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40 lines
1.1 KiB
Plaintext
40 lines
1.1 KiB
Plaintext
# solvers.SustainabilityModel { #mlsysim.solvers.SustainabilityModel }
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```python
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solvers.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.solvers.SustainabilityModel.solve) | Calculates energy, carbon, and water footprint for a fleet operation. |
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### solve { #mlsysim.solvers.SustainabilityModel.solve }
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```python
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solvers.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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