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a78f1bd8b0
Complete MLSYSIM v0.1.0 implementation with: - Documentation website (Quarto): landing page with animated hero and capability carousel, 4 tutorials (hello world, LLM serving, distributed training, sustainability), hardware/model/fleet/infra catalogs, solver guide, whitepaper, math foundations, glossary, and full quartodoc API reference - Typed registry system: Hardware (18 devices across 5 tiers), Models (15 workloads), Systems (fleets, clusters, fabrics), Infrastructure (grid profiles, rack configs, datacenters) - Core types: Pint-backed Quantity, Metadata provenance tracking, custom exception hierarchy (OOMError, SLAViolation) - SimulationConfig with YAML/JSON loading and pre-validation - Scenario system tying workloads to systems with SLA constraints - Multi-level evaluation scorecard (feasibility, performance, macro) - Examples, tests, and Jetson Orin NX spec fix (100 → 25 TFLOP/s) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
24 lines
724 B
Plaintext
24 lines
724 B
Plaintext
# core.config.SimulationConfig { #mlsysim.core.config.SimulationConfig }
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```python
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core.config.SimulationConfig()
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```
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Standard schema for an ML Systems Simulation.
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Can be loaded from YAML, JSON, or Python Dicts.
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## Methods
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| Name | Description |
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| --- | --- |
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| [validate_physical_feasibility](#mlsysim.core.config.SimulationConfig.validate_physical_feasibility) | Runs a pre-simulation check to ensure the configuration isn't |
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### validate_physical_feasibility { #mlsysim.core.config.SimulationConfig.validate_physical_feasibility }
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```python
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core.config.SimulationConfig.validate_physical_feasibility()
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```
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Runs a pre-simulation check to ensure the configuration isn't
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physically impossible (e.g., OOM on start).
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