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config.ini
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config.ini
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[project]
# The project name, used as the filename of the package and the PDF file. For
# example, if set to d2l-book, then will build d2l-book.zip and d2l-book.pdf
name = d2l-en
# Book title. It will be displayed on the top-right of the HTML page and the
# front page of the PDF file
title = Dive into Deep Learning
author = Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola
copyright = 2019, All authors. Licensed under the CC BY-NC-SA
release = 0.7
[html]
# A list of links that is displayed on the navbar. A link consists of three
# items: name, URL, and a fontawesome icon
# (https://fontawesome.com/icons?d=gallery). Items are separated by commas.
# PDF, http://numpy.d2l.ai/d2l-en.pdf, fas fa-file-pdf,
header_links = Courses, https://courses.d2l.ai, fas fa-user-graduate,
PDF, http://numpy.d2l.ai/d2l-en.pdf, fas fa-file-pdf,
All Notebooks, http://numpy.d2l.ai/d2l-en.zip, fas fa-download,
Discuss, https://discuss.mxnet.io, fab fa-discourse,
GitHub, https://github.com/d2l-ai/d2l-en, fab fa-github,
中文版, https://zh.d2l.ai, fas fa-external-link-alt
favicon = static/favicon.png
[build]
# A list of wildcards to indicate the markdown files that need to be evaluated as
# Jupyter notebooks.
notebooks = *.md */*.md
# A list of files that will be copied to the build folder.
resources = img/ data/ d2l/ d2l.bib environment.yml setup.py
# Files that will be skipped.
exclusions = README.md STYLE_GUIDE.md
# If True (default), then will evaluate the notebook to obtain outputs.
eval_notebook = True
# If True, the mark the build as failed for any warning. Default is False.
warning_is_error = False
# A list of files, if anyone is modified after the last build, will re-build all
# documents.
dependencies = Jenkinsfile
[library]
# Where code blocks will save to
save_filename = d2l/d2l.py
# The parttern to mark this block will be saved.
save_mark = Save to the d2l package
[deploy]
s3_bucket = s3://numpy.d2l.ai
google_analytics_tracking_id = UA-96378503-18