From c24c996843c78b590c6ec5ea8a328118d69f3747 Mon Sep 17 00:00:00 2001 From: Aston Zhang Date: Wed, 28 Nov 2018 12:08:04 +0000 Subject: [PATCH] gluon_tutorials_zh to d2l-zh --- chapter_appendix/aws.md | 8 ++++---- chapter_appendix/how-to-contribute.md | 10 +++++----- chapter_appendix/jupyter.md | 6 +++--- chapter_prerequisite/install.md | 17 ++++++++--------- img/contrib01.png | Bin 143510 -> 149413 bytes img/contrib02.png | Bin 174198 -> 165286 bytes img/contrib03.png | Bin 181008 -> 173546 bytes img/contrib04.png | Bin 118352 -> 124179 bytes img/contrib05.png | Bin 185920 -> 195831 bytes img/contrib06.png | Bin 125110 -> 125775 bytes 10 files changed, 20 insertions(+), 21 deletions(-) diff --git a/chapter_appendix/aws.md b/chapter_appendix/aws.md index aaf83a2ec..f3ffbd939 100644 --- a/chapter_appendix/aws.md +++ b/chapter_appendix/aws.md @@ -1,6 +1,6 @@ # 使用AWS运行代码 -当本地机器的计算资源有限时,我们可以通过云计算服务获取更强大的计算资源来运行本书中的深度学习代码。本节将介绍如何在AWS(亚马逊的云计算服务)上申请实例并通过Jupyter笔记本运行代码。本节中的例子有如下两个步骤: +当本地机器的计算资源有限时,我们可以通过云计算服务获取更强大的计算资源来运行本书中的深度学习代码。本节将介绍如何在AWS(亚马逊的云计算服务)上申请实例并通过Jupyter笔记本运行代码。本节中的例子有如下两个步骤: 1. 申请含一个K80 GPU的“p2.xlarge”实例。 2. 安装CUDA及相应GPU版本的MXNet。 @@ -134,9 +134,9 @@ to PATH in your /home/ubuntu/.bashrc ? [yes|no] 安装完成后,运行一次`source ~/.bashrc`让CUDA和conda生效。接下来,下载本书代码、安装并激活conda环境。 ``` -mkdir gluon_tutorials_zh && cd gluon_tutorials_zh -curl https://zh.gluon.ai/gluon_tutorials_zh.tar.gz -o tutorials.tar.gz -tar -xzvf tutorials.tar.gz && rm tutorials.tar.gz +mkdir d2l-zh && cd d2l-zh +curl https://zh.diveintodeeplearning.org/d2l-zh-1.0.zip -o d2l-zh.zip +unzip d2l-zh.zip && rm d2l-zh.zip conda env create -f environment.yml source activate gluon ``` diff --git a/chapter_appendix/how-to-contribute.md b/chapter_appendix/how-to-contribute.md index b808eebf2..391a294f7 100644 --- a/chapter_appendix/how-to-contribute.md +++ b/chapter_appendix/how-to-contribute.md @@ -15,7 +15,7 @@ ![代码库的页面。](../img/contrib01.png) -这时,本书的代码库会复制到你的用户名下,例如图11.21左上方显示的“你的Github ID/gluon-tutorials-zh”。 +这时,本书的代码库会复制到你的用户名下,例如图11.21左上方显示的“你的Github ID/d2l-zh”。 ![复制代码库。](../img/contrib02.png) @@ -24,13 +24,13 @@ ``` # 将 your_Github_ID 替换成你的 Github 用户名。 -git clone https://github.com/your_Github_ID/gluon-tutorials-zh.git +git clone https://github.com/your_Github_ID/d2l-zh.git ``` -这时,本地的“~/repo/gluon-tutorials-zh”路径下将包含本书代码库中的所有文件。 +这时,本地的“~/repo/d2l-zh”路径下将包含本书代码库中的所有文件。 -第四步,编辑本地路径下的本书代码库。假设我们修改了`~/repo/gluon-tutorials-zh/chapter_deep-learning-basics/linear-regression.md`文件中的一个错别字。在命令行模式中进入路径`~/repo/gluon-tutorials-zh`,执行命令 +第四步,编辑本地路径下的本书代码库。假设我们修改了`~/repo/d2l-zh/chapter_deep-learning-basics/linear-regression.md`文件中的一个错别字。在命令行模式中进入路径`~/repo/d2l-zh`,执行命令 ``` git status @@ -51,7 +51,7 @@ git push 其中的`'fix typo in linear-regression.md'`是描述提交改动的信息,也可以替换为其他有意义的描述信息。 -第五步,再次在浏览器输入本书代码库地址 [2]。点击图11.20左方红框中的“New pull request”按钮。在弹出的页面中,点击图11.23右方红框中的“compare across forks”链接,再点击下方红框中的“head fork: mli/gluon-tutorials-zh”按钮。在弹出的文本框中输入你的Github ID,在下拉菜单中选择“你的Github-ID/gluon-tutorials-zh”,如图11.23所示。 +第五步,再次在浏览器输入本书代码库地址 [2]。点击图11.20左方红框中的“New pull request”按钮。在弹出的页面中,点击图11.23右方红框中的“compare across forks”链接,再点击下方红框中的“head fork: diveintodeeplearning/d2l-zh”按钮。在弹出的文本框中输入你的Github ID,在下拉菜单中选择“你的Github-ID/d2l-zh”,如图11.23所示。 ![选择改动来源所在的代码库。](../img/contrib04.png) diff --git a/chapter_appendix/jupyter.md b/chapter_appendix/jupyter.md index 6a8fd63e9..a3a2e9a15 100644 --- a/chapter_appendix/jupyter.md +++ b/chapter_appendix/jupyter.md @@ -5,7 +5,7 @@ ## 在本地编辑和运行本书代码 -下面我们介绍如何在本地使用Jupyter笔记本来编辑和运行本书代码。假设本书代码所在的本地路径为“xx/yy/gluon_tutorials_zh-1.0/”。在命令行模式下进入该路径(`cd xx/yy/gluon_tutorials_zh-1.0`),然后运行命令`jupyter notebook`。这时在浏览器打开 http://localhost:8888 (通常会自动打开)就可以看到Jupyter笔记本的界面和本书代码所在的各个文件夹,如图11.1所示。 +下面我们介绍如何在本地使用Jupyter笔记本来编辑和运行本书代码。假设本书代码所在的本地路径为“xx/yy/d2l-zh/”。在命令行模式下进入该路径(`cd xx/yy/d2l-zh`),然后运行命令`jupyter notebook`。这时在浏览器打开 http://localhost:8888 (通常会自动打开)就可以看到Jupyter笔记本的界面和本书代码所在的各个文件夹,如图11.1所示。 ![本书代码所在的各个文件夹。](../img/jupyter00.png) @@ -53,8 +53,8 @@ 如果你希望为本书内容做贡献,需要修改在GitHub上markdown格式的源文件(.md文件非.ipynb文件)。通过notedown插件,我们就可以使用Jupyter笔记本修改并运行markdown格式的源代码。Linux/macOS用户可以执行以下命令获得GitHub源文件并激活运行环境。 ``` -git clone https://github.com/mli/gluon-tutorials-zh -cd gluon-tutorials-zh +git clone https://github.com/diveintodeeplearning/d2l-zh.git +cd d2l-zh conda env create -f environment.yml source activate gluon # Windows 用户运行 activate gluon ``` diff --git a/chapter_prerequisite/install.md b/chapter_prerequisite/install.md index 0150df4f3..dd5a7674f 100644 --- a/chapter_prerequisite/install.md +++ b/chapter_prerequisite/install.md @@ -14,9 +14,9 @@ 第二步:下载包含本书全部代码的压缩包。我们可以在浏览器的地址栏中输入以下地址并按回车键进行下载: -> https://zh.gluon.ai/gluon_tutorials_zh-1.0.zip +> https://zh.diveintodeeplearning.org/d2l-zh-1.0.zip -下载完成后,创建文件夹“gluon_tutorials_zh-1.0”并将以上压缩包解压到这个文件夹。在该目录文件资源管理器的地址栏输入`cmd`进入命令行模式。 +下载完成后,创建文件夹“d2l-zh”并将以上压缩包解压到这个文件夹。在该目录文件资源管理器的地址栏输入`cmd`进入命令行模式。 第三步:使用Conda创建并激活环境。Conda默认使用国外站点来下载软件,以下可选项配置使用国内镜像加速下载: @@ -77,9 +77,9 @@ to PATH in your /home/your_name/.conda ? [yes|no] 第二步:下载包含本书全部代码的压缩包,解压后进入文件夹。运行以下命令。 ``` -mkdir gluon_tutorials_zh-1.0 && cd gluon_tutorials_zh-1.0 -curl https://zh.gluon.ai/gluon_tutorials_zh-1.0.tar.gz -o tutorials.tar.gz -tar -xzvf tutorials.tar.gz && rm tutorials.tar.gz +mkdir d2l-zh && cd d2l-zh +curl https://zh.diveintodeeplearning.org/d2l-zh-1.0.zip -o d2l-zh.zip +unzip d2l-zh.zip && rm d2l-zh.zip ``` 第三至五步请参考前面Windows下的安装步骤。其中第四步需将命令替换为 @@ -92,12 +92,11 @@ source activate gluon 为了适应深度学习和MXNet的快速发展,本书的开源内容将定期发布新版本。我们推荐大家定期更新本书的开源内容(例如代码)和相应的运行环境(例如新版MXNet)。以下是更新的具体步骤。 -第一步,重新下载最新的包含本书全部代码的压缩包。下载地址可以从以下二者之间选择。 +第一步,重新下载最新的包含本书全部代码的压缩包。下载地址: -* https://zh.gluon.ai/gluon_tutorials_zh.zip -* https://zh.gluon.ai/gluon_tutorials_zh.tar.gz +* https://zh.diveintodeeplearning.org/d2l-zh.zip -解压后进入文件夹“gluon_tutorials_zh”。 +解压后进入文件夹“d2l-zh”。 第二步,使用下面命令更新运行环境。 diff --git a/img/contrib01.png b/img/contrib01.png index f614737ca156b433a3a348c6062a613a419d5a78..8c193a5355b9e6fa34890200c38c3bb61017bdbd 100644 GIT binary patch literal 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