Welcome to the vast world of Swarmlab!
Swarmlab is a powerful collaboration tool that connects tutors and students in a unique all-in-one environment that gets work done fast and easily.
In other words Swarmlab is the way to make your learning fun while at the same time getting to your goals faster!
But let’s get you started…
Note: These instructions are general targeted at both students and tutors.
If you wish to skip to the specific instructions for tutors (lab creation, student invitation etc) click here.
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"General info WILL NOT be repeated so if you have basic questions regarding registration or labroom usage please read on from here. |
1. The first steps
Before you have all of the powerfull tools of Swarmalb at your grasp you will have to create an account.
To do that navigate to the home page at https://api-client.swarmlab.io:8088
You should have been greeted by our login screen!
To register click on the 'login with sso' option and accept the terms.
Now you should be redirected to the login screen and you should be able to use the 'register' option at the bottom to make a new account!
Now fill in the required details
and follow the remaining instructions to activate your account.
Now that your acount is ready, lets get to know Swarmlabs main page.
2. Main Menou
2.1. The main page
Once loaded the main page should look like this:
- On the left we can see that there are two areas inside swarmlab:
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the dashboard where our services live
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the 'mylab' tab from which we can control our labrooms.
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- The dashboard page is pretty straight forward:
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The left list shows all the available services and allows you to run them.
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The other shows the available lab rooms and allows you to interact with them (p.e. subscribe).
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With that out of the way lets enter the heart of our platform, 'my lab'!
2.2. My Lab
Nested under 'mylab' are both creation tools (bootstrap) and execution tools (run).
2.3. The run menu
- Inside the run menu there are three areas:
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'Rub lab instance'
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'Enter lab room'
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the console
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As the names suggest we can use the first to run one of our available lab instances, the second to enter a created labroom, and the third to execute scripts.
Lets look at the options one by one.
For the purpose of this walkthrough 'testmpi2' and 'test 18' were created.
Since the console is explained along with the first area we dont feel there is a need to have seperate walkthrough.
2.3.1. Run lab instance area
Creating a lab
To create a lab instance simply add a repo to your git!
You will then be able to locate that repo inside the instance list and run it.
For more info on this please refer to the Bootstrap chapter here !
Running a lab instance
First we have to select the lab we want to run. To find a specific lab we can use the search bar at the top.
After we have located the desired lab we can run it by using the cloud icon. 
To request more info about the lab we use the info icon 
and the trashcan 
deletes the lab (forever).
Carefull, forever is a long time!
So lets go back to running the lab. After clicking on the according icon we should be redirected to the execution screen.
Here we can use the command line on the bottom to execute the required commands. Our results will be visible in the console above.
Note that you can choose to use the editor of your liking from the drop down menu.
For more complicated tasks that may require responsivines from the machine you can use the terminal option to open a full-blown unix command line.
2.3.2. Enter lab room area
This is the area where you will find the available labrooms created by tutors and join the desired one.
Browsing labrooms
You can easily browse throught the available labrooms in the list using the search bar at the top.
You can get more info about a specific labroom by clicking the info button .
Active labrooms are indicated using a green color while closed ones with orange.
Entering a labroom
Once you have found the labroom you were looking for you can enter by simply clicking on the cloud icon .
You should see a screen as follows:
The passwords are randomly generated and you can use them to connect to your lesson (see picture bellow).
If the tutor is sharing his screen you can watch by clicking on the according button and using the respective password.
- Now, under the password area there are three other options:
- 'Meeting'
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Here you can join a meeting with your group (this will redirect you to a jitsi implementation).
For more visit https://jitsi.org/ - 'Gui'
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This loads a graphic unix environment.
- 'Console'
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This loads a unix terminal.
By now it should propably be clear how to run a lab from the list or enter a labroom.
But you are propably wondering 'My list is empty how am i supposed to run labs from it?!'
Well here is where the final but most basic menu comes into play: 'Bootstrap' !
2.4. Bootstrap
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Menu
choose myLAB → create |
Here we can create and add labs to our list. To do so we have to go through a series of steps, but lets first understand some basic things about how swarmlab works…
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Every swarmlab lab instance resides in a git repo created inside our git and owned by the lab creator. This repo then serves as a 'home' for all the work inside that lab. So for us to create and run a lab we have to link a service to a git repo. |
Asuming we have an account (using the same email as our swarmlab account!) all our repos should be shown on the list in the top left corner.
To search for any public repo we can use the search bar.
From that list we can now select the desired repository that we want to use by clicking on the plug icon. 
We can use any repo we like, but we will be able to commit only to those that we have access to.
If we wish to edit or contribute to the development of a project we are not part of we can use the clone option as usual!
Now we have to select the service that our lab will be using. We just select the desired one from the list on the right and it will be automatically linked to the project.
Finally we have to save the lab instance by filling in the required info at the bottom.
3. Tutors Menou
Tutors have access to an extended 'my lab/bootstap' page where they can create labrooms from existing lab instances.
To do so we have to scroll all the way down whilst on the Bootstrap page.
There we should find two menus, 'My labrooms' and 'Bootstrap Labroom'
3.1. 'My labrooms'
Here we can see a list of all the labrooms programmed to run today. Again like in the run tab we can distinguish open from closed ones by color (green/orange).
We can also get info using the info icon or delete the labroom using the trashcan icon .
To search for a specific labroom we can use the searchbar at the top.
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The delete command is FORCED even if the labroom is running!!! |
3.2. 'Bootstrap Labroom'
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Menu
choose myLAB → Bootstrap |
Now lets see how we can bootstrap a new labroom using one of our lab instances.
To do that lets navigate to the respective tab that should look like this:
Here we can select the desired (existing!) lab instance that will be used to create the labroom using the plug icon .
Then we have to specify starting date and time and also set the duration for which our lab room will be open.
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You can only start a labroom every whole or half hour (p.e. 12.00 or 14.30) and never inbetween so please plan ahead!! |
Also note that the duration cannot be greater than three hours since we consider that the maximum for any type of lesson!
Now using the searchbar that follows (you HAVE to search for students for them to come up) we search and select () the students that we want to invite to the lesson.
Note that only students that have subscribed to our labroom will be available for invites to avoid confusion!
After selecting all the desired students we have to fill in the last details at the bottom of the page.
Please give concise titles and good descriptions to your labrooms!!
Now we shall check and upon making shure that everything is right we click the 'bootstrap your lab' button!
| Bootstraping is final and no further edits are allowed!! |
4. Learning Objects Notebooks
A LO Notebook is a reusable web-based interactive chunks of e-learning designed to explain a stand-alone learning objective, which can be used, reused or referenced during technology supported learning.
Swarmlab.io Notebooks includes the following key characteristics:
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Learning objects are a new way of thinking about learning content. Traditionally, content comes in a several hour chunk. Learning objects are much smaller units of learning, typically ranging from 2 minutes to 15 minutes.
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Are self-contained – each learning object can be taken independently
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Are reusable – a single learning object may be used in multiple contexts for multiple purposes
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Can be aggregated – learning objects can be grouped into larger collections of content, including traditional course structures
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Are tagged with metadata – every learning object has descriptive information allowing it to be easily found by a search
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Learning Object Notebooks is: "A digital self-contained and reusable entity, with a clear educational purpose, with at least three internal and editable components: content, learning activities and elements of context.".[1]
— IEEE
"learning object any entity, digital or non-digital, that may be used for learning, education or training".[2]
— Wikipedia
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4.1. Usage
LO notebooks have a wealth of different uses including as a testing ground for development work, a presentation platform, and more.
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Ad hoc nature. - The ad hoc nature of notebooks is excellent for trying things out
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Designing, developing, and testing solutions to problems.
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Ready-to-Use Services. - Easier start. Your just start with the cleaned data, the trained model, and get right to the analysis.
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Presenting analyses, demonstrating both the code and the output, that can be easily turned into slides/pdf.
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Built for collaboration. - Swarmlab.io notebooks are easy to collaborate with using version control systems (git). The YAML index file makes it extremely easy to tell where things belongs to.
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Providing hands-on walkthroughs of new library modules, visualization techniques, and strategies for attacking existing problems. They allow someone to mostly follow along while allowing them space to try out new things right in-line.
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4.2. Run Learning Object Notebook
4.2.1. Find
After we have located the desired LO we can
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run it by using the cloud icon.
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To request more info about the lab we use the info icon
4.2.2. Run
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After we have Run the desired LO we can run "try it" by using the edit icon.
4.2.3. Try
4.3. Create Learning Object Notebook
4.3.1. Main Options
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"My LabLearningObjects" - LLO Admin panel
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"Publish LearningObjects" - Metadata and Publish your LLO
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"Bootstrap LearningObjects" - Create your LLO
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"How to Contribute"
4.3.2. Publish
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Creative Commons
Get familiar with "The Creative Commons copyright licenses". ‘About the Licenses'.footnote[http://creativecommons.org/about/licenses] Creative Commons is an international nonprofit that offers flexible copyright management tools for creative work. Many learning objects have CC licences. |
When you are finished creating your Learning Object, Use "git push" to push commits to swarmlab.io repository
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Find the url where the yaml-index resides.
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Whithin the "Publish Learning Object" menu you can choose to publish your LLO
After that you can admin your LLO using the "My Lab Learning Object".
4.3.3. Bootstrap
index
Learning Objects Notebooks Based on the following structure:
--- # Lab Learning Object
name: "test" (1)
author: "zeus"
intro: "intro" # intro is a file (2)
base_url: "https://git.swarmlab.io:3000/labs/examples-mpi2/raw/branch/master" (3)
0_netstat: (4)
display_name: "netstat" (5)
swarmlabservice: "os2" // (6)
info:
file: "0_netstat/info" (7)
code:
lang: "bash" (8)
exec: "/bin/bash" (9)
file: "0_netstat/code" (10)
options: "" (11)
mimetype: "bash" (12)
output:
log: "console" (13)
mimetype: "bash"
challenge: (14)
question: "0_netstat/q-info"
codehelp: "0_netstat/codehelp"
codeanswer: "0_netstat/codeanswer"
mimetype: "bash"
points: "1" (15)
output:
log: "ascii"| 1 | LabLearningObject Name |
| 2 | Here you can describe your LLO. Describe what students should know or be able to do at the end of the course that they couldn’t do before! |
| 3 | The ''base url'' field is the landing page of your LLO. |
| 4 | Code spec |
| 5 | Task Name |
| 6 | A Swarmlab service is an dockerized microservice. It receives code snippets to execute, runs these code snippets, and returns the result and output of the execution (Click on Field [Service] will show all available services to use). |
| 7 | Task info. Here you can describe your Task |
| 8 | Set the programming language! |
| 9 | The full path of the executable |
| 10 | Here you can add the code snippet to be executed. ( Using the ''Give It A Try'' option you can test your Code.) |
| 11 | Exec Options |
| 12 | Code block syntax highlighting |
| 13 | Output: Console, file |
| 14 | Challenge spec [Optional] |
| 15 | Difficulty - 1 beginer, 2 intermediate, 3 advanced, 4 expert, 5 guru |
This example LO is organized as follows
/test # The ''base url'' https://git.swarmlab.io:3000 (1)
├── 0_netstat (2)
│ ├── code (3)
│ ├── codeanswer (4)
│ ├── codehelp (5)
│ ├── info (6)
│ └── q-info (7)
└── intro (8)| 1 | Base Directory - based on: LabLearningObject Name. (This Directory Location is the desired URL in ''base_url'' above) |
| 2 | Code spec - based on: display_name |
| 3 | Code snippets to execute |
| 4 | Contain the answer. |
| 5 | Hints. |
| 6 | Task info file. |
| 7 | Question file |
| 8 | Intro file. |
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Swarmlab.io notebooks are organized in files easy to collaborate using version control systems like git. |
Text
Text can be added to swarmlab.io Notebooks using Ascidoctor
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Asciidoctor is a fast text processor and publishing toolchain for converting AsciiDoc content to HTML5, DocBook 5, EPUB3, PDF and other formats. Asciidoctor is the leading implementation of the AsciiDoc syntax, first introduced and implemented in the Python-based AsciiDoc project. |
Why AsciiDoc?
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Standardized format
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The formatting of Asciidoc is standardized so there is only one 'flavor' unlike in Markdown. The definitive user guide is here
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Asciidoctor can convert directly to HTML or DocBook.
AsciiDoc is about being able to focus on expressing your ideas, writing with ease and passing on knowledge without the distraction of complex applications or angle brackets.
AsciiDoc works because:
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It’s readable
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It’s concise
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It’s comprehensive
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It’s extensible
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It produces beautiful output (HTML, DocBook, PDF, ePub and more)
AsciiDoc is easy to write and easy to read (in raw form). It’s also easy to proof and edit. After all, it’s plain text, just like that familiar e-mail.
Best of all, it only requires a text editor to read or write.
AsciiDoc vs Markdown
The defacto lightweight markup language is Markdown. (At least, that’s what you call it at first). The main advantage of Markdown lies in its primitive syntax: its manual and cheatsheet are one and the same. But this advantage is also its greatest weakness.
As soon as authors need something slightly more complex than basic prose (e.g., tables, cross references, footnotes, embedded YouTube videos, etc.), they find themselves resorting to embedded HTML or seeking out more feature-rich implementations. Markdown has become a maze of different implementations, termed “flavors”, which make a universal definition evasive.
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The IETF has declared “there is no such thing as "invalid" Markdown.” See This Is Markdown! Or: Markup and Its Discontents. |
Here’s how the story inevitably goes.
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You start out with Markdown.
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Then it’s Markdown + X.
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Then Markdown + X + Y.
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And down the rabbit hole you go.
What’s worse, X and Y often require you to sprinkle in HTML, unnecessarily coupling content with presentation and wrecking portability. Your instinct to choose Markdown is good. There are just better options.
Code
The Swarmlab Services offers an easy place to run your Source code.
you can create, run and share your live code right away without worrying about the system that’s running
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Swarmlab Learning Object Notebooks are language agnostic |
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As you see it’s really easy to create a LO you may not even need the "Bootstrap LearningObject" menou. Follow the instructions given in the "yaml-index" file and you’ll be ready to start. You can allways create a prototype LO from the menu and edit it later on. |
5. Get Involved
Swarmlab.io is a open source project born to support interactive Virtual Labs and interactive Learning Objects across all programming languages and systems.
Swarmlab.io is open and welcoming to all.
| And we need your help. |
We invite anyone to come and participate in the creation of Swarmlab.io. Regardless of your skillset, we’re sure there is something you can add to our community and project.
5.1. Reporting Security Issues
The Swarmlab.io maintainers take security seriously. If you discover a security issue, please bring it to their attention right away!
| Please DO NOT file a public issue, instead send your report privately to security@swarmlab.io. |
Security reports are greatly appreciated and we will publicly thank you for it, although we keep your name confidential if you request it.
5.2. Reporting other issues
A great way to contribute to the project is to send a detailed report when you encounter an issue. We always appreciate a well-written, thorough bug report, and will thank you for it!
Check that our issue database doesn’t already include that problem or suggestion before submitting an issue. However, if you have ways to reproduce the issue or have additional information that may help resolving the issue, please leave a comment.
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When reporting issues, always include: as much information as you can |
Also include the steps required to reproduce the problem if possible and applicable. This information will help us review and fix your issue faster. Don’t forget to remove sensitive data from your logfiles before posting (you can replace those parts with “REDACTED”). Quick contribution tips and guidelines contributions can make a docker-compose prototype
5.2.1. Creating an issue
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On git.swarmlab.io , navigate to the main page of the feedback repository.
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Under repository name, click Issues.
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Click New issue.
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Type a title and description for your issue.
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When you’re finished, click Submit Create issue.
5.3. Develop swarmlab services
All developers already familiar with docker-compose and that have already set up interesting stacks that may be useful to others, are highly encouraged to add them to the repository and share them. Improvements on the current samples are also very much appreciated!