Efficient MultiModal Data Pipeline: PyTorch IterableDataset and knapsack packing

AI Impact Summary

Efficient MultiModal Data Pipeline introduces a five-stage data preparation flow that replaces naive padding with knapsack-based packing. By leveraging PyTorch's IterableDataset and a producer-consumer model backed by Python queues, batches are constructed on-the-fly and fed to training without stalling GPUs. Two packing strategies (greedy and bin-packing/First Fit Decreasing) and a ConstantLengthDataset concept tighten batch occupancy across images, prompts, and responses, addressing the 60% padding waste observed in naive padding. This design reduces data pipeline latency, improves GPU utilization, and enables more balanced multi-GPU training for multimodal workloads, which can lower training costs and shorten time-to-solution.