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97 lines
4.4 KiB
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97 lines
4.4 KiB
Plaintext
---
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title: "For Instructors"
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subtitle: "Reproducible, hardware-independent exercises for ML systems courses."
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---
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MLSYSIM provides a framework for assigning analytically grounded problem sets where every answer is deterministic and reproducible — regardless of what hardware your students have access to.
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---
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## Why MLSYSIM for Teaching?
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| Challenge | How MLSYSIM Helps |
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|:----------|:------------------|
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| Students lack GPU access | All analysis runs on a laptop — no cloud credits needed |
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| Homework answers vary by hardware | Vetted registry specs produce identical results everywhere |
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| Hard to grade open-ended systems questions | Analytical solvers give deterministic, verifiable outputs |
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| Specifications become stale | Registry updated from official datasheets; one update propagates everywhere |
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| Students memorize without understanding | "Predict first" exercises build genuine intuition |
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---
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## Course Integration Patterns
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### Pattern 1: Textbook Companion
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MLSYSIM maps directly to chapters in the [Machine Learning Systems](https://mlsysbook.ai) textbook. Assign tutorials alongside readings:
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| Week | Textbook Chapter | MLSYSIM Assignment |
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|:-----|:-----------------|:-------------------|
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| 3 | Hardware Acceleration | [Hello World](tutorials/hello_world.qmd) — Roofline analysis, batch size sweep |
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| 5 | Model Serving | [LLM Serving](tutorials/llm_serving.qmd) — TTFT/ITL analysis |
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| 7 | Distributed Training | [Distributed Training](tutorials/distributed.qmd) — 3D parallelism |
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| 9 | Sustainable AI | [Sustainability Lab](tutorials/sustainability.qmd) — Carbon footprint |
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| 11 | Compute Infrastructure | [Solver Guide](solver-guide.qmd) — Composing solvers for TCO analysis |
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### Pattern 2: Standalone Labs
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Use individual tutorials as self-contained lab assignments in any systems course. Each tutorial includes exercises with clear expected outputs.
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### Pattern 3: Capstone Projects
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Advanced students can write custom solvers (see [Extending MLSYSIM](solver-guide.qmd#extending-mlsysim)) or compose multiple solvers to answer research-style questions.
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---
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## Assignment Ideas
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### Homework: Hardware Comparison (30 min)
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> Using `Engine.solve()`, compare ResNet-50 inference latency on the A100, H100, and Jetson AGX at batch sizes 1, 32, and 256. For each configuration, state whether the workload is memory-bound or compute-bound and explain why the bottleneck changes.
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### Lab: Carbon-Aware Training (45 min)
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> Using the SustainabilitySolver, calculate the carbon footprint of training GPT-3 on a 256-GPU H100 cluster in Quebec vs. US Average vs. Poland. Produce a table and a 2-paragraph analysis of why location matters.
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### Exam Question: Back-of-Envelope
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> The NVIDIA H100 has 1,979 TFLOP/s (FP16) and 3.35 TB/s bandwidth. What is the ridge point in FLOP/Byte? If a model has arithmetic intensity of 50 FLOP/Byte, is it compute-bound or memory-bound? Show your work.
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---
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## Reproducibility Guarantee
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All specifications in the [MLSys Zoo](zoo/index.qmd) are:
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- **Sourced** from official manufacturer datasheets and published benchmarks
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- **Typed** with `pint.Quantity` for dimensional correctness
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- **Frozen** per release — `mlsysim==0.1.0` always produces the same answers
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This means your answer key works for every student, every semester.
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---
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## Jupyter & Quarto Compatibility
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All tutorials are designed to run in:
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- **Jupyter Notebooks** — Standard `.ipynb` workflow
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- **Quarto documents** — Render to HTML, PDF, or slides with `quarto render`
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- **Google Colab** — `pip install mlsysim` in the first cell, then go
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No GPU runtime required. CPU-only environments work perfectly because MLSYSIM computes from equations, not empirical profiling.
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---
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## Getting Started
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1. Point students to the [Getting Started](getting-started.qmd) guide for installation
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2. Assign the [Hello World](tutorials/hello_world.qmd) tutorial as a warmup
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3. Use the [Solver Guide](solver-guide.qmd) to select solvers for your course topics
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4. Browse the [MLSys Zoo](zoo/index.qmd) for available hardware and model specifications
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---
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## Next Steps
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- **[Solver Guide](solver-guide.qmd)** — Which solver maps to which topic
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- **[Math Foundations](math.qmd)** — All equations, for your own reference and exam prep
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- **[Accuracy & Validation](accuracy.qmd)** — How close are analytical estimates to empirical results?
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- **[Whitepaper](whitepaper.qmd)** — The academic paper describing MLSYSIM's design and pedagogy
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