[D] What platform environment would you use for young Python ML learners?

Submitted by jshkk t3_yeckvk in MachineLearning

EDIT: Thanks for all the constructive feedback! Seems like Colab might still be reasonable even after the business model change, and one could have Binder as a backup (or vice versa). Appreciate the idea sharing!

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I'm considering helping out a nonprofit with a summer camp that introduces some early Python ML ideas to high school students. Given that Google Colab now essentially doesn't have a "free" version, what would you fine folks use?

In educational settings like this, one of the key things is reduced complexity. Colab was great for that in that many libraries were already installed and you're pulling up a notebook immediately. Whereas one could theoretically set up a number of computers for this, that's not always viable. And like many things in education, cost is typically prohibitive. Even harder in the educational setting is students having their own accounts, which I'll leave off as a worry for now (but I mention it on the off-chance).

I should mention, in my case at least, I don't need any GPU hours at all. Just small 100Kish or so datasets and standard models. Maybe Colab somehow still works for that if not using GPU, but their model is unclear to me there.

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zyl1024 t1_itx9pwi wrote

Have you tried colab recently? I found it quite handy, especially if I don't need GPU.

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jshkk OP t1_itx9xek wrote

Replying to zyl1024 (#248,085)

I've still got a Pro subscription that needs to time out to see what it's like without it, but doesn't it still use compute units if there's no GPU? And wouldn't users still need to buy a first set of them if so?

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iPlayWithWords13 t1_itxahqy wrote

Replying to jshkk (#248,104)

So I was just tutoring some kids for the first time today and we tried out colab. I signed in with my Gmail account (no credit card info attached to it) and we were able to start coding immediately so I'd assume the free tier is fine. We were doing a computer vision project as well, so computationally, it wasn't nothing.

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jshkk OP t1_itxarm7 wrote

Replying to iPlayWithWords13 (#248,169)

That's good to know! My guess is then that it just becomes a "resources aren't guaranteed" thing? That's... tricky if so since you wouldn't want a student getting stuck mid day colab not serving up another/longer session. Curious if anyone knows more on that?

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iPlayWithWords13 t1_itxazd6 wrote

Replying to jshkk (#248,196)

That was my understanding of it. I can't imagine you'd ever run into a resource issue though unless you were running some intense models. I'd imagine there's more info out there on the limitations though.

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Randomramman t1_itxh3yu wrote

Binder is designed to solve this exact problem! It’s been super helpful for tutorials I’ve attended at conferences. https://mybinder.org

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mlguy314 t1_itxhtqy wrote

Check out deepnote. They have an actually free tier, with which you and two other people can be on the same notebook at once. It’s like google docs but for notebooks. I’ve used it to teach data science courses, and it’s been really helpful. You can also change your compute if needed. Integration with plenty of services included.

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Pitiful-You-8410 t1_ityfbfu wrote

Are you sure collab is not free ? I use collab free verson. It is decent enough. more polished than Jupyter Notebooks.

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jshkk OP t1_itz964u wrote

Replying to Pitiful-You-8410 (#253,202)

I didn't really understand how things changed after their new business model move, but just got someone with a fresh account to try it out last night, and they were fine without any compute units so that's good! What I worry about though is that with them having a pay-as-you-go model now, free might have more disruptions to service, which wouldn't work in an educational environment. Would be nice to know how likely that is

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jshkk OP t1_iu0824m wrote

Replying to [deleted] (#260,908)

This reads weirdly hostile. The fact of the matter is that there *are* free services online more generous than your claims, some of which have been posted here already, and which provide alternative ways to think about it, or backups in case Colab for whatever reasons fell through.

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Synthetic-Charm t1_iu0ewlr wrote

we should make a chrome browser for codex and openai. then you can just call any library you want anywhere. jupyter piece by piece or anything. but some things take learning sometimes.

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knowledgebass t1_iu0ot25 wrote

I don't know what it costs but we have used a notebook-based platform called CoCalc which I thought was really solid.

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knowledgebass t1_iu0p9jh wrote

Replying to [deleted] (#254,763)

The vast majority of machine learning topics can be covered without needing GPU compute, with the exception of neural nets and deep learning. That's like a course unto itself though. If model complexity and dataset sizes are kept reasonable then CPUs should be fine.

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knowledgebass t1_iu0qcqe wrote

Might I suggest reducing your dataset sizes by about an order of magnitude to ~10k or even less? Less than 1000 records would be ideal.

If it is a learning environment, you want a pretty quick turnaround on training models, say less than 30 seconds. Of course, it can take much (much) longer on actual production systems with huge training sets, but it is going to be frustrating for students if they have to wait minutes for their models to train. I'd test this beforehand and make sure that they won't get bogged down by this. (Plenty of small ML datasets out there which are still interesting and instructive.)

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rehrev t1_iu147sd wrote

I think getting used to an IDE with an introduction to debiggin from the very beginning is very useful. Depends on your time frame though, because it would make things considerably more complicated.

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jshkk OP t1_iu1go4z wrote

Replying to rehrev (#265,935)

Time's short so I personally would be sticking with notebook style environments, but for a longer course I think more exposure to diversity would be good

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