Max Autotune Prune Choices Based On Shared Mem Flag Wasn't As Groundbreaking As It Was Promised To Be

Max Autotune Prune Choices Based On Shared Mem Flag Wasn't As Groundbreaking As It Was Promised To Be
pytorch-memes, machine-learning-memes, performance-memes, optimization-memes, training-memes | ProgrammerHumor.io

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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