mirror of
https://github.com/harvard-edge/cs249r_book.git
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Vijay Janapa Reddi
cb0ae4082f
Run quartodoc build against the refactored package (engine/ extracted from core/, infra->infrastructure): regenerate the API reference so every stub points at the new paths. Adds engine.*/infrastructure.* stubs, removes the 41 stale core.solver.* / core.engine.* / core.scenarios.* / core.config.* / core.evaluation.* / infra*.qmd orphans, and refreshes the package pages (core now primitives-only) + index. Verified: all 47 documented symbols resolve in the new package; every index link resolves; zero stale mlsysim.core.<engine-mod> / mlsysim.infra references anywhere in docs/. (objects.json inventory is gitignored -- regenerated at build.)
34 lines
1000 B
Plaintext
34 lines
1000 B
Plaintext
# engine.solver.ResponsibleEngineeringModel { #mlsysim.engine.solver.ResponsibleEngineeringModel }
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```python
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engine.solver.ResponsibleEngineeringModel()
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```
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Models the computational cost of responsible AI practices (Wall 20: Safety).
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This model quantifies the 'Safety Tax' — the additional compute and data
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required for differential privacy or fairness guarantees.
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Literature Source:
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1. Abadi et al. (2016), "Deep Learning with Differential Privacy."
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2. Anil et al. (2022), "Large-Scale Differentially Private BERT."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.ResponsibleEngineeringModel.solve) | Calculates the overhead of responsible engineering practices. |
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### solve { #mlsysim.engine.solver.ResponsibleEngineeringModel.solve }
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```python
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engine.solver.ResponsibleEngineeringModel.solve(
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base_training_time,
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epsilon=1.0,
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delta=1e-05,
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min_subgroup_prevalence=0.01,
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)
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```
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Calculates the overhead of responsible engineering practices.
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