6f5732558f
Add all Vol1 (labs 01-16) and Vol2 (labs 01-17) interactive Marimo labs as the first full first-pass implementation of the ML Systems curriculum labs. Each lab follows the PROTOCOL 2-Act structure (35-40 min): - Act I: Calibration with prediction lock → instruments → overlay - Act II: Design challenge with failure states and reflection Key pedagogical instruments introduced progressively: - Vol1: D·A·M Triad, Iron Law, Memory Ledger, Roofline, Amdahl's Law, Little's Law, P99 Histogram, Compression Frontier, Chouldechova theorem - Vol2: NVLink vs PCIe cliff, Bisection BW, Young-Daly T*, Parallelism Paradox, AllReduce ring vs tree, KV-cache model, Jevons Paradox, DP ε-δ tradeoff, SLO composition, Adversarial Pareto, two-volume synthesis capstone All 35 staged files pass AST syntax verification (36/36 including lab_00). Also includes: - labs/LABS_SPEC.md: authoritative sub-agent brief for all lab conventions - labs/core/style.py: expanded unified design system with semantic color tokens
Labs
Understanding the Interplay Between Algorithms and Systems
Status: Coming Summer 2026
What Are Labs?
Labs are hands-on interactive notebooks that bridge the gap between reading about ML systems (the textbook) and building them from scratch (TinyTorch).
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Textbook │────▶│ Labs │────▶│ TinyTorch │
│ │ │ │ │ │
│ Concepts & │ │ Experiment & │ │ Build from │
│ Theory │ │ Explore │ │ Scratch │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
READ EXPLORE BUILD
The Learning Journey
| Phase | Resource | What You Do |
|---|---|---|
| Understand | Textbook | Learn concepts, theory, and system design principles |
| Experiment | Labs | Explore tradeoffs, tweak parameters, see how decisions ripple through systems |
| Build | TinyTorch | Implement everything from scratch, own every line of code |
Why Labs?
ML systems are where algorithms meet hardware. A model that works perfectly in theory can fail in practice due to memory limits, latency constraints, or numerical precision. Labs help you develop intuition for these algorithm-system interactions.
- See the tradeoffs — How does batch size affect memory? How does quantization affect accuracy?
- Explore interactively — Adjust parameters and watch how changes ripple through the system
- Build intuition — Understand why systems behave the way they do, not just what they do
- Zero setup — Run directly in your browser via Google Colab
Example Topics (Planned)
- Memory vs. Compute Tradeoffs — Watch how batch size affects memory footprint and training speed
- Quantization Effects — See accuracy degradation as you reduce precision from FP32 → INT8 → INT4
- Attention Visualization — Explore what transformer attention heads actually learn
- Optimization Landscapes — Navigate loss surfaces with different optimizers
- Pruning Strategies — Compare structured vs. unstructured pruning on real models
Stay Updated
Labs are under active development. To be notified when they launch:
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 🧑💻 🎨 ✍️ |
Recognize a contributor: Comment on any issue or PR:
@all-contributors please add @username for code, tutorial, test, or doc
Read. Explore. Build. (Labs coming soon)