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Complete MLSYSIM v0.1.0 implementation with: - Documentation website (Quarto): landing page with animated hero and capability carousel, 4 tutorials (hello world, LLM serving, distributed training, sustainability), hardware/model/fleet/infra catalogs, solver guide, whitepaper, math foundations, glossary, and full quartodoc API reference - Typed registry system: Hardware (18 devices across 5 tiers), Models (15 workloads), Systems (fleets, clusters, fabrics), Infrastructure (grid profiles, rack configs, datacenters) - Core types: Pint-backed Quantity, Metadata provenance tracking, custom exception hierarchy (OOMError, SLAViolation) - SimulationConfig with YAML/JSON loading and pre-validation - Scenario system tying workloads to systems with SLA constraints - Multi-level evaluation scorecard (feasibility, performance, macro) - Examples, tests, and Jetson Orin NX spec fix (100 → 25 TFLOP/s) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
76 lines
2.5 KiB
Plaintext
76 lines
2.5 KiB
Plaintext
---
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title: "Tutorials"
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subtitle: "Step-by-step guides for modeling ML Systems."
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format:
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html:
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toc: false
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---
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These tutorials are designed to build intuition for ML systems using the `mlsysim` framework.
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They map directly to chapters in the *Machine Learning Systems* textbook—start at the beginning
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or jump to any topic.
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::: {.tutorial-grid}
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::: {.tutorial-card}
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[Beginner]{.tutorial-level .level-beginner}
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### Hello World: Single-Node Roofline
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Learn to lower a model onto hardware and identify the performance bottleneck.
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Understand memory-bound vs. compute-bound in 5 minutes.
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[Start Tutorial →](hello_world.qmd){.tutorial-arrow}
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:::
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::: {.tutorial-card}
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[Intermediate]{.tutorial-level .level-intermediate}
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### Sustainability Lab: Carbon Footprint
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Calculate the energy and CO₂ cost of training a frontier LLM across different
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geographical grid regions. Quebec vs. Poland—the numbers will surprise you.
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[Start Tutorial →](sustainability.qmd){.tutorial-arrow}
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:::
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::: {.tutorial-card}
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[Intermediate]{.tutorial-level .level-intermediate}
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### LLM Serving: TTFT, ITL & the Memory Wall
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Model the two physical regimes of autoregressive generation: the compute-bound
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pre-fill phase and the memory-bound decoding phase. Discover how quantization
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and hardware choice affect each phase differently.
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[Start Tutorial →](llm_serving.qmd){.tutorial-arrow}
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:::
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::: {.tutorial-card}
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[Advanced]{.tutorial-level .level-advanced}
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### Distributed Training: 3D Parallelism
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Explore Data, Tensor, and Pipeline parallelism overhead. Model the ring all-reduce
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communication cost and pipeline bubble fraction on a 256-GPU H100 cluster.
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[Start Tutorial →](distributed.qmd){.tutorial-arrow}
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:::
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:::
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---
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## Learning Path
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If you're new to ML systems modeling, we recommend this sequence:
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1. **[Hello World](hello_world.qmd)** — Understand the roofline model and what determines inference speed.
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2. **[Sustainability Lab](sustainability.qmd)** — Apply the framework to a real-world carbon analysis.
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3. **[LLM Serving Lab](llm_serving.qmd)** — Model TTFT, ITL, and KV-cache pressure for production LLM serving.
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4. **[Distributed Training](distributed.qmd)** — Scale to hundreds of GPUs and analyze where efficiency is lost.
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5. **[Hardware Zoo](../zoo/hardware.qmd)** — Explore the vetted hardware specifications across deployment tiers.
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6. *(Optional)* **[Math Foundations](../math.qmd)** — The first-principles equations behind every solver.
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> **Tip:** All tutorials are Jupyter/Quarto compatible. Run them locally after `pip install mlsysim`.
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