> [!NOTE] > **📌 Early release (2026)** > > Co-Labs shipped with the **2026** MLSysBook refresh. Lab notebooks, WASM builds, and scoring flows are **actively iterated** as we refine the hands-on curriculum. > > **Feedback** — [GitHub issues](https://github.com/harvard-edge/cs249r_book/issues) or pull requests. > > [![dev branch](https://img.shields.io/badge/branch-dev-orange?logo=git&logoColor=white)](https://github.com/harvard-edge/cs249r_book/tree/dev) [![live site](https://img.shields.io/badge/live_site-mlsysbook.ai-blue?logo=safari&logoColor=white)](https://mlsysbook.ai)

Co-Labs

33 Interactive Labs Powered by MLSys·IM
Predict → Discover → Explain

--- ## What Are Co-Labs? Co-Labs are interactive [Marimo](https://marimo.io) notebooks that bridge the gap between **reading about ML systems** (the textbook) and **building them from scratch** (TinyTorch). Every lab runs in your browser via WebAssembly — no installation required.

Textbook to Co-Labs to TinyTorch learning flow

## How Labs Work Each lab follows a consistent structure: 1. **Briefing** — Learning objectives, prerequisites, and the core question 2. **Parts A–E** — Tabbed explorations, each with a prediction lock, interactive instruments, and a reveal 3. **Synthesis** — Key takeaways, textbook connections, and a pointer to the next lab Every prediction is structured (radio buttons or numeric entry, never free text). You predict first, then explore the instruments to discover whether you were right. The gap between prediction and reality is the learning moment. ## Lab Inventory ### Volume I: Foundations (17 labs · Single-machine ML systems)

#	Slug	Title
00	`lab_00_introduction`	The Architect's Portal (orientation)
01	`lab_01_ml_intro`	The AI Triad
02	`lab_02_ml_systems`	The Iron Law
03	`lab_03_ml_workflow`	The Silent Degradation Loop
04	`lab_04_data_engr`	The Data Gravity Trap
05	`lab_05_nn_compute`	The Activation Tax
06	`lab_06_nn_arch`	The Quadratic Wall
07	`lab_07_ml_frameworks`	The Kernel Fusion Dividend
08	`lab_08_model_train`	The Training Memory Budget
09	`lab_09_data_selection`	The Data Selection Tradeoff
10	`lab_10_model_compress`	The Compression Frontier
11	`lab_11_hw_accel`	The Roofline
12	`lab_12_perf_bench`	The Speedup Ceiling
13	`lab_13_model_serving`	The Tail Latency Trap
14	`lab_14_ml_ops`	The Silent Degradation Problem
15	`lab_15_responsible_engr`	There Is No Free Fairness
16	`lab_16_ml_conclusion`	The Architect's Audit (capstone)

### Volume II: At Scale (16 labs · Distributed ML systems)

#	Slug	Title
01	`lab_01_introduction`	The Scale Illusion
02	`lab_02_compute_infra`	The Compute Infrastructure Wall
03	`lab_03_communication`	Communication at Scale
04	`lab_04_data_storage`	The Data Pipeline Wall
05	`lab_05_dist_train`	The Parallelism Puzzle
06	`lab_06_fault_tolerance`	When Failure Is Routine
07	`lab_07_fleet_orch`	The Scheduling Trap
08	`lab_08_inference`	The Inference Economy
09	`lab_09_perf_engineering`	The Optimization Trap
10	`lab_10_edge_intelligence`	The Edge Thermodynamics Lab
11	`lab_11_ops_scale`	The Silent Fleet
12	`lab_12_security_privacy`	The Price of Privacy
13	`lab_13_robust_ai`	The Robustness Budget
14	`lab_14_sustainable_ai`	The Carbon Budget
15	`lab_15_responsible_ai`	The Fairness Budget
16	`lab_16_fleet_synthesis`	The Fleet Synthesis (capstone)

## The Design Ledger Every lab saves your predictions and design decisions to the **Design Ledger** — an IndexedDB-backed persistence layer in your browser, with a local file fallback when running outside WebAssembly. Later labs read earlier decisions: Lab 08's training memory budget builds on Lab 05's activation analysis, which builds on Lab 01's magnitude calibration. The capstone labs synthesize your full Design Ledger into a portfolio. ## Running Labs ### In the Browser (Recommended) Visit the [Co-Labs site](https://mlsysbook.ai/labs/) and click any lab. They run via Marimo + WebAssembly with zero setup. ### Locally ```bash git clone https://github.com/harvard-edge/cs249r_book.git cd cs249r_book python3 -m pip install -r labs/requirements.txt python3 -m pip install -e mlsysim cd labs marimo run vol1/lab_01_ml_intro.py ``` ## Development See [PROTOCOL.md](PROTOCOL.md) for the lab development specification and [TEMPLATE.md](TEMPLATE.md) for the cell architecture and quality checklist. ### Running Tests ```bash pytest tests/test_static.py -v ``` --- ## Related Resources

Resource	Description
Textbook	ML Systems principles and practices
TinyTorch	Build your own ML framework from scratch
Discussions	Ask questions, share feedback

--- ## Contributors Thanks to these wonderful people who helped build the labs! **Legend:** 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder

_{Vijay Janapa Reddi}
🧑‍💻 🎨 ✍️

_Rocky
🪲 🧑‍💻 🎨

_{Salman Chishti}
🧑‍💻

_{Pratham Chaudhary}
🧑‍💻

_{Peter Koellner}
🪲

**Recognize a contributor:** Comment on any issue or PR: ```text @all-contributors please add @username for code, tutorial, test, or doc ``` ---

Predict. Discover. Explain.