16Implement a data loader that batches and shuffles a dataset, and explain efficient input pipelines.▼mediumNVIDIAGoogleMeta1 replies○ sign inData loading is where training pipelines silently bottleneck. The signal is a correct shuffle-then-batch iterator plus knowing why prefetching and parallel loading keep the GPU fed.Open full answer →
126Build a mini data loader with sharding for distributed training: split data across workers without overlap.▼mediumMetaNVIDIAGoogle2 replies◆ premiumA from-scratch test of distributed input pipelines. The signal is partitioning data across workers with no overlap and no gaps, epoch-consistent shuffling with a shared seed, and handling the uneven-last-batch problem. Here is the implementation.Open full answer →
