AI BriefingHow Pinterest Achieved Near-Linear Training Scalability for Its Foundation Model
Key point
Pinterest solved performance degradation issues in distributed training to achieve near-linear training scalability for its foundation model.
Details
Pinterest is leveraging a Foundation Model to power its recommendation systems for over 600 million monthly active users (MAU). This model is pretrained on 2 years of user activity data and plays a core role in the Home Feed and Related Pins ranking systems.
In its initial multi-node distributed training attempts, Pinterest faced a low Scaling Factor of around 0.2x, with training speed slowing down by 5x when a second machine was added. This is a typical bottleneck that occurs when training large-scale models.
Pinterest's engineering team focused on optimizing the infrastructure and software stack as a whole to resolve these issues and achieve Near-Linear Training Scalability.
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