Optimizing Deep Learning Performance Through First-Principles Reasoning
By
tosh
Slow-proofed and worth the wait. Worth its weight in flour.
Summary
The article discusses improving deep learning model performance by reasoning from first principles rather than relying on ad-hoc tricks and tips found on social media. It critiques the common approach of using random optimization techniques without understanding the underlying system architecture, and advocates for a more scientific, principle-driven methodology to performance optimization in deep learning.
Key quotes
· 4 pulledOften, folk fall back to a grab-bag of tricks that might've worked before or saw on a tweet.
Use in-place operations! Set gradients to None! Install PyTorch 1.10.0 but not 1.10.1!
performance on modern systems (particularly deep learning) often feels as much like alchemy as it does science.
reasoning from first principles can still eliminate broad swathes of approaches, thus making the problem much
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