~/talk

Keynote: Do Repeat Yourself - Designing Open-source Libraries for Modern Machine Learning

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Argues for a purposeful Do Repeat Yourself strategy instead of strict DRY, applied to Hugging Face’s Transformers and Diffusers: each model lives in a single model file policy, many implementations are deliberately copied and kept synchronized by code generators, and a huge test suite enforces a uniform API. Gives concrete metrics: ~750,000 daily pip installs, 100k+ GitHub stars, 2,000 external contributors, ~20k forks, ~400k LOC and ~60,000 tests run daily, with ~40–50 commits to main per week.

AI-written summary · about

  1. 0:00DRY principle debate
  2. 2:04DRY for ML libraries
  3. 5:33DRY in open-source
  4. 8:21Readability vs abstraction
  5. 10:28Single model file policy
  6. 13:01Code generation for duplication
  7. 14:47Abandoning DRY drawbacks
  8. 15:28Q&A
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