What is the most likely reason for slower training times in one model compared to others on a shared GPU cluster?

In the context of slow training times for a model on a shared GPU cluster, it is essential to consider how the data preprocessing pipeline can significantly impact overall performance. A poorly optimized preprocessing pipeline can introduce bottlenecks, causing delays in how quickly data becomes available for training. This could manifest as slower input/output operations, inefficient data handling, or increased latency during the data loading phase, all of which can affect the training speed of the model.

If this model spends a substantial amount of time in preprocessing, then the backend GPU resources will remain underutilized, leading to longer training times. This situation can be exacerbated on a shared GPU cluster where multiple models are competing for resources, and any inefficiencies in the preprocessing can lead to a disproportionate impact on training times.

Additionally, while other factors such as resource contention, model complexity (number of parameters), or learning rate settings could also contribute to training performance, they would not specifically address the latency issues that arise from data handling inefficiencies. Therefore, the data preprocessing pipeline being underperforming is a plausible reason for the model’s slower training times when running on a shared infrastructure.