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Voila – From notebooks to standalone web applications and dashboards

voila.readthedocs.io

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Re: Voila – From notebooks to standalone web applications and dashboards

#61

Post by abdullahkhalids »

ianbooker wrote: post #49
abdullahkhalids wrote: post #42

I have a question about hosting costs - not a SW. Suppose I write some educational Jupyter notebooks, which are not particularly resource intensive, say 100 seconds of compute time per notebook. I host them on some cloud server, using something like OP, and get a 1000 people to learn from it. Maybe they end up using say, 1000 people x 5 notebooks x 100 seconds/run x 20 runs of each notebook = 10 million seconds of co…

We host at this scale on a dedicated server and it is roughly 0.5 Euro per user per month. We provide this no matter the season and usage is very seasonal for us ;) We also provide way more computational bandwidth that would be necessary, so I guess you can provision this for half the cost, just be sure to put out the right restrictions for resource usage.

Thanks for the quantitative answer.

At half cost that's 250 dollars a month, which unfortunately is currently outside our non-profit budget.

Re: Voila – From notebooks to standalone web applications and dashboards

#62

Post by civilized »

BiteCode_dev wrote: post #9
pavlovskyi wrote: post #4

I really like Jupyter notebooks to build a simple concept and then move to .py files. But what I observe, especially at the entry level or junior level jobs in data science is that people spend huge amount of its work on jupyter, which did not focus on how to plan flow properly. What I meant is that there is very short path from usefullness to overkill.

That's because they are not programmers. One should not expect them to be experts in 2 fields. They do their work with the tool provided, and if we want a better output, we need to provide better tooling or accept what comes out. I want my physicists to spend their mental effort on physics, not on software architecture.

Nah, data science is an inherently multidisciplinary topic, and most data scientists aren't domain experts in anything. Data science is mostly valuable to the extent that data scientists learn the full spectrum of skills needed to do their jobs well.

Re: Voila – From notebooks to standalone web applications and dashboards

#63

Post by throwaway2568 »

Voila is quite nice but I find panel [1] is the best option these days. It has plenty of widgets, including those from Voila which can be used as a backend, a few different ways of defining callbacks and has added nice features lately like autoreload if you are using scripts instead of notebooks [2] and new fast HTML elements so it's super easy to define custom widgets straight from the web. They have a discussion page comparing the project to the standard alternatives (dash, streamlit, voila etc) [3]. The docs could do with improving but their discourse is very active [4].

[1] https://panel.holoviz.org/getting_started/index.html

[2] https://github.com/holoviz/panel/pull/1983

[3] https://panel.holoviz.org/about/comparisons.html

[4] https://discourse.holoviz.org/c/panel/5

Re: Voila – From notebooks to standalone web applications and dashboards

#64

Post by r-zip »

d4rkp4ttern wrote: post #50
vladev wrote: post #17

Note that there's also streamlit [1]. It uses regular python files, rather than notebooks, so they can be easily version controlled. And it has more UI tools. [1]: https://streamlit.io/

Streamlit is great for demos but not for building a product.

Notebooks aren't great for building a product, either.

Re: Voila – From notebooks to standalone web applications and dashboards

#65

Post by BiteCode_dev »

misja111 wrote: post #47
BiteCode_dev wrote: post #9

Earlier quoted context omitted.

That's because they are not programmers. One should not expect them to be experts in 2 fields. They do their work with the tool provided, and if we want a better output, we need to provide better tooling or accept what comes out. I want my physicists to spend their mental effort on physics, not on software architecture.

Maybe they are not programmers but they surely work in a team or company where everybody is supposed to work as efficiently as possible together. Simply throwing your unstructured and unreadable code over the wall, with the excuse that it's not your key responsibility so somebody else should fix it, is just bad. It doesn't mean of course that everybody is supposed to be an expert programmer, but a minimum effort to h…

It's not a matter of responsability or will, but of ability. Expecting a fish to fly is a very bad working relationship.

Re: Voila – From notebooks to standalone web applications and dashboards

#66

Post by BiteCode_dev »

civilized wrote: post #62
BiteCode_dev wrote: post #9

Earlier quoted context omitted.

That's because they are not programmers. One should not expect them to be experts in 2 fields. They do their work with the tool provided, and if we want a better output, we need to provide better tooling or accept what comes out. I want my physicists to spend their mental effort on physics, not on software architecture.

Nah, data science is an inherently multidisciplinary topic, and most data scientists aren't domain experts in anything. Data science is mostly valuable to the extent that data scientists learn the full spectrum of skills needed to do their jobs well.

Just like devoos, this is a fallacy. Eventually you can't be an expert in everything. Lost devops are very good at either dev or sysadmin, and not too bad at the other. Same for data scientists.

Re: Voila – From notebooks to standalone web applications and dashboards

#67

Post by dachryn »

abdullahkhalids wrote: post #60
qayxc wrote: post #48

Earlier quoted context omitted.

This could cost anywhere from nothing (e.g. free) to 3-figures (in USD) depending on the specifics. How many users are accessing the notebooks concurrently (e.g. all 1000 or only a dozen at a given time)? Is there any downtime, i.e. do the users come from the same time zone, so that app can have inactive hours (say it's OK to be unreachable during the night)? Depending on the specifics, free hosting may be available…

We run a non-profit minimal-budget workshop where currently hundreds of people work together at the same time, but they run code on their own computer. But making people install Jupyter and other python packages on their computer is difficult. So we are exploring the possibility of the hosting the notebooks ourselves. We don't want options like Google Colab, because we want the experience to be tightly integrated (th…

Would a cloud provider be in the option at all? Zepl is pretty cheap, althoug not jupyter.

Stuff like Google cloud managed notebooks are also pretty cheap, you can create a template, and one-click on demand create, kill it at the end of the day. There is an option for 7 cent/hour per user. 5 hours = roughly 50 cent, so above your budget but soo easy from a management point of view. And infinitely scalable.

You could also start messing indeed by just installing it on a cloud server and only turning it on for those 5 hours you actually need it.

And if you still want to install yourselves, look at tlhj (the littlest jupyter hub). We use that succesfully internally, but it takes a few hours to get everything configured the way you like it. Sill a lot better than actually installing a proper jupyter server imo.

Re: Voila – From notebooks to standalone web applications and dashboards

#68

Post by civilized »

BiteCode_dev wrote: post #66
civilized wrote: post #62

Earlier quoted context omitted.

Nah, data science is an inherently multidisciplinary topic, and most data scientists aren't domain experts in anything. Data science is mostly valuable to the extent that data scientists learn the full spectrum of skills needed to do their jobs well.

Just like devoos, this is a fallacy. Eventually you can't be an expert in everything. Lost devops are very good at either dev or sysadmin, and not too bad at the other. Same for data scientists.

I would call "can only use Jupyter notebooks" very bad at programming, not merely "not too bad". It's well below expectation in my opinion.

Whether you choose to call it "good" or "not too bad", there is a minimum bar of competence that data scientists need to meet in statistics/ML/AI, programming, and their domain of application. And the ability to move from Jupyter notebooks to Python modules/packages is a basic.

Re: Voila – From notebooks to standalone web applications and dashboards

#70

Post by cycomanic »

bluescarni wrote: post #28
BiteCode_dev wrote: post #9

Earlier quoted context omitted.

That's because they are not programmers. One should not expect them to be experts in 2 fields. They do their work with the tool provided, and if we want a better output, we need to provide better tooling or accept what comes out. I want my physicists to spend their mental effort on physics, not on software architecture.

As a scientist myself, I strongly disagree. If you spend a non-negligible amount of your time telling a computer what to do, you are, indeed, a programmer. And as such you should be expected to become a decently proficient programmer. Physicists are not mathematicians, and yet they are required to acquire a relatively high degree of proficiency in maths because maths is a fundamental tool in their job, and nobody wou…

As a fellow scientist I wholeheartedly agree! I find that programming skills (and willingness to improve on them) are strongly correlated to success as a (experimental) PhD student, much higher than mathematical abilities. The ability to automate your experiment or do a quick simulation is a huge productivity boost.

However, the programming education in science degrees is absolutely appalling. Just show them how program a newton raphson method in matlab (without any considerations for performance) and expect them to know how to program.

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