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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.)
40 lines
1.2 KiB
Plaintext
40 lines
1.2 KiB
Plaintext
# engine.solver.SustainabilityModel { #mlsysim.engine.solver.SustainabilityModel }
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```python
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engine.solver.SustainabilityModel()
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```
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Calculates Datacenter-scale Sustainability metrics.
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Handles Power Usage Effectiveness (PUE), Carbon Intensity,
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and Water Usage Effectiveness (WUE) across different regional grids.
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This model simulates the 'Infrastructure Tax' — the energy spent on
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cooling and power delivery rather than on neural computation.
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Literature Source:
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1. Patterson et al. (2021), "Carbon Emissions and Large Neural Network
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Training."
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2. Belkhir & Elmeligi (2018), "Assessing ICT Global Emissions Footprint."
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3. Wu et al. (2022), "Sustainable AI: Environmental Implications,
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Challenges and Opportunities."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.SustainabilityModel.solve) | Calculates energy, carbon, and water footprint for a fleet operation. |
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### solve { #mlsysim.engine.solver.SustainabilityModel.solve }
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```python
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engine.solver.SustainabilityModel.solve(
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fleet,
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duration_days,
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datacenter=None,
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mfu=1.0,
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embodied_carbon_per_device=0.0,
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)
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```
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Calculates energy, carbon, and water footprint for a fleet operation.
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