Emacs in ubuntu

Download GNU Emacs

Emacs download address

一、编译安装

1. 准备工作
(1)安装svn、ssh

sudo apt-get install ssh subversion

(2)安装texinfo(为了编译man包)

sudo apt-get install texinfo

(3)安装编译所需的支持包,依环境而定

sudo apt-get build-dep emacs25 libgtk2.0-dev xserver-xorg-dev
xorg-dev libncurses5 libncurses5-dev libidl.dev

此处记得勾上 Software & Updates 里的source code
这里写图片描述

2. 编译、安装
创建目录:/opt/emacs25,将emacs安装到这里

sudo mkdir /opt/emacs25

注:最好指定一个安装目录,要不然编译出来的binary会被分散装到不同的地方

cd ~/emacs ./configure --prefix=/opt/emacs24 --with-x-toolkit=gtk

参数解释:
可以通过/.configure -h 来查看帮助文档
–prefix=/opt/emacs24 指定emacs安装目录,默认为/usr/local
–with-x-toolkit=gtk 指定环境为gtk

没有错误则继续:

make bootstrap

make info

编译完后,试运行一下 src/emacs -q,没有问题就可以安装了:

sudo make install

清理:

make clean

3. 添加你的安装路径
安装完后要在/usr/local/bin做一个链接(因为/opt/emacs24/bin/不在PATH变量中)

cd /usr/local/bin

sudo ln -s /opt/emacs24/bin/* ./

链接好后,在终端中输入emacs就可以启动emacs了。

4. 配置cedet
5. 配置ecb

5.1 下载ecb:

https://github.com/alexott/ecb/

5.2 将下载好的ecb目录拷贝到~/.emacs.d目录

cp -rf ecb ~/.emacs.d

5.3 配置.emacs文件,添加以下内容

;;ecb
(add-to-list 'load-path "~/.emacs.d/ecb")
(require 'ecb)
;; (setq ecb-auto-activate t). ;;自动启动ecb
(global-set-key  [(f7)] 'ecb-activate) ;;F7:打开ecb
(global-set-key  [(f8)] 'ecb-deactivate) ;;F8:关闭ecb

声明:本文乃本人参考以下两位大神整理并且亲身测试成功整理出来的,为个人以后笔记。
参考1 http://blog.youkuaiyun.com/gqb_driver/article/details/29407717
参考2 https://blog.youkuaiyun.com/u010164190/article/details/78070206

### IsaacLab Installation Guide for Ubuntu 20.04 For installing IsaacLab on an Ubuntu 20.04 system, a series of preparatory steps must be completed to ensure compatibility and functionality. #### Preparing the System Environment Ensure that all necessary software is installed as described in tutorials covering essential installations for Ubuntu 20.04[^2]. This includes setting up the correct time zone, configuring input methods such as Sogou Pinyin, deploying Anaconda for Python development environments, using Terminator terminal emulator, ensuring Nvidia drivers are properly set up which is crucial since IsaacLab relies heavily on GPU acceleration capabilities provided by Nvidia hardware. #### Installing CUDA Toolkit Since IsaacLab requires CUDA support, follow guidelines similar to those outlined for WSL 2 but adapted specifically for native Linux distributions like Ubuntu 20.04[^4]. Install the appropriate version of the CUDA toolkit compatible with your graphics card model through either package managers or direct downloads from Nvidia's official website. ```bash sudo apt-get update && sudo apt-get install cuda ``` After installation completes successfully, verify it works correctly: ```bash nvcc --version ``` #### Setting Up cuDNN Following successful setup of CUDA, proceed to configure cuDNN according to instructions found within documentation related to Autoware environment configuration under Ubuntu 20.04[^3]: Download `libcudnn8` and its developer library corresponding to the chosen CUDA version before executing commands below: ```bash sudo dpkg -i libcudnn8_*.deb sudo dpkg -i libcudnn8-dev_*.deb ``` Confirm proper integration via running tests included inside sample directories bundled alongside cuDNN packages. #### Obtaining and Configuring IsaacLab Once prerequisites have been met, obtain access rights to download IsaacSim/IsaacLab SDK directly from Nvidia’s developer portal after registering there first if not done already. Follow detailed guides available online regarding unpacking archives, placing files into designated locations while adhering closely to any specific requirements mentioned during this process concerning dependencies management tools used throughout projects built around Omniverse platform components including IsaacLab itself. Finally, customize settings based upon personal preferences or project needs following post-installation procedures recommended officially by developers behind these applications. --related questions-- 1. What versions of CUDA should one choose when preparing their machine learning workstation? 2. How does adjusting system locale affect non-English speaking users' experiences on fresh installs of Ubuntu-based systems? 3. Can you explain how to troubleshoot common issues encountered after updating Nvidia drivers on Linux machines? 4. In what ways can integrating Jupyter notebooks enhance productivity within data science workflows conducted over Conda-managed virtual environments? 5. Are there alternative text editors besides Vim/GNU Emacs suitable for beginners who wish to write scripts efficiently without steep learning curves?
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