Machine Learning Engineer, Applied Sciences; Saurabh Vishwas Joshi | Software Engineer, AI Platform; Gaurav Arora | Software Engineer, AI Platform; Karthik Anantha Padmanabhan | Engineering Director, AI Platform
Sheng Huang | AI 平台软件工程师；Pong Eksombatchai | 应用科学机器学习工程师；Saurabh Vishwas Joshi | AI 平台软件工程师；Gaurav Arora | AI 平台软件工程师；Karthik Anantha Padmanabhan | AI 平台工程总监
At Pinterest, foundation models power recommendations for over 600 million monthly active users. Our latest Foundation Model (ACM RecSys 2025) pre-trains on two years of user activity data and is deployed into Home feed and Related Pins ranking, the platform’s two most important recommendation systems. Multi-node distributed training is the key to unlocking the next level of that capacity.¹
在 Pinterest，基础模型为超过 6 亿月活跃用户提供推荐动力。我们最新的基础模型（ACM RecSys 2025）在两年的用户活动数据上进行预训练，并部署到 Home feed 和 Related Pins 排序中，这是该平台两个最重要的推荐系统。多节点分布式训练是释放该能力下一层级潜力的关键。¹
But when we first attempted multi-node training, adding a second machine made training 5x slower, producing a scaling factor of roughly 0.2x. Enabling AWS Elastic Fabric Adapter (EFA) for OS-bypass networking fixed the networking layer and recovered a viable baseline, ...