To improve GPU utilization during the training of a large-scale AI model for fraud detection, what should be optimized?

Optimizing the data pipeline with DALI (Data Loading Library) is a crucial approach to improving GPU utilization during the training of large-scale AI models. DALI is designed to speed up the data loading and preprocessing operations, which are often bottlenecks in deep learning workflows. By efficiently handling data preparation, including tasks like image decoding, augmentation, and normalization, DALI can help ensure that the GPU receives a continuous stream of data. This keeps the GPU engaged in computations without idling due to waiting on data and thereby enhances overall training performance.

In contrast, switching from NVMe to traditional HDD storage would likely decrease performance because HDDs have significantly slower read/write speeds compared to NVMe, impacting data access times. Increasing the number of NVMe storage devices could improve throughput, but without optimizing the data pipeline, the advantages may not be fully realized. Disabling RAPIDS for CPU-based processing would not inherently improve GPU utilization and could hinder performance for applications that benefit from GPU acceleration in data processing. Thus, focusing on optimizing the data pipeline is a strategic method to ensure that the capabilities of the GPU are effectively utilized during model training.