You've enabled every optimization flag known to humanity. CUDA kernels? Optimized. Batch sizes? Tuned. Mixed precision? Obviously. You've read the entire PyTorch performance guide twice, set torch.backends.cudnn.benchmark=True, and even sacrificed a USB drive to the machine learning gods.
Your training loop still moves like it's running on a Pentium II from 1997. Turns out all those fancy optimization techniques that promised "up to 10x speedup" in the blog posts were tested on datasets that fit in a teacup and hardware that costs more than a small car.
The real bottleneck? Your data loader was single-threaded the whole time. Classic.
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