I spent my not-OU day today battling with trying to bundle up a dockerised VM, going round in circles trying simplify things a bit, and getting confused by docker-compose not working quite so well following an upgrade.
I think there’s still some weirdness going on (eg in docker-ui showing messed container names?) but I’m now way too confused to care or try to unpick it…
I also spent a chunk of time considering the 32 bit problem, but got nowhere with it…. Docker is predominantly a 64 bit thing, but the course has decided in it’s wisdom that we have to support 32 bit machines, which means I need to find a way of getting a 32 bit version of docker into a base box (apt-get install docker.io I think?), finding way of getting the vagrant docker provisioner to use it (would an alias help?), and checking that vagrant-docker-compose works in a 32 bit VM, then tracking down 32 docker images for PostgreSQL, MongoDB, dockerUI and OpenRefine (or finding build files for them so I can build my own 32 bit images).
We then need to be able to test the VM in a variety of regimes: 32 bit O/S on a 32 bit machine, 32 bit O/S on a 64 bit machine, 64 bit O/S on a 64 bit machine, with a variety of hardware virtualisation settings we might expect on students’ machines. I’m on a highly specced Macbook Pro, though, so my testing is skewed…
And I’m not sure I have it in me to try to put together 32 bit installs…:-( Perhaps that’s what LTS are for…?!;-)
(I keep wondering if we could get access to stats about the sorts of machines students are using to log in to the OU VLE from the user-agent strings of their browsers that can be captured in webstats? And take that two ways: 1) look to see how it’s evolving over time; 2) look to see what the profile of machines is for students in computing programmes, particular those coming up to level 3 option study? That’s the sort of pratical, useful data that could help inform course technology choices but that doesn’t have learning analytics buzzword kudos or budget attached to it though, so I suspect it’s not often championed…)
When LTS was an educational software house, I think there was also more opportunity, support and willingness to try to explore what the technology might be able to do for us and OUr students? Despite the continual round of job ads to support corporate IT, I fear that exploring the educational uses of software has not had much developer support in recent years…
As an example of the sort of thing I think we could explore – if only we could find a forum to do so – is the following docker image that contains an OU customised IPython notebook: psychemedia/ouflpy_scipystacknserver
The context is a forthcoming FutureLearn course on introductory programming. We’re currently planning on getting students to use Anaconda to run the IPython Notebooks that provide the programming environment for the course, but I idly wondered what a Kitematic route might be like. (The image is essentially the scipystack and notebook server with a few notebook extensions and OU customisations installed.)
There are some sample (testing) notebooks here that illustrate some of the features.
Here’s the installation recipe:
– download and unzip the notebooks (double click the downloaded file) and keep a note of where you unzipped the notebook directory to.
– download and install Kitematic. Ths makes use of docker and Virtualbox – but I think it should install them both for you if you don’t already have them installed.
– start Kitematic, search for psychemedia/ouflpy_scipystacknserver and create an application container.
It should download and start up automatically.
When it’s running, click on the Notebooks panel and Enable Volumes. This allows the container to see a folder on your computer (“volumes” are a bit like folders that can be aliased or mapped on to other folders across devices).
Click the cog (settings) symbol in the Notebooks panel to get to the Volumes settings. Select the directory that you created when you unzipped the downloaded notebooks bundle.
Click on the Ports tab. If you click on the link that’s displayed, it should open an IPython notebook server homepage in your browser.
Here’s what you should see…
Click on a notebook link to open the notebook.
The two demo notebooks are just simple demonstrations of some custom extensions and styling features I’ve been experimenting with. You should be able to create you own notebooks, open other people’s notebooks, etc.
You can also run the container in the cloud. Tweak the following recipe to try it out on Digital Ocean: Getting Started With Personal App Containers in the Cloud or Running RStudio on Digital Ocean, AWS etc Using Tutum and Docker Containers. (That latter example you could equally well run in Kitematic – just search for and install rocker/rstudio.)
The potential of using containers still excites me, even after 6 months or so of messing around the fringes of what’s possible. In the case of writing a new level computing course with a major practical element, limiting ourselves to a 32 bit build seems a backward step to me? I fully appreciate the need to to make our courses as widely accessible as possible, and in an affordable a way as possible (ahem…) but here’s why I think supporting 32 bit machines in for a new level 3 computing course is a backward step.
In the first case, I think we’re making life harder for OUrselves. (Trying to offer backwards compatibility is prone to this.) Docker is built for 64 bit and most of the (reusable) images are 64 bit. If we had the resource to contribute to a 32 bit docker ecosystem, that might be good for making this sort of technology accessible more widely internationally, as well as domestically, but I don’t think there’s the resource to do that? Secondly, we arguably worsen the experience for students with newer, more powerful machines (though perhaps this could be seen as levelling the playing field a bit?) I always liked the idea of making use of progressive enhancement as a way of trying to offer the best possible experience using the technology they have, though we’d always have to ensure we weren’t then favouring some students over others. (That said, the OU celebrates diversity across a whole range of dimensions in every course cohort…)
Admittedly, students on a computing programme may well have bought a computer to see them through their studies – if the new course is the last one they do, that might mean the machine they bought for their degree is now 6 years old. But on the other hand, students buying a new computer recently may well have opted for an affordable netbook, or even a tablet computer, neither of which can support the installation of “traditional” software applications.
The solution I’d like to explore is a hybrid offering, where we deliver software that makes use of browser based UIs and software services that communicate using standard web protocols (http, essentially). Students who can install software on their computers can run the services locally and access them through their browser. Students who can’t install the software (because they have an older spec machine, or a newer netbook/tablet spec machine, or who do their studies on a public access machine in a library, or using an IT crippled machine in their workplace (cough, optimised desktop, cOUgh..) can access the same applications running in the cloud, or perhaps even from one or more dedicated hardware app runners (docker’s been running on a Raspberry Pi for some time I think?). Whichever you opt for, exactly the same software would be running inside the container and exposing it in the same way though a browser… (Of course, this does mean you need a network connection. But if you bought a netbook, that’s the point, isn’t it?!)
There’s a cost associated with running things in the cloud, of course – someone has to pay for the hosting, storage and bandwidth. But in a context like FutureLearn, that’s an opportunity to get folk paying and then top slice them with a (profit generating…) overhead, management or configuration fee. And in the context of the OU – didn’t we just get a shed load of capital investment cash to spend on remote experimentation labs and yet another cluster?
There are also practical consequences – running apps on you own machine makes it easier to keep copies of files locally. When running in the cloud, the files have to live somewhere (unless we start exploring fast routes to filesharing – Dropbox can be a bit slow at synching large files, I think…)
Anyway – docker… 32 bit… ffs…
If you give the container a go, please let me know how you get on… I did half imagine we might be able to try this for a FutureLearn course, though I fear the timescales are way too short in OU-land to realistically explore this possibility.