ca34ba6bc7
Second agent pass matched the LABS_SPEC brief more precisely: - Act I renamed to 'Design Ledger Archaeology' — reads actual ledger history, computes per-domain constraint hit rate, renders radar chart + bar chart - Act II is 'The Final Architecture Challenge' with 6 simultaneous scorecard constraints (accuracy, P99, DP, adversarial, carbon, fault tolerance) - Stakeholder scenario: Chief Architect / Principal Engineer promotion framing - Medical fleet (1000 hospitals, 100k inferences/day) as the deployment target - Curriculum journey timeline grid (all 33 labs) in closing section - All constants match spec: FLEET_SIZE_NODES=1000, COAL_CI_G_KWH=820, etc.
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)