forked from d2l-ai/d2l-zh
-
Notifications
You must be signed in to change notification settings - Fork 0
/
config.ini
256 lines (186 loc) · 8.34 KB
/
config.ini
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
[project]
name = d2l-zh
title = 动手学深度学习
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 = 2.0.0
lang = zh
[translation]
origin_repo = d2l-ai/d2l-en
origin_lang = en
translator = aws
[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/ d2lzh/ d2l.bib setup.py
# Files that will be skipped.
exclusions = */*_origin.md 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 = mxnet, pytorch, tensorflow, paddle
sphinx_configs = numfig_format = {'figure': '图%%s', 'table': '表%%s', 'code-block': '列表%%s', 'section': '%%s节'}
latex_elements = {
'utf8extra' : '',
'inputenc' : '',
'babel' : r'''\usepackage[english]{babel}''',
'preamble' : r'''
\usepackage{ctex}
\setmainfont{Source Serif Pro}
\setsansfont{Source Sans Pro}
\setmonofont{Inconsolata}
\setCJKmainfont[BoldFont=Source Han Serif SC SemiBold]{Source Han Serif SC}
\setCJKsansfont[BoldFont=Source Han Sans SC Medium]{Source Han Sans SC Normal}
\setCJKmonofont{Source Han Sans SC Normal}
\addto\captionsenglish{\renewcommand{\chaptername}{}}
\addto\captionsenglish{\renewcommand{\contentsname}{目录}}
\setlength{\headheight}{13.6pt}
\makeatletter
\fancypagestyle{normal}{
\fancyhf{}
\fancyfoot[LE,RO]{{\py@HeaderFamily\thepage}}
\fancyfoot[LO]{{\py@HeaderFamily\nouppercase{\rightmark}}}
\fancyfoot[RE]{{\py@HeaderFamily\nouppercase{\leftmark}}}
\fancyhead[LE,RO]{{\py@HeaderFamily }}
}
\makeatother
\CJKsetecglue{}
\usepackage{zhnumber}
\definecolor{d2lbookOutputCellBackgroundColor}{RGB}{255,255,255}
\definecolor{d2lbookOutputCellBorderColor}{rgb}{.85,.85,.85}
\def\diilbookstyleoutputcell
{\sphinxcolorlet{VerbatimColor}{d2lbookOutputCellBackgroundColor}
\sphinxcolorlet{VerbatimBorderColor}{d2lbookOutputCellBorderColor}
\sphinxsetup{verbatimwithframe,verbatimborder=0.5pt}
}
\definecolor{d2lbookInputCellBackgroundColor}{rgb}{.95,.95,.95}
\def\diilbookstyleinputcell
{\sphinxcolorlet{VerbatimColor}{d2lbookInputCellBackgroundColor}
\sphinxsetup{verbatimwithframe=false,verbatimborder=0pt}
}
''',
'sphinxsetup': '''verbatimsep=2mm,
VerbatimColor={rgb}{.95,.95,.95},
VerbatimBorderColor={rgb}{.95,.95,.95},
pre_border-radius=3pt,
''',
# The font size ('10pt', '11pt' or '12pt').
'pointsize': '10pt',
# Latex figure (float) alignment
'figure_align': 'H',
'fncychap': '\\usepackage[Sonny]{fncychap}',
}
[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 = MXNet, https://zh-v2.d2l.ai/d2l-zh.pdf, fas fa-file-pdf,
PyTorch, https://zh-v2.d2l.ai/d2l-zh-pytorch.pdf, fas fa-file-pdf,
Jupyter 记事本, https://zh-v2.d2l.ai/d2l-zh.zip, fas fa-download,
课程, https://courses.d2l.ai/zh-v2/, fas fa-user-graduate,
GitHub, https://github.com/d2l-ai/d2l-zh, fab fa-github,
English, https://d2l.ai, fas fa-external-link-alt
favicon = static/favicon.png
html_logo = static/logo-with-text.png
[pdf]
# The file used to post-process the generated tex file.
post_latex = ./static/post_latex/main.py
latex_logo = static/logo.png
bibfile = d2l.bib
[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, zeros, arange, meshgrid, sin, sinh, cos, cosh, tanh,
linspace, exp, log, tensor -> array, normal -> random.normal,
randn -> random.randn,
rand -> random.rand, matmul -> dot, int32, 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,
alias =
size = lambda a: a.size
transpose = lambda a: a.T
nn_Module = nn.Block
reverse_alias =
d2l.size\(([\w\_\d]+)\) -> \1.size
d2l.transpose\(([\w\_\d]+)\) -> \1.T
d2l.nn_Module -> nn.Block
[library-pytorch]
lib_file = d2l/torch.py
lib_name = torch
simple_alias = ones, zeros, tensor, arange, meshgrid, sin, sinh, cos, cosh,
tanh, linspace, exp, log, normal, rand, randn, matmul, int32, float32,
concat -> cat, stack, abs, eye
fluent_alias = numpy -> detach().numpy, size -> numel, reshape, to,
reduce_sum -> sum, argmax, astype -> type, transpose -> t,
reduce_mean -> mean
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, 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, float32, transpose,
concat, stack, abs, eye, log -> math.log
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
[library-paddle]
lib_file = d2l/paddle.py
lib_name = paddle
simple_alias = ones, zeros, tensor -> to_tensor, arange, meshgrid, sin, sinh, cos, cosh,
tanh, linspace, exp, log, normal, rand, randn, matmul, int32, float32,
concat, stack, abs, eye
fluent_alias = numpy -> detach().numpy, size -> numel, reshape, to,
reduce_sum -> sum, argmax, astype, transpose -> t,
reduce_mean -> mean
alias =
nn_Module = nn.Layer
reverse_alias =
d2l.nn_Module -> nn.Layer
[deploy]
other_file_s3urls = s3://d2l-webdata/releases/d2l-zh/d2l-zh-1.0.zip
s3://d2l-webdata/releases/d2l-zh/d2l-zh-1.1.zip
s3://d2l-webdata/releases/d2l-zh/d2l-zh-2.0.0.zip
google_analytics_tracking_id = UA-96378503-2
[colab]
github_repo = mxnet, d2l-ai/d2l-zh-colab
pytorch, d2l-ai/d2l-zh-pytorch-colab
tensorflow, d2l-ai/d2l-zh-tensorflow-colab
paddle, d2l-ai/d2l-zh-paddle-colab
replace_svg_url = img, http://d2l.ai/_images
libs = mxnet, mxnet, -U mxnet-cu101==1.7.0
mxnet, d2l, git+https://github.com/d2l-ai/d2l-zh@release # installing d2l
pytorch, d2l, git+https://github.com/d2l-ai/d2l-zh@release # installing d2l
tensorflow, d2l, git+https://github.com/d2l-ai/d2l-zh@release # installing d2l
paddle, d2l, git+https://github.com/d2l-ai/d2l-zh@release # installing d2l
[sagemaker]
github_repo = mxnet, d2l-ai/d2l-zh-sagemaker
pytorch, d2l-ai/d2l-zh-pytorch-sagemaker
tensorflow, d2l-ai/d2l-zh-tensorflow-sagemaker
paddle, d2l-ai/d2l-zh-paddle-sagemaker
kernel = mxnet, conda_mxnet_p36
pytorch, conda_pytorch_p36
tensorflow, conda_tensorflow_p36
paddle, conda_paddle_p36
libs = mxnet, mxnet, -U mxnet-cu101==1.7.0
mxnet, d2l, .. # installing d2l
pytorch, d2l, .. # installing d2l
tensorflow, d2l, .. # installing d2l
paddle, d2l, .. # installing d2l
[slides]
top_right = <img height=80px src='http://d2l.ai/_static/logo-with-text.png'/>
github_repo = pytorch, d2l-ai/d2l-zh-pytorch-slides