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105 lines
4.7 KiB
Markdown
105 lines
4.7 KiB
Markdown
# Labs
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**Understanding the Interplay Between Algorithms and Systems**
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> **Status:** Coming Summer 2026
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---
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## What Are Labs?
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Labs are hands-on interactive notebooks that bridge the gap between **reading about ML systems** (the textbook) and **building them from scratch** (TinyTorch).
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```
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┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
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│ │ │ │ │ │
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│ Textbook │────▶│ Labs │────▶│ TinyTorch │
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│ │ │ │ │ │
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│ Concepts & │ │ Experiment & │ │ Build from │
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│ Theory │ │ Explore │ │ Scratch │
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│ │ │ │ │ │
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└─────────────────┘ └─────────────────┘ └─────────────────┘
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READ EXPLORE BUILD
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```
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## The Learning Journey
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| Phase | Resource | What You Do |
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|-------|----------|-------------|
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| **Understand** | [Textbook](https://mlsysbook.ai) | Learn concepts, theory, and system design principles |
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| **Experiment** | Labs | Explore tradeoffs, tweak parameters, see how decisions ripple through systems |
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| **Build** | [TinyTorch](https://mlsysbook.ai/tinytorch) | Implement everything from scratch, own every line of code |
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## Why Labs?
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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.
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- **See the tradeoffs** — How does batch size affect memory? How does quantization affect accuracy?
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- **Explore interactively** — Adjust parameters and watch how changes ripple through the system
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- **Build intuition** — Understand *why* systems behave the way they do, not just *what* they do
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- **Zero setup** — Run directly in your browser via Google Colab
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## Example Topics (Planned)
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- **Memory vs. Compute Tradeoffs** — Watch how batch size affects memory footprint and training speed
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- **Quantization Effects** — See accuracy degradation as you reduce precision from FP32 → INT8 → INT4
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- **Attention Visualization** — Explore what transformer attention heads actually learn
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- **Optimization Landscapes** — Navigate loss surfaces with different optimizers
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- **Pruning Strategies** — Compare structured vs. unstructured pruning on real models
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## Stay Updated
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Labs are under active development. To be notified when they launch:
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- [Subscribe to updates](https://buttondown.email/mlsysbook)
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- [Star the repo](https://github.com/harvard-edge/cs249r_book)
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- [Join discussions](https://github.com/harvard-edge/cs249r_book/discussions)
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---
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## Related Resources
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| Resource | Description |
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|----------|-------------|
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| [Textbook](https://mlsysbook.ai) | ML Systems principles and practices |
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| [TinyTorch](https://mlsysbook.ai/tinytorch) | Build your own ML framework from scratch |
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| [Discussions](https://github.com/harvard-edge/cs249r_book/discussions) | Ask questions, share feedback |
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---
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## Contributors
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Thanks to these wonderful people who helped build the labs!
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**Legend:** 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder
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<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
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<!-- prettier-ignore-start -->
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<!-- markdownlint-disable -->
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<table>
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<tbody>
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<tr>
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<td align="center" valign="top" width="14.28%"><a href="https://github.com/profvjreddi"><img src="https://avatars.githubusercontent.com/profvjreddi?v=4?s=80" width="80px;" alt="Vijay Janapa Reddi"/><br /><sub><b>Vijay Janapa Reddi</b></sub></a><br />🧑💻 🎨 ✍️</td>
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<td align="center" valign="top" width="14.28%"><a href="https://github.com/salmanmkc"><img src="https://avatars.githubusercontent.com/u/32169182?v=4?v=4?s=80" width="80px;" alt="Salman Chishti"/><br /><sub><b>Salman Chishti</b></sub></a><br />🧑💻</td>
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</tr>
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</tbody>
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</table>
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<!-- markdownlint-restore -->
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<!-- prettier-ignore-end -->
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<!-- ALL-CONTRIBUTORS-LIST:END -->
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**Recognize a contributor:** Comment on any issue or PR:
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```
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@all-contributors please add @username for code, tutorial, test, or doc
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```
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---
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<div align="center">
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**Read. Explore. Build.** *(Labs coming soon)*
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</div>
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