Open edX（4）
转载至http://blog.csdn.net/u/article/details/
1.列出所有manage.py命令参数:
www-data&/edx/app/edxapp/venvs/edxapp/bin/python/edx/app/edxapp/edx-platform/manage.py
lms --settings aws help
2.重启edxapp
sudo&/edx/bin/supervisorctl&restart
以前的版本使用:
sudo&/edx/bin/supervisorctl-c&/edx/etc/supervisord.conf&
3.查看服务器状态:
sudo&/edx/bin/supervisorctl&status
4.创建一个名为”user”超级管理员(django-admin)：
www-data&/edx/bin/python.edxapp
./manage.py
lms --settings aws create_user -s -p edx -e
www-data&/edx/bin/python.edxapp
./manage.py
lms --settings aws changepassword user
www-data&/edx/bin/python.edxapp
./manage.py
lms --settings aws shell
from django.contrib.auth.models&import&User
me = User.objects.get(username=&user&)
me.is_superuser = True
me.is_staff = True
5.以dubug模式启动lms:
cd&/edx/app/edxapp/edx-platform
www-data&/edx/bin/python.edxapp
./manage.py
lms runserver 8000 --settings aws
&&&&ip:8000可访问
6.重建数据库表结构
sudo&su&edxapp
-s&/bin/bash
source&edxapp_env
python&/edx/app/edxapp/edx-platform/manage.py
lms/cmssyncdb
--migrate --settings=aws
(lms/cms二选一)
7.编译lms静态文件
-u edxapp&bash
source&/edx/app/edxapp/edxapp_env
cd&/edx/app/edxapp/edx-platform
paver update_assets lms --settings aws
编译cms只要将lms改成cms既可。
/edx/app/edxapp/edx-platform
1.从github导入一门课程(以/edx-demo为例)
cd&/var/tmp
git clone https://github.com/edx/edx-demo-course.git
cd&/edx/app/edxapp/edx-platform
www-data&/edx/bin/python.edxapp
./manage.py
cms --settings=aws&import/edx/var/edxapp/data&&/var/tmp/edx-demo-course
2.删除一门课程(cypress版本):
www-data&/edx/bin/python.edxapp
./manage.py
cms& --settings=aws delete_course course-v1:Organization+CourseNumber+CourseRun commit
course-v1:Organization+CourseNumber+CourseRun为课程
以前的版本使用:
www-data&/edx/bin/python.edxapp
./manage.py
cms --settings=aws delete_course Organization/CourseNumber/CourseRun&commit
如果删除失败可从数据库删除：
#列出所有课程
use edxapp
db.modulestore.find(
{&&_id.category&:&&course&},
{'name':'1'}
#删除课程代码为999的课程
db.modulestore.remove({'_id.course':&'999'})
3.删除一个邮箱为””的账户
www-data&/edx/bin/python.edxapp
./manage.py
lms --settings aws shell
from django.contrib.auth.models&import&User
u=User.objects.get(email=&&);
[obj.delete()&forobj&inu.preferences.all()];
u.delete()
sudo&su&edxapp
-s&/bin/bash
source&edxapp_env
from django.contrib.auth.models&import&User
u=User.objects.get(email=&&);
[obj.delete()&forobj&inu.preferences.all()];
u.delete()
参考知识库
* 以上用户言论只代表其个人观点，不代表CSDN网站的观点或立场
访问：8325次
排名：千里之外
原创：24篇
转载：26篇
(4)(1)(4)(11)(2)(1)(27)(2)6687人阅读
编程竞赛（8）
转载自&http://blog.csdn.net/fightforyourdream/article/details/感觉是很好的CS 网上公开课总结，在终身学习时代，好好利用网络学习资源对于个人成长和发展非常重要。从面向找工作的角度出发，我觉得以下课程有很大帮助：首推Robert Sedgewick，也是我觉得对我帮助最大的老师，讲课特点是能把复杂的算法讲解清楚（典型例子：红黑树，KMP算法）他在Coursera有四门课，循序渐进，也越来越理论，尤其是前三门，非常值得一上。个人认为上完前两门，你的理论基础（当然还要结合刷题的实践）已经可以虐普遍的小公司和大部分的大公司了。上完第三门可以虐一流公司如Google，Facebook，Linkedin等。第四门还没开，不过看过课程介绍，觉得上完可以去当大公司的算法工程师了。下面列出这四门课：& 内容：Union-Find，Analysis of Algorithms，Stacks and Queues，Elementary Sorts，Mergesort，Quicksort，Priority Queues，Elementary Symbol Tables，Balanced Search Trees，Geometric Applications of BSTs，Hash Tables& 内容：Undirected Graphs，Directed Graphs，Minimum Spanning Trees，Shortest Paths，Maximum Flow，String Sorts，Tries，Substring Search，Regular Expressions，Data Compression，Reductions，Linear Programming，Intractability & & 唯一的遗憾就是没有讲Dynamic Programming& 内容：Analysis of Algorithms，Recurrences，Solving recurrences with GFs，Asymptotics，The symbolic method，Trees，Permutations，Strings and Tries，Words and Mappings &也是非常干货的一门课！& 内容请参考连接，感觉已经非常理论了。Intro to Data Science然后我想上的课有:Stanford的Machine Learning：& & &(有课)Functional Programming Principles in Scala && （有课）Principles of Computing && && （6/9）Programming Cloud Services for Android Handheld Systems && 云(7/21)Algorithmic Thinking && &(8/25)機器學習基石 (Machine Learning Foundations) && &试试台湾大学的课程(有课)程序设计实习 / Practice on Programming && & 前半部分都是介绍C++比较无趣，后半部分讲算法。另外一个优点就是用POJ平台！（有课）Web Intelligence and Big Data&&& &大数据（有课）The Hardware/Software Interface && &其实就是CMU的15213，但据说讲的比CMU还好（有课）Machine Learning &&& （watchlist）Introduction to Data Science &&&（watchlist）Introduction to Recommender Systems &&&&感觉非常有意思的一门课，能做出像Amazon一样的推荐系统~ &&（watchlist）Web Application&& RoR（有课）Software as a Service &HTML5 Game Development &&& &感觉是个挺有意思的项目Software Testing& &了解一些Test是做什么的Software Debugging &&& & 同上DebugProgramming Languages &&Design of Computer Programs &&Intro to Parallel ProgrammingDiscrete Mathematics in Computer Science&&TsinghuaX: X 组合数学Stanford系列：& 好课！MIT系列：Introduction to Algorithm:Mathematics for Computer ScienceAdvanced Data StructuresComputer System EngineeringMulticore Programming PrimerPython:组合数学：图论：&&&&初等数论：Distributed System (KTH)Data Science is a hot topic and there are plenty of courses and resources available for anyone interested. Try out these 9 free resources to get started if you are new to the topic or want to refresh on one of the subjects.Data ScienceA Coursera course specifically about data science, due to start in April 2013. I am very curious about it since its broad syllabus appears to capture many of the experiences data scientists need. Much of it had to be gathered in the field until now. Having a dedicated course for it is an appealing idea.Course Syllabus – Specific TopicsData modeling: relations, key-value, trees, graphs, images, textRelational algebra and parallel query processingNoSQL systems, key-value storesTradeoffs of SQL, NoSQL, and NewSQL systemsAlgorithm design in Hadoop (and MapReduce in general)Basic statistical analysis at scale: sampling, regressionIntroduction to data mining: clustering, association rules, decision treesCase studies in analytics: social networking, bioinformatics, text processingThe academy is due to start early 2013 with some interesting workshops:Dive into Cloudera ImpalaNumPy for Data ScientistsCouchbase for Data ScientistsMapReduce Algorithm DesignIntegrating SAP HANA with RScikit-learn: Machine Learning with PythonThe School of Data recently started with their first course, Data Fundamentals. It is a great starting point for anyone interested in (big) data and data science and lays the foundations for more serious work.“The mission of the School of Data is to promote data literacy and data ‘wrangling’ skills – the ability to find, clean, retrieve, manipulate, analyse, interpret and represent different types of data – across the world. The more people who have the skills to understand and work with data effectively, the greater its value and impact, and the more likely it is that data will be able to bring about positive social benefits.”You can read through the blog of Columbia’s fall 2012 data science course if you can not wait for Coursera in April 2013. The blog posts are very detailed and worthwhile reading if you are new to the field or want to get a broad view of it.This free book is available under a Creative Commons licence.&So download it and read it for free.&It utilises R and lots of examples to introduce the topic.Machine LearningData Science and machine learning are tightly related and should be of interest to any data science enthusiast. The Coursera machine learning course by Stanford Associate Professor Andrew Ng comes highly recommended to anyone interested in a solid introduction into machine learning with a hands-on approach, and great lecture material and videos.The California Institute of Technology ran a free online machine learning course with video lectures earlier in 2012. The lectures are still online for anyone to watch and another course will start in January 2013.VisualisationAn important aspect of data science can be data visualisation. The best analytics and models are not effective if the information and insight gained can not be easily and transparently shared with your client, consumer, or customer. The Knight Center is running their second massive open online course early 2013 about infographics and data visualisation.StatisticsStatistics and data analysis are, of course, the bread and butter of data science.&This fall 2012 Carnegie Mellon University course is not as fancy as Coursera one. In fact, it is little more than a page with all the lecture slides, homework, lab sheets and solutions. But it is free and comprehensive so give it a try.UpdateI know I wrote 9 resources but as I come across something good I might just append it here to the end.This is a fun way to get started with R. It is a web site that teaches you, interactively, R. Not much more to say than give it a go.Wiki BooksHead over to Wiki Books to read ‘‘. There is already some signifcant material. Nevertheless, it is a work in progress and you can contribute.Nearly complete is ‘‘ a book, you guessed it, about statistics.Python, OpenStack, Docker感觉coursera的很多课都受制于开课时间，Udacity到时时间很灵活，如果选非付费版也可以看所有的视频和做作业，除了不能做项目罢了，还是挺不错的。本list将保持不断更新。。。
参考知识库
* 以上用户言论只代表其个人观点，不代表CSDN网站的观点或立场
访问：1181135次
积分：12370
积分：12370
排名：第952名
原创：240篇
转载：36篇
评论：736条
(1)(3)(3)(4)(1)(9)(5)(6)(4)(19)(13)(25)(2)(13)(7)(2)(1)(1)(1)(1)(1)(4)(1)(6)(3)(2)(2)(4)(9)(11)(5)(24)(17)(1)(27)(36)(1)(2)(2)在内部网络为edX配置视频服务 - 博客频道 - CSDN.NET
由于CSDN的博客使用体验不好，此博客不再更新。新版博客https://my.oschina.net/liuyuantao/blog
分类：Open edX
原文地址：http://edustack.org/
在部署使用Open edX的过程中，一个普遍的问题是：视频放在哪儿？
Open edX本身并不是设计来存储视频的。edX本质上提供了一套学习工具和学习过程，而一些比较专业化的资源是置于外部的，比如视频以及机器学习、自动评分等等。
CMS中有提供文件上传功能。如果你的edX仅仅是提供给少数人用的，你也可以把视频放到这里，并在视频模块中输入相应的链接。你需要考虑的是edX主机的空间是否足够存储视频；视频播放带来的cpu内存损耗能否能让edX代码正常的运行；edX主机的网络带宽能支持播放多少并发？
在公网上的edX可以直接购买视频云服务，例如腾讯云、阿里云等。服务商保证了视频的可用性和播放速度，也不会对edX主机的性能造成影响。还可以根据视频的需求扩展资源。如果不想在视频投入成本，也可以使用@wwj的。当然，会有优酷广告（没有免费的午餐）。
有几种场景需要搭建内网视频服务器来配合edX：
一般这种场景出现在教育机构和大型企业中。此时应该搭建专门的视频服务器。
选择视频格式
目前最常见的视频播放格式就是FLV和MP4。对应的有flash播放器以及HTML5播放器。
由于flash播放器的兼容性以及未来flash技术的前景，不建议选择这种格式。
目前HTML5播放技术比较成熟，但仍存在一些兼容问题，具体可见&
根据上面链接的资料，H264格式是受支持最广的，所以一般应该用这个格式来编码课程视频。
视频版权保护DRM方面，可见这个讨论&，W3C已经提出了EME（Encrypted
Media Extensions）标准，并已经得到支持。不过目前还并不能在HTML5中很方便的进行视频版权保护。
搭建视频服务器
比较简单的方案，自行搭建Nginx来提供视频播放。
Nginx中有一些和视频相关的模块：
http://nginx.org/en/docs/http/ngx_http_flv_module.html
The&ngx_http_flv_module&module provides pseudo-streaming server-side support for Flash Video (FLV) files.
It handles requests with the&start&argument in the request URI’s query string specially, by sending back the contents
of a file starting from the requested byte offset and with the prepended FLV header.
http://nginx.org/en/docs/http/ngx_http_mp4_module.html
The&ngx_http_mp4_module&module provides pseudo-streaming server-side support for MP4 files. Such files typically
have the&.mp4,&.m4v,
or&.m4a&filename extensions.
Pseudo-streaming works in alliance with a compatible Flash player. The player sends an HTTP request to the server with the start time specified in the query string argument (named simply&start&and
specified in seconds), and the server responds with the stream such that its start position corresponds to the requested time, for example:
http://nginx.org/en/docs/http/ngx_http_hls_module.html
The&ngx_http_hls_module&module provides HTTP Live Streaming (HLS) server-side support for MP4 and MOV media files.
Such files typically have the&.mp4,&.m4v,&.m4a,&.mov,
or&.qt&filename extensions. The module supports H.264 video codec, AAC and MP3 audio codecs.
可见ngx_http_flv_module和ngx_http_mp4_module都是为了flash播放器而准备的，主要都是提供了视频定位的功能，让flash播放器可以直接定位到视频中的时间点，根据指定的参数start。所以这两个模块如果不用flash播放器就无需安装。其中pseudo-streaming是一种伪流技术，实现了服务器端和flash播放器之间的通信。
ngx_http_hls_module提供了对于HLS的支持。HLS是苹果定义的一个基于HTTP的流媒体传输协议，可实现流媒体的直播和点播。如不打算用HLS对课件进行编码，这个模块也可以不装。
那么不安装附加的组件是否就不可以在HTML5播放器对视频进行时间定位？W3C有一个标准Media Fragments URI，可以实现浏览器传递时间参数。可以在浏览器输入&课程视频来测试。在视频后面加入不同的参数：
这里也展示了使用js来定位视频时间的方式。
视频集群负载均衡
对于一个机构内部的在线课程视频服务，需要经过计算来确定视频集群的数量。这里给出一个比较粗放的计算方法。
首先确定用户数量。这里有几个概念：总用户数/在线用户数/并发数。例如一个学校内部有10000个学生用户，在线用户可能为1000人，同时并发观看视频的有200人。具体的数量关系目前并没有统计数据，需要经过调查及观察，在不同时间段还会有不同的峰值，比如晚上学生下课后观看人数比白天会多得多。
其次确定视频的码率，几种常见的码率：
流畅：码率—256kbps，分辨率—480*360，俗称360P
高清：码率—512kbps，分辨率—640*480，俗称480P
超清：码率—1024kbps，分辨率—，俗称720P
以720p为例，同时并发200人，并发流量为200*1024kbps=204800kbps=204mbps。再综合考虑服务器磁盘性能和网卡性能，对应交换机的性能，估算出需要什么规模的视频集群。
nginx视频负载均衡&
排名：第5988名
（48）（14）（16）（43）（13）（5）（14）（63）（7）（34）（10）（83）（22）（7）（1）（17）（6）（2）（3）（10）（14）（15）（13）（0）（5）（1）（27）（10）（7）（7）（6）