Some Recent Noticings From the Jupyter Ecosystem

Over the last couple of weeks, I’ve got back into the speaking thing, firstly at an OU TEL show’n’tell event, then at a Parliamentary Digital Service show’n’tell.

In each case, the presentation was based around some of the things you can do with notebooks, one of which was using the RISE extension to run a notebook as an interactive slideshow: cells map on to slides or slide elements, and code cells can be executed live within the presentation, with any generated cell outputs being displayed in the slide.

RISE has just been updated to include an autostart mode that can be demo’ed if you run the RISE example on Binderhub.

Which brings me to Binderhub. Originally know as MyBinder, Binderhub takes the MyBinder idea of building a Docker image based on the build specification and content files contained in a public Github repository, and launching a Docker container from that image. Binderhub has recently moved into the Jupyter ecosystem, with the result that there are several handy spin-off command line components; for example, jupyter-repo2docker lets you build, and optionally push and/or launch, a local image from a Github repository or a local repository.

To follow on from my OU show’n’tell, I started putting together a set of branches on a single repository (psychemedia/showntell) that will eventually(?!) contain working demos of how to use Jupyter notebooks as part of “generative document” workflow in particular topic areas. For example, for authoring texts containing rich media assets in a maths subject area, or music. (The environment I used for the shown’n’tell was my own build (checks to make sure I turned that cloud machine off so I’m not still paying for it!), and I haven’t got working Binderhub environments for all the subject demos yet. If anyone would like to contribute to setting up the builds, or adding to subject specific demos, please get in touch…)

I also prepped for the PDS event by putting together a Binderhub build file in my psychemedia/parlihacks repo so (most of) the demo code would work on Binderhub. I think the only think that doesn’t work at the moment is the Shiny app demo? This includes an RStudio environment, launched from the Jupter notebooks New menu. (For an example, see the binder-examples/dockerfile-rstudio demo.)

So – long and short of that – you can create multiple demo environments in a single Github repo using a different branch for each demo, and then launch them separately using Binderhub.

What else…?

Oh yes, a new extension gives you a Shiny like workflow for creating simple apps from a Jupyter notebook: appmode. This seems to complement the Jupyter dashboards approoach, by providing an “app view” of a notebook that displays the content of markdown cells and code cell outputs, but hides the code cell contents. So if you’e been looking for a Jupyter notebook equivalent to R/shiny app development, this may get you some of the way there… (One of the nice things about the app view is that you can easily “View Source” – and modify that source…)

Possibly related to the appmode way of doing things, one thing I showed in the PDS show’n’tell was how notebooks can be used to define simple API services using the jupyter/kernel_gateway (example). These seem to run okay – locally at least – inside Binderhub, although I didn’t try calling a Jupyter API service from outside the container. (Maybe they can be made publicly available via the jupyterhub/nbserverproxy? Why’s this relevant to appmode? My thinking is architecturally you could separate out concerns, having one or more notebooks running an API that is consumed from the appmode notebook?

Another recent announcement came from Google in the form of Colaboratory, a “research project created to help disseminate machine learning education and research”. The environment is “a Jupyter notebook environment that requires no setup to use”, although it does require registration to run notebook cells, and there appears to be a waiting list. The most interesting thing, perhaps, is the ability to collaboratively work on notebooks shared with other people across Google Drive. I think this is separate from the jupyterlab-google-drive initiative, which is looking to offer a similar sort of shared working, again through Google Drive?

By the by, it’s probably also worth noting that other big providers make notebooks available, such as Microsoft (notebooks.azure.com) and IBM (eg datascientistworkbench.com, cognitiveclass.ai; digging around, cognitiveclass.ai seems to be a rebranding of bigdatauniversity.com).

There are other hosted notebook servers relevant to education too: CoCalc (previously SageMathCloud) offers a free way in, as does gryd.us if you have a .edu email address. pythonanywhere.com/ offers notebooks to anyone on a paid plan.

It also seems like there are services starting to appear that offer free notebooks as well as compute power for research/scientific computing on a model similar to CoCalc (free tier in, then buy credits for additional services). For example, Kogence.

For sharing notebooks, I also just spotted Anaconda Cloud, which looks like it could be an interesting place to browse every so often…

Interesting times…

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