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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>
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
from pydantic import BaseModel, ConfigDict, Field, model_validator
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from typing import Optional, Union, Dict, Any
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from ..models.registry import Models
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from ..hardware.registry import Hardware
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from ..infra.registry import Infra
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from ..systems.registry import Fabrics
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from .exceptions import OOMError
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class SimulationConfig(BaseModel):
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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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"""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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# Identifiers (can be names from registry or full objects)
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model: str = Field(description="Name of the model (e.g., 'GPT3', 'ResNet50')")
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hardware: str = Field(description="Name of the accelerator (e.g., 'A100', 'H100')")
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# Execution Parameters
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batch_size: int = 1
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precision: str = "fp16"
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efficiency: float = 0.5
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# Scale Parameters
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fleet_size: int = 1
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fabric: str = "100GbE"
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# Environment
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region: str = "US_Avg"
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duration_days: float = 30.0
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@model_validator(mode='after')
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def validate_physical_feasibility(self) -> 'SimulationConfig':
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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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"""
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# 1. Resolve registry items
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m_obj = getattr(Models, self.model, None)
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h_obj = getattr(Hardware, self.hardware, None)
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if not m_obj or not h_obj:
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return self # Let the solver handle missing objects with better errors
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# 2. Check basic OOM (Weights only)
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weight_size = m_obj.size_in_bytes()
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if weight_size > h_obj.memory.capacity:
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raise ValueError(
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f"Configuration Infeasible: {self.model} weights ({weight_size.to('GB')}) "
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f"exceed {self.hardware} capacity ({h_obj.memory.capacity.to('GB')})."
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)
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return self
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def load_config(data: Dict[str, Any]) -> SimulationConfig:
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"""Helper to parse a dictionary into a validated simulation configuration."""
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return SimulationConfig.model_validate(data)
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