Add fallback hook dependencies in validate-dev and apply trailing-whitespace fixes to lab plan files so pre-commit no longer fails on auto-modifications.
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.
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
Complete rewrite of lab_00_introduction.py with four sections:
1. The 95% Problem — ML systems vs ML framing (not models, infrastructure)
2. Physical Constraints — speed of light, thermodynamics, memory physics
3. Four Deployment Regimes — Cloud/Edge/Mobile/TinyML constraint walls
4. Interface Orientation — live cockpit tour (tabs, levers, prediction lock, MathPeek)
Each concept block gates the next via mo.stop() with structured checks:
- Check 1: radio MCQ (silent degradation / ML systems domain)
- Check 2: multiselect (AV latency / speed-of-light constraint)
- Check 3: radio scenario (ICU sensor / TinyML constraint analysis)
Interface tour uses real mo.ui components (dropdown, slider, accordion)
so students build motor memory for the cockpit before Lab 01 content begins.
Design Ledger initialized at completion with deployment context + check answers.
Fix: DesignLedger import corrected to labs.core.state (not mlsysim.sim.ledger).
Verified: Exit 0 under Python 3.13 with marimo 0.19.6.
- book/quarto/mlsys/__init__.py: add repo-root sys.path injection so
mlsysim is importable when scripts run from book/quarto/ context
- book/quarto/mlsys/{constants,formulas,formatting,hardware}.py: new
compatibility shims that re-export from mlsysim.core.* and mlsysim.fmt
- mlsysim/viz/__init__.py: remove try/except for dashboard import; use
explicit "import from mlsysim.viz.dashboard" pattern instead
- .codespell-ignore-words.txt: add "covert" (legitimate security term)
- book/tools/scripts/reference_check_log.txt: delete generated artifact
- Various QMD, bib, md files: auto-formatted by pre-commit hooks
(trailing whitespace, bibtex-tidy, pipe table alignment)
Transforms the initial lab from a static manifesto into an interactive orientation. Introduces three Knowledge Assessment Tasks (KATs) designed to calibrate architectural intuition:
- KAT 1: Explores the cost implications of discovering design constraints late.
- KAT 2: Illustrates non-linear physical scaling laws through processor power consumption.
- KAT 3: Guides users to select a career specialization, defining their long-term mission and binding physical constraints.
Integrates new interactive components and a persistent Design Ledger HUD to enhance user engagement and track progress through the curriculum.
Establishes the foundational content for a structured ML engineering curriculum, covering topics from single-node physics to fleet-scale orchestration.
Adds detailed mission plans for 16 labs in Volume 1 and 17 labs in Volume 2. Each plan outlines chapter context, core invariants, narrative arcs, 3-part missions with objectives, interactive workbenches, and reflection questions to define comprehensive learning experiences.
Refines book abstracts, table of contents, and diagram configurations for improved clarity and structure.
This commit enhances the descriptions of both Volume I and Volume II, emphasizing their respective focuses. It also introduces a framework decision tree to guide the selection of parallel training strategies and inference frameworks, and diagrams for visualizing hardware constraints.
Set up separate contributor tracking for each sub-project:
- book/.all-contributorsrc - Book content contributors
- kits/.all-contributorsrc - Hardware kit contributors
- labs/.all-contributorsrc - Lab exercise contributors
- tinytorch/.all-contributorsrc - Framework contributors
Each project now has:
- Its own .all-contributorsrc config file
- Contributors section in README with All Contributors format
- Project-specific contribution types in the recognition guide
- Cheatsheet in CONTRIBUTING.md (where applicable)
Added @AmirAlasady as first TinyTorch contributor for bug report #1122.
Usage: Comment on any issue/PR with:
@all-contributors please add @username for bug, code, doc, or ideas
- Book: New Year greeting + navbar, TinyTorch, Kits, Newsletter
- Kits: New Year greeting + navbar, Kits intro, Textbook link
- Labs: New Year greeting + navbar, Labs coming 2026, Textbook link
- Rename collabs/ directory to labs/
- Rename workflow files: collabs-publish-*.yml → labs-publish-*.yml
- Update all paths in workflows, Quarto configs, and navbars
- Update cross-references in book, kits, and dev-landing
- Update site-url to mlsysbook.ai/labs/
Note: GitHub repo variable DEV_COLLABS_PATH needs to be renamed
to DEV_LABS_PATH in repository settings.
