mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-04-30 09:38:38 -05:00
718f867039
- Config: academic, standalone abstracts for PDF/EPUB/copyedit - Chapters: ml_systems, nn_architectures, nn_computation, training
147 lines
5.2 KiB
YAML
147 lines
5.2 KiB
YAML
# =============================================================================
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# VOLUME I EPUB CONFIGURATION
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# =============================================================================
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# Builds only Volume I: Introduction as EPUB
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#
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# Usage:
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# cp config/_quarto-epub-vol1.yml _quarto.yml
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# quarto render --to epub
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# =============================================================================
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project:
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type: book
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output-dir: _build/epub-vol1
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execute-dir: project
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post-render:
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- scripts/clean_svgs.py
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- scripts/epub_postprocess.py
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preview:
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browser: false
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navigate: false
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book:
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favicon: assets/images/icons/favicon.png
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cover-image: assets/images/covers/cover-hardcover-book-vol1.png
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cover-image-alt: "Cover image."
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title: "Introduction to Machine Learning Systems"
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date: today
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date-format: long
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author:
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name: "Prof. Vijay Janapa Reddi"
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email: vj@eecs.harvard.edu
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url: https://www.google.com/search?q=Vijay+Janapa+Reddi
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affiliations:
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- name: "Harvard University"
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department: "School of Engineering and Applied Sciences"
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city: "Cambridge"
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state: "MA"
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country: "USA"
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corresponding: true
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roles: "Author, editor and curator."
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orcid: "0000-0002-5259-7721"
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abstract: |
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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.
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repo-url: https://github.com/harvard-edge/cs249r_book
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page-footer:
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left: |
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Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
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right: |
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Built with <a href="https://quarto.org/">Quarto</a>.
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chapters:
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- index.qmd
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# Volume I Frontmatter
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- contents/vol1/frontmatter/dedication.qmd
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- contents/vol1/frontmatter/foreword.qmd
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- contents/vol1/frontmatter/about.qmd
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- contents/vol1/frontmatter/acknowledgements.qmd
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# Part I: Foundations
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- part: "Foundations"
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- contents/vol1/parts/foundations_principles.qmd
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- contents/vol1/introduction/introduction.qmd
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- contents/vol1/ml_systems/ml_systems.qmd
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- contents/vol1/ml_workflow/ml_workflow.qmd
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- contents/vol1/data_engineering/data_engineering.qmd
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# Part II: Build
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- part: "Build"
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- contents/vol1/parts/build_principles.qmd
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- contents/vol1/nn_computation/nn_computation.qmd
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- contents/vol1/nn_architectures/nn_architectures.qmd
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- contents/vol1/frameworks/frameworks.qmd
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- contents/vol1/training/training.qmd
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# Part III: Optimize
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- part: "Optimize"
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- contents/vol1/parts/optimize_principles.qmd
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- contents/vol1/data_selection/data_selection.qmd
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- contents/vol1/optimizations/model_compression.qmd
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- contents/vol1/hw_acceleration/hw_acceleration.qmd
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- contents/vol1/benchmarking/benchmarking.qmd
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# Part IV: Deploy
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- part: "Deploy"
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- contents/vol1/parts/deploy_principles.qmd
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- contents/vol1/model_serving/model_serving.qmd
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- contents/vol1/ml_ops/ml_ops.qmd
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- contents/vol1/responsible_engr/responsible_engr.qmd
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- contents/vol1/conclusion/conclusion.qmd
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- contents/vol1/backmatter/references.qmd
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- part: "Appendices"
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chapters:
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- contents/vol1/backmatter/appendix_dam.qmd
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- contents/vol1/backmatter/appendix_machine.qmd
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- contents/vol1/backmatter/appendix_algorithm.qmd
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- contents/vol1/backmatter/appendix_data.qmd
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- contents/vol1/backmatter/glossary/glossary.qmd
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bibliography:
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- contents/vol1/backmatter/references.bib
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format:
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epub:
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language: "en-US"
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identifier: "https://mlsysbook.ai/epub/vol1"
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contributor: "Harvard University, School of Engineering and Applied Sciences"
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subject: "Machine Learning, Computer Science, Engineering, Artificial Intelligence, Systems Design"
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description: "Volume I of Machine Learning Systems, covering foundations, development, optimization, and deployment."
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type: "Text"
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format: "application/epub+zip"
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source: "https://mlsysbook.ai"
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toc: true
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toc-depth: 3
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toc-title: "Table of Contents"
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epub-chapter-level: 1
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epub-cover-image: "assets/images/covers/cover_image_title-vol1.png"
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epub-title-page: true
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css: "assets/styles/epub-vol1.css"
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code-overflow: wrap
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code-copy: true
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code-line-numbers: true
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number-sections: false
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number-depth: 3
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fig-align: center
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fig-caption: true
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footnotes-hover: true
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link-citations: true
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citations-hover: true
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metadata-files:
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- config/shared/base/custom-numbered-blocks.yml
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- config/shared/base/execute-env.yml
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- config/shared/base/diagram.yml
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- config/shared/epub/filters.yml
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- config/shared/epub/filter-metadata.yml
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- config/shared/vol1/filter-metadata-paths.yml
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