- 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)
- Adds standardized callout-definition blocks with bold term + clear definition
to all Vol.2 chapters (distributed training, inference, network fabrics, etc.)
- Fixes caption_inline_python errors: replaces Python inline refs in table
captions with static text in responsible_engr, appendix_fleet, appendix_reliability,
compute_infrastructure
- Fixes undefined_inline_ref errors: adds missing code fence for PlatformEconomics
class in ops_scale.qmd; converts display math blocks with Python refs to prose
- Fixes render-pattern errors: moves inline Python outside $...$ math delimiters
in conclusion, fleet_orchestration, inference, introduction, network_fabrics,
responsible_ai, security_privacy, sustainable_ai, distributed_training
- Fixes dropcap errors: restructures drop-cap sentences in hw_acceleration and
nn_architectures to not start with cross-references
- Fixes unreferenced-label errors: removes @ prefix from @sec-/@tbl- refs inside
Python comment strings in training, model_compression, ml_systems
- Adds clientA to codespell ignore words (TikZ node label in edge_intelligence)
- Updates mlsys constants, hardware, models, and test_units for Vol.2 calculations
- Updates _quarto.yml and references.bib for two-volume structure
This commit refactors the underlying Python calculation cells for Chapters 1-16
to strictly enforce mathematical consistency with the narrative.
**Key Text/Numeric Updates (For Editorial Review):**
1. **Chapter 3 (Workflow) - Edge Necessity Scenario:**
- *Change:* Increased clinic patient count from **100** to **150**.
- *Reason:* With 100 patients, the calculated upload time was ~5.5 hours, which fits within the 8-hour clinic day, contradicting the chapter's conclusion that 'Edge is Mandatory.' Increasing to 150 pushes upload time to >8 hours, mathematically validating the narrative.
2. **Chapter 1 (Introduction) - Model Drift Scenario:**
- *Change:* Reduced monthly accuracy drift rate from **8.0%** to **0.8%**.
- *Reason:* An 8% monthly drop is a catastrophic failure that would be immediately noticed. A 0.8% drop correctly models the 'silent failure' (boiling frog) scenario described in the text.
3. **Chapter 3 (Workflow) - Velocity vs Quality:**
- *Change:* Reduced 'Large Model' accuracy gain per iteration from **0.5%** to **0.15%**.
- *Reason:* The original rate caused the large model to hit 99% accuracy almost instantly, invalidating the 'Velocity is a Feature' argument. The new rate correctly models diminishing returns, allowing the faster (small) model to win.
4. **Chapter 15 (Responsible Engineering) - TCO Analysis:**
- *Verification:* Verified and stabilized the 3-year Total Cost of Ownership (TCO) calculations. Confirmed that Inference TCO (.5M) dominates Training TCO (8K) by ~40x, supporting the 'Efficiency as Responsibility' thesis.
**Technical Changes (Code Only):**
- Refactored all calculation cells to use the **P.I.C.O. (Parameters, Invariants, Calculation, Outputs)** design pattern.
- Added assertion guards (Invariants) to prevent future regressions where math contradicts prose.
- Fixed variable scope issues in Chapter 10 (Model Compression) and Chapter 15.
- Disabled false-positive linter warnings for standard LaTeX spacing.
- Fixes fig-amortization-comparison: scales Y-axis values from 12000 to 12
to avoid LaTeX dimension limit (~16383pt)
- Fixes fig-compute-optimal-frontier: replaces problematic \fill...plot
with proper \addplot[fill=...] \closedcycle for log-scale coordinates
- Updates figure reference text to use @fig-selection-inequality
- Adds ch_data_selection.py calculation module
- Updates viz.py with new plot functions
- Various chapter updates across vol1 and vol2
This commit includes:
- Bibliography reformatting across all Volume I chapters
- Updated cross-references in Vol II chapters
- Added 'fpr' to codespell ignore list
- Updated symlink to point to vol1 PDF config
Changes span both volumes as part of ongoing volume restructure work.
Add README translations in Chinese (zh), Japanese (ja), and Korean (ko) with language switcher links.
Changes made by maintainer:
- Standardized file names to ISO 639-1 codes
- Fixed year target (2026 → 2030) to match main README
- Added language switcher to all READMEs
ROUGE is a legitimate ML evaluation metric (Recall-Oriented Understudy
for Gisting Evaluation) used in text summarization benchmarks.
FPR is False Positive Rate, a standard ML classification metric.
