It’s a good experience, but a motivated student who’s good at charging through docs can do it on their own. Having to manage that for a class that’s intended to teach conceptual material would be a big time-sink.

Its a valid point to consider that as part of the learning experience. I don't think that students with limited time should be forced to go through that though. If you get them hooked on to computing, their natural curiosity would lead them to explore it further. Bogging them down with unnecessary setup stuff would probably only make them think that this shit takes way too much effort etc.

I've had many courses that were bogged down by software setup issues in college; I would rather than not be the case.

Same here.

I want to say that, if it's pedagogically valuable, then it needs to be made into a small lab course (or part of the lab unit for an intro class), and taught once, in an organized manner.

And then stop letting professors hide behind this lame excuse so that they can get on with teaching the stuff that their course is actually about.

Agreed. At the beginning of class, walk students through the setup. Then for every project after, let them use the pre-rolled systems.

I'd actually do the opposite. Let them use pre-rolled first, then when they actually know and care about how the system is set up, have them set it up the way they like it.

(I am actually leading a machine learning for high-schoolers camp in 2 weeks and we are using Jupyter notebooks so that all students, with heterogeneous backgrounds, will start in the same place and get to the fun stuff fast. Many will never have used Python and will not know or care about 2.7 vs 3, just to give the most high-level and basic example!)

I wish I had ML camps growing up! Mine were photography and adobe flash :<

Setup is orthogonal to understanding and learning, leave it for extra credit or an optional follow up exercise. It will lose or distract many students.

I think at the very least they should be able to see someone do it so they know what steps are involved. Possibly a video with steps but stated that it isn't the focus of the class.

Depends what that particular course is supposed to teach. Stats, maybe some sort of intro course, or a programming course intended for non-CS majors are all courses where it could make sense to abstract away setting up complex environments -- just like how, in the business world, companies (SAS, etc) make tons of money abstracting away complex environments so businesses can have their employees focus on what provides value in that context.

The majority of the courses that utilize JupyterHub at the moment involve some kind of stats work. They use JupyterHub to ease people with little technical experience into using stats libraries and coding generally, with the aim of giving them a high-level knowledge of CS principles and techniques.

An exception would be the example of the deep learning project work. In this case JupyterHub was utilized as an easy way to deploy a centrally managed, cost effective environment for a large class to use GPU resources without the risk of running up huge AWS costs for each student.

It's Harvard. They'll figure it out if push comes to shove.