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translate all guide to chinese #43

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70 changes: 70 additions & 0 deletions docs-zh/config.ini
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# A demo for d2l-book.
[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-book

# All author names
author = Mu Li

release = 0.1.17

[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.
header_links = PDF, https://book.d2l.ai/d2l-book.pdf, fas fa-file-pdf,
Github, https://github.com/d2l-ai/d2l-book, fab fa-github

# The filename of the favicon
favicon = img/favicon.png

html_logo = img/d2l-book.png

[pdf]

latex_logo = img/d2l.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, if anyone is modified after the last build, will re-build all
# documents.
dependences = config.ini

# A list of files that will be copied to the build folder.
resources = img/ refs.bib

# Specify the tabs, separated by ",". The first one will be the default tab.
tabs = python, numpy, cpython

[deploy]


# Tracking ID for the HTML pages
google_analytics_tracking_id = UA-96378503-15

# The S3 bucket that all files will copy to
s3_bucket = s3://book.d2l.ai

# [colab]

# # The github repo to host the notebooks for colab, such as d2l-ai/d2l-book-colab
# # Also make sure that the machine's ssh key is added to github before running
# # "deploy" so that it can commit into d2l-ai/d2l-book-colab
# github_repo = d2l-ai/d2l-book-colab

# # Colab cannot display SVG files with a relative fname or a github URL. You can
# # replace it with your website URL. For example: img, http://book.d2l.ai/_images
# # will "img/test.svg" to "http://book.d2l.ai/_images/test.svg"
# replace_svg_url = img, http://book.d2l.ai/_images


# [sagemaker]
# github_repo = d2l-ai/d2l-book-sagemaker
20 changes: 20 additions & 0 deletions docs-zh/demo.md
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# 构建本网站

您可能会发现建立这个网站是您项目的一个很好的起点。该网站的源代码可在以下位置获得 [demo/](https://github.com/d2l-ai/d2l-book/tree/master/demo).

请确保你安装了`git`(可以通过 `conda install git` 安装)、`numpy``matplotlib` (可以通过 `pip install numpy matplotlib` 安装)。以下命令将下载源代码、评估所有笔记本并生成 `ipynb``html``pdf` 格式的输出。

```sh
git clone https://github.com/d2l-ai/d2l-book
cd d2l-book/demo
d2lbook build all
```

完成后,您可以在 `_build` 文件夹中检查结果。例如,现在这个页面会在 `_build/html/index.html`,PDF版本会在 `_build/pdf/d2l-book.pdf`,所有评估的笔记本都会在 `_build/eval/` 下。
你可以单独生成以下特定的格式:

```sh
d2lbook build eval # 评估笔记本并以 .ipynb 格式保存
d2lbook build html # 构建 html 版本
d2lbook build pdf # 构建 pdf 版本
```
8 changes: 8 additions & 0 deletions docs-zh/develop/index.md
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# Development Guide

解释它是如何工作的。


```toc
pipeline
```
22 changes: 22 additions & 0 deletions docs-zh/develop/pipeline.md
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# 构建管道


![](../img/build.svg)


源文件是一些 markdown 文件。它们要么是纯粹的 markdown 文件,要么是以 markdown 格式保存、但已删除输出的 juypyter 笔记本。对于后者,我们可以直接使用 Jupyter 运行 `notedown` 插件来打开并编辑它们,然后在提交前运行 “Kernel -> Restart & Clear Output” 以清除输出。

然后我们的构建管道会运行以下步骤来构建结果。

1. 将 .md 文件转换为 .ipynb 文件并评估每个文件。我们使用 .md 文件作为源格式的原因是因为它很容易查看源文件的更改情况。我们每次都进行评估的原因是保证每个笔记本都是可执行的。这个评估步骤可能很耗时,我们可以
- 假设每个 notebook 可以在 10 分钟内执行,我们可以使用多个 GPU 来加速执行。
- 如果源.md文件在上次评估后没有变化,我们可以重新使用缓存的.ipynb文件,以避免再次执行。
-我们使用多个进程并行运行 notebook 。
1. 带有输出的 .ipynb 文件可以直接上传到 Github,因此用户可以克隆它以在本地或云端运行它们。我们还压缩所有文件,以便用户可以轻松下载。
1. 然后将这些 .ipynb 文件转换为格式与 Sphinx 兼容的 .rst 文件。额外的预处理步骤用于图像/表格/引文参考。
1. 使用 Sphinx 构建 .html 和 .pdf 文件
1. 在线发布所有 .html/.pdf/.zip 文件,例如发布到 AWS S3 存储桶中。

## 多种实现方案

![](../img/multi-lang.svg)
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