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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>
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🚀 mlsysim: The ML Systems Modeling Platform
mlsysim is the high-performance, physics-grounded analytical simulator powering the Machine Learning Systems textbook ecosystem. It provides a unified "Single Source of Truth" (SSoT) for modeling systems from sub-watt microcontrollers to exaflop-scale global fleets.
🏗 The 5-Layer Analytical Stack
mlsysim implements a "Progressive Lowering" architecture, separating high-level workloads from the physical infrastructure that executes them.
Layer A: Workload Representation (mlsysim.models)
High-level model definitions (TransformerWorkload, CNNWorkload).
- Math: FLOPs, parameter counts, and arithmetic intensity.
- Key Models:
Models.Llama3_70B,Models.GPT3,Models.ResNet50.
Layer B: Hardware Registry (mlsysim.hardware)
Precise, concrete specifications for real-world silicon.
- Cloud:
Hardware.H100,Hardware.H200,Hardware.MI300X,Hardware.TPUv5p. - Mobile/Workstation:
Hardware.iPhone,Hardware.Snapdragon,Hardware.MacBookM3Max. - Edge/Tiny:
Hardware.Jetson,Hardware.TeslaFSD,Hardware.ESP32,Hardware.Arduino.
Layer C: Infrastructure & Environment (mlsysim.infra)
Regional grid profiles and datacenter sustainability.
- Math: PUE, Carbon Intensity (gCO2/kWh), WUE.
- Grids:
Infra.Quebec,Infra.Poland,Infra.US_Avg.
Layer D: Systems & Topology (mlsysim.systems)
Fleet configurations, network fabrics, and narrative scenarios.
- Scenarios:
Applications.Doorbell,Applications.AutoDrive,Applications.Frontier.
Layer E: Execution & Solvers (mlsysim.core.solver)
The physics-grounded solvers that resolve the hierarchy of constraints.
SingleNodeSolver: Roofline and Iron Law performance.ServingSolver: LLM Pre-fill vs. Decoding and KV-Cache growth.DistributedSolver: 3D Parallelism (TP/PP/DP) and Network Oversubscription.SustainabilitySolver: Carbon Footprint and Water usage.
🚀 Quick Usage: The System Evaluation
The primary way to use mlsysim is through the Hierarchy of Constraints.
import mlsysim
# 1. Pick a Lighthouse Scenario
scenario = mlsysim.Applications.Doorbell
# 2. Run a Multi-Level Evaluation
evaluation = scenario.evaluate()
# 3. View the Scorecard
print(evaluation.scorecard())
Example Scorecard Output:
=== SYSTEM EVALUATION: Smart Doorbell ===
Level 1: Feasibility -> [PASS]
Model fits in memory (0.5 MB / 0.5 MB)
Level 2: Performance -> [PASS]
Latency: 105.00 ms (Target: 200 ms)
Level 3: Macro/Economics -> [PASS]
Annual Carbon: 5.1 kg | TCO: $31,501
🛡 Stability & Integrity
Because this core powers a printed textbook, we enforce strict Invariant Verification. Every physical constant is traceable to a primary source (datasheet or paper), and dimensional integrity is enforced via pint.
🛠 Installation
pip install -e .