mlsysim 0.1.0

ML Systems Infrastructure Modeling Engine — first-principles analytical framework for ML workloads.

[!NOTE] 📌 Early release (2026)

MLSys·im shipped with the 2026 MLSysBook refresh. The modeling platform, APIs, and lab integrations are actively iterated as we harden the simulator and teaching workflows.

Feedback — GitHub issues or pull requests.

🚀 MLSys·im: The Modeling Platform

The physics-grounded analytical simulator powering the Machine Learning Systems ecosystem.
Provides a unified "Single Source of Truth" (SSoT) for modeling systems from sub-watt microcontrollers to exaflop-scale global fleets.

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🏗 The 5-Layer Analytical Stack

mlsysim implements a "Progressive Lowering" architecture, separating high-level workloads from the physical infrastructure that executes them.

Layer	Domain	Key Components
Layer A	Workload Representation mlsysim.models	FLOPs, parameters, and intensity. e.g., Llama3_70B, ResNet50
Layer B	Hardware Registry mlsysim.hardware	Concrete specs for real-world silicon. e.g., H100, TPUv5p, Jetson
Layer C	Infrastructure mlsysim.infra	Grid profiles and datacenter sustainability. e.g., PUE, Carbon Intensity, WUE
Layer D	Systems & Topology mlsysim.systems	Fleet configurations and network fabrics. e.g., Doorbell, AutoDrive Scenarios
Layer E	Execution & Resolvers mlsysim.core.solver	The 3-tier math engine: Models, Solvers, and Optimizers (Design space search).

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🚀 Quick Usage: The Agent-Ready CLI

mlsysim is a first-principles analytical calculator for ML systems. It provides a terminal UI for humans and a strict JSON API for CI/CD pipelines and AI agents.

Accuracy note: mlsysim predictions are typically within 2–5× of measured performance for well-characterized workloads. For production capacity planning, always validate with benchmarks. This tool formalizes the back-of-envelope math that senior engineers do intuitively — it is not a substitute for profiling or load testing.

1. Explore the Registry (The Zoo)

Discover built-in hardware, models, and infrastructure without reading source code: mlsysim zoo hardware
mlsysim zoo models

2. Quick Evaluation (CLI Flags)

Evaluate the physics of a workload on a specific hardware node instantly: mlsysim eval Llama3_8B H100 --batch-size 32

3. Deep Simulation (Infrastructure as Code)

Define your entire cluster and SLA constraints in a declarative mlsys.yaml file:

# example_cluster.yaml
version: "1.0"
workload:
  name: "Llama3_70B"
  batch_size: 4096
hardware:
  name: "H100"
  nodes: 64
ops:
  region: "Quebec"
  duration_days: 14.0
constraints:
  assert:
    - metric: "performance.latency"
      max: 50.0

Then compile and evaluate the 3-lens scorecard (Feasibility, Performance, Macro): mlsysim eval example_cluster.yaml

4. CI/CD & Agentic Automation

Every command supports strict, schema-validated JSON output. If an assert constraint is violated, the CLI returns a semantic Exit Code 3.

# Export the JSON Schema for your IDE or AI Agent
mlsysim schema > schema.json

# Run an evaluation in a CI pipeline
tco=$(mlsysim --output json eval example_cluster.yaml | jq .macro.metrics.tco_usd)

5. Design Space Search (Optimizers)

Use the Tier 3 Engineering Engine to automatically find the optimal configuration: mlsysim optimize parallelism example_cluster.yaml
mlsysim optimize placement example_cluster.yaml --carbon-tax 150

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🛡 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.

⚠️ What This Tool Does Not Model

MLSysim is an analytical hardware calculator, not a production deployment simulator. The 22 walls model physical and economic constraints that bound ML system performance. Several critical production concerns are deliberately out of scope:

Concern	Why it matters	Where to learn more
Data drift / distribution shift	The #1 cause of production ML failures — model accuracy degrades silently as input distributions change	Sculley et al. (2015), "Hidden Technical Debt in ML Systems"
Model versioning & rollback	Production requires running multiple versions, A/B testing, and safe rollback	Huyen (2022), Designing Machine Learning Systems
Monitoring & observability	You cannot manage what you cannot measure — prediction distributions, latency percentiles, error rates	Google SRE Book (2016); Huyen (2022)
Feature store freshness	Stale features silently degrade real-time models (recommendations, fraud detection)	Uber Michelangelo (2017)
Software bugs & misconfigurations	Most outages are caused by software, not hardware	Barroso et al. (2018)
Human factors	Team velocity, on-call burden, and organizational alignment often dominate outcomes	Brooks (1975), The Mythical Man-Month

Passing all 22 walls is necessary but not sufficient for a successful production deployment.

Students using this tool should understand that infrastructure physics (what mlsysim models) is one dimension of a multi-dimensional engineering challenge.

📖 How to Cite

If you use mlsysim in your research or teaching, please cite:

@software{mlsysim2026,
  author       = {Janapa Reddi, Vijay},
  title        = {{MLSysim}: A Composable Analytical Framework for Machine Learning Systems},
  year         = {2026},
  url          = {https://mlsysbook.ai/mlsysim},
  version      = {0.1.0},
  institution  = {Harvard University}
}

🛠 Installation

MLSys·im is designed to be highly modular. Install only what you need:

# Core physics engine only (fastest, smallest footprint)
pip install mlsysim

# Install with the beautiful Terminal UI & YAML support
pip install "mlsysim[cli]"

# Install with dependencies for interactive labs (Marimo, Plotly)
pip install "mlsysim[labs]"

🐍 Python API Usage

The framework is just as powerful inside a Python script or Jupyter Notebook. The SystemEvaluator provides a clean, unified entry point for full-stack analysis:

import mlsysim

# 1. Define the scenario
model = mlsysim.Models.Language.Llama3_8B
hardware = mlsysim.Hardware.Cloud.H100

# 2. Run the evaluation
evaluation = mlsysim.SystemEvaluator.evaluate(
    scenario_name="Llama-3 8B on H100",
    model_obj=model,
    hardware_obj=hardware,
    batch_size=32,
    precision="fp16",
    efficiency=0.45
)

# 3. View the beautifully formatted scorecard
print(evaluation.scorecard())

Efficiency Parameter Guide

The efficiency parameter (0.0–1.0) captures the gap between peak hardware performance and what your software stack actually achieves. Use these guidelines:

Scenario	Efficiency	Rationale
Training (Megatron-LM, large Transformer)	0.40–0.55	Well-optimized GEMM + FlashAttention
Training (PyTorch eager, small model)	0.08–0.15	Kernel launch overhead dominates
Inference decode, batch=1	0.01–0.05	Memory-bound; compute nearly idle
Inference decode, batch=32+	0.15–0.35	Batch amortizes weight loading
Inference prefill, long context	0.30–0.50	Compute-bound GEMM + attention
TinyML (TFLite Micro on ESP32)	0.05–0.15	Interpreter overhead, no tensor cores

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Contributors

Thanks to these wonderful people for helping improve MLSys·im!

Legend: 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder

Vijay Janapa Reddi 🧑‍💻 🎨 ✍️ 🧠 maintenance

Peter Koellner 🪲 ✍️

Zeljko Hrcek 🧑‍💻

Rocky 🧑‍💻

Recognize a contributor: Comment on any issue or PR:

@all-contributors please add @username for code, doc, ideas, or bug