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My two cents: We've been recently working in a FREE hosted version of Jupyter Lab mainly intended for education. Feel free to check it out.

https://notebooks.ai/

Would love to hear some feedback.




Wondering how you are planning to keep it free. Also wondering whether you would possibly consider shifting to Sagemath/CoCalc as a service.


We got support of the local university at my city and we got a bunch of free credits at AWS. Costs are very low and we want to keep it that way so we can support the most students we can with a free access.


What happens when your funding and AWS credits run out?


We might add bigger paid tiers later if we decide to support business usage of it. For now it's only educational and we can deal with the costs, even without the credits. Containers used are small and get shut down on inactivity. So, we only need to care about concurrent users. Hope it makes sense.


> Containers used are small and get shut down on inactivity.

How do you define inactivity? If I do

$ nohup ./computational_intense_and_runs_for_100_hours.py &

Do you just kill the process (or stop the container)? In essence Jupyter is a graphical rich shell, so you providing free *nix machines - don't underestimate how this feature can be exploited (e.g. CoCalc limits at least internet access for free instances).


First, that will use 100% of the CPU quota assigned to your user, which is really small.

Second, yes. The container will be killed after 10min unless we keep detecting activity of your user in the platform. So, basically the rule is: If we don't detect user's activity after 10mins we kill all containers for that user. You could hack this by doing periodical requests to the API to simulate activity, but at some point your JWT will be expired and requests will start failing.

In any case, other students won't be affected at all by the appropriate usage and we will end up banning your account at some point when we detect it.

We also limit the amount of parallel running containers to avoid unlimited containers running at the same time.

Do you see any drawbacks on this implementation? Happy to hear about possible improvements.


> We've been recently working in a FREE hosted version [...]

Oh no, you make it sound like this is a good thing. It only means I can't take you serious.

Don't make it free. That is not a feature for a computation environment, it will only cause headaches on your side and people get wrong (bad) impression about the performance (assuming free accounts get some limited shared instances).

I rather pay a monthly fee for a good application, than a pseudo free instance, where you get limited resources. Do you have your credits forever?


That's a very good point. We wanted to make it super accessible for students using Jupyter. Also, we only support very small containers which of course don't have GPU. It's not mainly intended to be used for business, but for learning purposes.


What would be the difference between using this and something like Google Colab?


We were using Colab and Azure Notebooks before, but we found a few not covered requirements. For example:

* We added a way for students to present their projects / analysis in an static blog-looking version of their notebooks. Here's an example: https://notebooks.ai/martinzugnoni/how-to-trade-bitcoin-with...

They can share that version with classmates, and classmates can "fork" the project and do their own changes to it.

* Also, we added a custom JupyterLab extension called "solutions" that allow the teacher to mark certains parts of the notebooks as assignment solution and it will be hidden for the student until they decide to reveal it. Here's how it works:

(teacher) https://user-images.githubusercontent.com/7065401/50402147-1...

(student) https://user-images.githubusercontent.com/7065401/50402146-1...

We now have the ability to keep adding educational relates stuff to it, without depending on Google or Microsoft, that are not focussed on this space.




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