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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, Zachary C. Lipton, Mu Li, and Alexander J. Smola
copyright = 2022, All authors. Licensed under CC-BY-SA-4.0 and MIT-0.
release = 1.0.0-alpha1.post0
[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/ d2l/ d2l.bib setup.py static/latex_style/
# Files that will be skipped.
exclusions = README.md STYLE_GUIDE.md INFO.md CODE_OF_CONDUCT.md CONTRIBUTING.md contrib/*md
# If True (default), then will evaluate the notebook to obtain outputs.
eval_notebook = True
tabs = pytorch, mxnet, tensorflow
[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,
# Links within hashes are replaced dynamically in static/build_html.sh
header_links = ###_ALTERNATE_VERSION_###, ###_ALTERNATE_VERSION_BASE_LINK_###, fas fa-book,
PyTorch, ###_CURRENT_VERSION_BASE_LINK_###/d2l-en.pdf, fas fa-file-pdf,
MXNet, ###_CURRENT_VERSION_BASE_LINK_###/d2l-en-mxnet.pdf, fas fa-file-pdf,
Notebooks, https://d2l.ai/d2l-en.zip, fab fa-python,
Courses, https://courses.d2l.ai, fas fa-user-graduate,
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
html_logo = static/logo-with-text.png
include_css = static/d2l.css
[pdf]
# The file used to post-process the generated tex file.
post_latex = ./static/post_latex/main.py
latex_url = https://d2l-webdata.s3.us-west-2.amazonaws.com/latex-styles/PT1.zip
latex_logo = static/logo.png
main_font = Source Serif Pro
sans_font = Source Sans Pro
mono_font = Inconsolata
bibfile = d2l.bib
style = cambridge
[library]
version_file = d2l/__init__.py
[library-mxnet]
lib_file = d2l/mxnet.py
lib_name = np
# Map from d2l.xx to np.xx
simple_alias = ones_like, ones, zeros_like, zeros, arange, meshgrid, sin, sinh, cos, cosh, tanh,
linspace, exp, log, tensor -> array, normal -> random.normal,
randn -> random.randn, expand_dims
rand -> random.rand, matmul -> dot, int32, int64, float32,
concat -> concatenate, stack, abs, eye
# Map from d2l.xx(a, *args, **kwargs) to a.xx(*args, **kwargs)
fluent_alias = numpy -> asnumpy, reshape, to -> as_in_context, reduce_sum -> sum,
argmax, astype, reduce_mean -> mean, swapaxes, repeat
alias =
size = lambda a: a.size
transpose = lambda a: a.T
nn_Module = nn.Block
sigmoid = npx.sigmoid
batch_matmul = npx.batch_dot
reverse_alias =
d2l.size\(([\w\_\d]+)\) -> \1.size
d2l.transpose\(([\w\_\d]+)\) -> \1.T
d2l.nn_Module -> nn.Block
d2l.sigmoid -> npx.sigmoid
d2l.batch_matmul -> npx.batch_dot
[library-pytorch]
lib_file = d2l/torch.py
lib_name = torch
simple_alias = ones_like, ones, zeros_like, zeros, tensor, arange, meshgrid, sin, sinh, cos, cosh,
tanh, linspace, exp(, log, normal, rand, randn, matmul, int32, int64, float32,
concat -> cat, stack, abs, eye, sigmoid, batch_matmul -> bmm
fluent_alias = numpy -> detach().numpy, size -> numel, reshape, to,
reduce_sum -> sum, argmax, astype -> type, transpose -> t,
reduce_mean -> mean, expand_dims -> unsqueeze, swapaxes, repeat
alias =
nn_Module = nn.Module
reverse_alias =
d2l.nn_Module -> nn.Module
[library-tensorflow]
lib_file = d2l/tensorflow.py
lib_name = tf
simple_alias = reshape, ones_like, ones, zeros_like, zeros, meshgrid, sin, sinh, cos, cosh, tanh,
linspace, exp, normal -> random.normal, rand -> random.uniform,
matmul, reduce_sum, reduce_mean, argmax, tensor -> constant,
arange -> range, astype -> cast, int32, int64, float32, transpose,
concat, stack, abs, eye, log -> math.log, sigmoid, expand_dims, repeat,
batch_matmul -> matmul
fluent_alias = numpy,
alias =
size = lambda a: tf.size(a).numpy()
reverse_alias =
d2l.size\(([\w\_\d]+)\) -> tf.size(\1).numpy()
d2l.nn_Module -> tf.keras.Model
[deploy]
other_file_s3urls = s3://d2l-webdata/releases/d2l-en/d2l-en-0.7.0.zip
s3://d2l-webdata/releases/d2l-en/d2l-en-0.7.1.zip
google_analytics_tracking_id = UA-96378503-10
[colab]
github_repo = mxnet, d2l-ai/d2l-en-colab
pytorch, d2l-ai/d2l-pytorch-colab
tensorflow, d2l-ai/d2l-tensorflow-colab
replace_svg_url = img, http://d2l.ai/_images
libs = mxnet, mxnet, -U mxnet-cu101==1.7.0
mxnet, d2l, d2l==RELEASE
pytorch, d2l, d2l==RELEASE
tensorflow, d2l, d2l==RELEASE
[sagemaker]
github_repo = mxnet, d2l-ai/d2l-en-sagemaker
pytorch, d2l-ai/d2l-pytorch-sagemaker
tensorflow, d2l-ai/d2l-tensorflow-sagemaker
kernel = mxnet, conda_mxnet_p36
pytorch, conda_pytorch_p36
tensorflow, conda_tensorflow_p36
libs = mxnet, mxnet, -U mxnet-cu101==1.7.0
mxnet, d2l, .. # installing d2l
pytorch, d2l, .. # installing d2l
tensorflow, d2l, .. # installing d2l
[slides]
top_right = <img height=80px src='http://d2l.ai/_static/logo-with-text.png'/>
github_repo = pytorch, d2l-ai/d2l-pytorch-slides