Hidden Bottlenecks in CNN Training That Engineers Often Miss

Modern CNN training speed is frequently limited by hidden factors beyond the network itself, such as slow data loading, file format overhead, and misconfigured hardware. By profiling, increasing DataLoader workers with pinned memory, using efficient data formats, leveraging GPU features like mixed precision and GPUDirect, and choosing the correct multi-GPU strategy, engineers can dramatically improve training throughput. The following timeline outlines the steps to identify and mitigate these bottlenecks.