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Vol1: improve book abstracts and chapter content

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- Config: academic, standalone abstracts for PDF/EPUB/copyedit
- Chapters: ml_systems, nn_architectures, nn_computation, training
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Vijay Janapa Reddi
2026-02-21 06:58:22 -05:00
parent 09602445de
commit 718f867039
8 changed files with 53 additions and 38 deletions
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4
book/quarto/config/_quarto-epub-vol1.yml
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@@ -45,7 +45,7 @@ book:
orcid: "0000-0002-5259-7721"
abstract: |
Volume I of Machine Learning Systems presents a comprehensive introduction to understanding and engineering machine learning systems, with a focus on mastering the machine learning node. This volume establishes the foundations through four progressive stages: building conceptual models, engineering complete workflows, optimizing for real-world constraints, and deploying with confidence.
Foundations of machine learning systems engineering for single-machine deployment. ML systems are treated as infrastructure governed by physical constraints: data movement, memory bandwidth, and compute limits shape design decisions from model architecture to deployment target. The treatment progresses through conceptual models, end-to-end workflows, optimization under operational constraints, and production deployment. Quantitative reasoning and enduring principles are emphasized over transient tools. Suitable for undergraduate and graduate courses in computer science and engineering, and for practitioners designing flexible, efficient, and robust ML systems.
repo-url: https://github.com/harvard-edge/cs249r_book
@@ -53,7 +53,7 @@ book:
left: |
Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
right: |
This book was built with <a href="https://quarto.org/">Quarto</a>.
Built with <a href="https://quarto.org/">Quarto</a>.
chapters:
- index.qmd

2
book/quarto/config/_quarto-html-vol1.yml
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@@ -70,7 +70,7 @@ website:
text: "Volume I: Introduction"
href: ./
- icon: journal
text: "Volume II: Advanced"
text: "Volume II: At Scale"
href: ../vol2/
- text: "---"
- icon: fire

4
book/quarto/config/_quarto-pdf-vol1-copyedit.yml
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@@ -35,7 +35,7 @@ book:
roles: "Author, editor and curator."
abstract: |
Volume I of Machine Learning Systems presents a comprehensive introduction to understanding and engineering machine learning systems, with a focus on mastering the machine learning node. This volume establishes the foundations through four progressive stages: building conceptual models, engineering complete workflows, optimizing for real-world constraints, and deploying with confidence. The content bridges theoretical foundations and practical engineering, emphasizing the systems context that engineers need to master when building AI solutions. While ML applications and tools evolve rapidly, the engineering principles for building ML systems remain largely consistent. This volume distills these enduring concepts for anyone seeking to build flexible, efficient, and robust ML systems.
Foundations of machine learning systems engineering for single-machine deployment. ML systems are treated as infrastructure governed by physical constraints: data movement, memory bandwidth, and compute limits shape design decisions from model architecture to deployment target. The treatment progresses through conceptual models, end-to-end workflows, optimization under operational constraints, and production deployment. Quantitative reasoning and enduring principles are emphasized over transient tools. Suitable for undergraduate and graduate courses in computer science and engineering, and for practitioners designing flexible, efficient, and robust ML systems.
repo-url: https://github.com/harvard-edge/cs249r_book
@@ -43,7 +43,7 @@ book:
left: |
Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
right: |
This book was built with <a href="https://quarto.org/">Quarto</a>.
Built with <a href="https://quarto.org/">Quarto</a>.
chapters:
- index.qmd

64
book/quarto/config/_quarto-pdf-vol1.yml
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@@ -34,7 +34,7 @@ book:
roles: "Author, editor and curator."
abstract: |
This book presents a comprehensive introduction to understanding and engineering machine learning systems, with a focus on mastering the machine learning node. It establishes the foundations through four progressive stages: building conceptual models, engineering complete workflows, optimizing for real-world constraints, and deploying with confidence. The content bridges theoretical foundations and practical engineering, emphasizing the systems context that engineers need to master when building AI solutions. While ML applications and tools evolve rapidly, the engineering principles for building ML systems remain largely consistent. This book distills these enduring concepts for anyone seeking to build flexible, efficient, and robust ML systems.
Foundations of machine learning systems engineering for single-machine deployment. ML systems are treated as infrastructure governed by physical constraints: data movement, memory bandwidth, and compute limits shape design decisions from model architecture to deployment target. The treatment progresses through conceptual models, end-to-end workflows, optimization under operational constraints, and production deployment. Quantitative reasoning and enduring principles are emphasized over transient tools. Suitable for undergraduate and graduate courses in computer science and engineering, and for practitioners designing flexible, efficient, and robust ML systems.
repo-url: https://github.com/harvard-edge/cs249r_book
@@ -42,7 +42,7 @@ book:
left: |
Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
right: |
This book was built with <a href="https://quarto.org/">Quarto</a>.
Built with <a href="https://quarto.org/">Quarto</a>.
chapters:
- index.qmd
@@ -50,58 +50,58 @@ book:
# ==================================================
# Volume I Frontmatter
# ==================================================
- contents/vol1/frontmatter/dedication.qmd
- contents/vol1/frontmatter/foreword.qmd
- contents/vol1/frontmatter/about.qmd
- contents/vol1/frontmatter/acknowledgements.qmd
- contents/vol1/frontmatter/notation.qmd
# - contents/vol1/frontmatter/dedication.qmd
# - contents/vol1/frontmatter/foreword.qmd
# - contents/vol1/frontmatter/about.qmd
# - contents/vol1/frontmatter/acknowledgements.qmd
# - contents/vol1/frontmatter/notation.qmd
# ==================================================
# Part I: Foundations
# ==================================================
- contents/vol1/parts/foundations_principles.qmd
- contents/vol1/introduction/introduction.qmd
- contents/vol1/ml_systems/ml_systems.qmd
- contents/vol1/ml_workflow/ml_workflow.qmd
- contents/vol1/data_engineering/data_engineering.qmd
# - contents/vol1/parts/foundations_principles.qmd
# - contents/vol1/introduction/introduction.qmd
# - contents/vol1/ml_systems/ml_systems.qmd
# - contents/vol1/ml_workflow/ml_workflow.qmd
# - contents/vol1/data_engineering/data_engineering.qmd
# ==================================================
# Part II: Build
# ==================================================
- contents/vol1/parts/build_principles.qmd
- contents/vol1/nn_computation/nn_computation.qmd
- contents/vol1/nn_architectures/nn_architectures.qmd
- contents/vol1/frameworks/frameworks.qmd
- contents/vol1/training/training.qmd
# - contents/vol1/parts/build_principles.qmd
# - contents/vol1/nn_computation/nn_computation.qmd
# - contents/vol1/nn_architectures/nn_architectures.qmd
# - contents/vol1/frameworks/frameworks.qmd
# - contents/vol1/training/training.qmd
# ==================================================
# Part III: Optimize
# ==================================================
- contents/vol1/parts/optimize_principles.qmd
- contents/vol1/data_selection/data_selection.qmd
- contents/vol1/optimizations/model_compression.qmd
- contents/vol1/hw_acceleration/hw_acceleration.qmd
# - contents/vol1/parts/optimize_principles.qmd
# - contents/vol1/data_selection/data_selection.qmd
# - contents/vol1/optimizations/model_compression.qmd
# - contents/vol1/hw_acceleration/hw_acceleration.qmd
- contents/vol1/benchmarking/benchmarking.qmd
# ==================================================
# Part IV: Deploy
# ==================================================
- contents/vol1/parts/deploy_principles.qmd
- contents/vol1/model_serving/model_serving.qmd
- contents/vol1/ml_ops/ml_ops.qmd
- contents/vol1/responsible_engr/responsible_engr.qmd
- contents/vol1/conclusion/conclusion.qmd
- contents/vol1/backmatter/references.qmd
# - contents/vol1/parts/deploy_principles.qmd
# - contents/vol1/model_serving/model_serving.qmd
# - contents/vol1/ml_ops/ml_ops.qmd
# - contents/vol1/responsible_engr/responsible_engr.qmd
# - contents/vol1/conclusion/conclusion.qmd
# - contents/vol1/backmatter/references.qmd
# ==================================================
# Appendices (uses Appendix A, B, C... numbering)
# ==================================================
# appendices:
- contents/vol1/backmatter/appendix_dam.qmd
- contents/vol1/backmatter/appendix_machine.qmd
- contents/vol1/backmatter/appendix_algorithm.qmd
- contents/vol1/backmatter/appendix_data.qmd
- contents/vol1/backmatter/glossary/glossary.qmd
# - contents/vol1/backmatter/appendix_dam.qmd
# - contents/vol1/backmatter/appendix_machine.qmd
# - contents/vol1/backmatter/appendix_algorithm.qmd
# - contents/vol1/backmatter/appendix_data.qmd
# - contents/vol1/backmatter/glossary/glossary.qmd
bibliography:
- contents/vol1/backmatter/references.bib

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book/quarto/contents/vol1/ml_systems/ml_systems.qmd
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@@ -705,7 +705,7 @@ These archetypes map naturally to deployment paradigms: **Compute Beasts** and *
# └─────────────────────────────────────────────────────────────────────────────
from mlsys import Models
from mlsys.constants import (
RESNET50_FLOPs, GFLOPs, Mparam, Bparam, byte, MB, GB
RESNET50_FLOPs, GFLOPs, Mparam, Bparam, Kparam, byte, MB, GB, KB
)
from mlsys.formatting import fmt, check

3
book/quarto/contents/vol1/nn_architectures/nn_architectures.qmd
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@@ -297,6 +297,9 @@ class LighthouseSpecs:
a100_mem_str = fmt(a100_mem, precision=0)
# Transformer quadratic scaling: doubling sequence length quadruples attention memory
transformer_scaling_ratio_str = "4"
# Note: No exports. Use LighthouseSpecs.variable directly.
```

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book/quarto/contents/vol1/nn_computation/nn_computation.qmd
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@@ -66,6 +66,10 @@ Neural networks reduce to a small set of mathematical operations. Matrix multipl
from mlsys.constants import *
from mlsys.formatting import fmt, sci
from mlsys.formulas import model_memory
# MNIST 784→128→64→10 MAC count (used in Purpose / From Logic to Arithmetic)
_inf_madd = 784 * 128 + 128 * 64 + 64 * 10 # 109,184
inf_madd_total_str = f"{_inf_madd:,}"
```
## From Logic to Arithmetic {#sec-neural-computation-deep-learning-systems-engineering-foundation-597f}

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book/quarto/contents/vol1/training/training.qmd
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@@ -17,6 +17,14 @@ engine: jupyter
:::
```{python}
#| label: purpose-anchor
#| echo: false
# Early anchor for Purpose section inline refs (training-setup runs later)
gpt2_train_cost_str = "$50,000"
gpt2_inf_cost_str = "$0.0001"
```
## Purpose {.unnumbered}
\begin{marginfigure}