Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

What’s wrong with computational notebooks?

web.eecs.utk.edu

Post reply on HN
Print view View original

21–30 of 223 posts

Re: What’s wrong with computational notebooks?

#21

Post by roblabla »

ivalm wrote: post #17
moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

If the interpreter state contains large variables checkpointing might not be viable (eg I have dataframes that are 100s of GB/large fractions of total available memory, reading/writing from hard drive all the time would be relatively slow. If you can save deltas I guess it wouldn't be too space inefficient but I imagine still slow). At the same time, I do like the idea of an append only notebook where you can: 1. Onl…

Assuming the data isn't changed, thanks to CoW forking wouldn't cause any extra memory usage. If only a subset of data is changed, same thing - only the changed cells will take extra space. The problem only occurs when the whole variable changes - in which case yeah, you're SOL. I wonder what the usage patterns are for such datasets?

Re: What’s wrong with computational notebooks?

#22

Post by ivalm »

roblabla wrote: post #21
ivalm wrote: post #17

Earlier quoted context omitted.

If the interpreter state contains large variables checkpointing might not be viable (eg I have dataframes that are 100s of GB/large fractions of total available memory, reading/writing from hard drive all the time would be relatively slow. If you can save deltas I guess it wouldn't be too space inefficient but I imagine still slow). At the same time, I do like the idea of an append only notebook where you can: 1. Onl…

Assuming the data isn't changed, thanks to CoW forking wouldn't cause any extra memory usage. If only a subset of data is changed, same thing - only the changed cells will take extra space. The problem only occurs when the whole variable changes - in which case yeah, you're SOL. I wonder what the usage patterns are for such datasets?

Personal experience: when first looking at the data I often do lots of map /reduce style operations which might transform large portions of the dataframe.

Question, if you use CoW then presumably your variable blocks are no longer contiguous, wouldn't this really slow down vector operations?

Re: What’s wrong with computational notebooks?

#23

Post by mumblemumble »

ivalm wrote: post #17
moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

If the interpreter state contains large variables checkpointing might not be viable (eg I have dataframes that are 100s of GB/large fractions of total available memory, reading/writing from hard drive all the time would be relatively slow. If you can save deltas I guess it wouldn't be too space inefficient but I imagine still slow). At the same time, I do like the idea of an append only notebook where you can: 1. Onl…

I want a notebook situation where the platform understand sampling, so that, while I'm doing my EDA and initial development and generally doing the kinds of work that are appropriate to do in notebook, I'm never working with 100GB data frames.

I suspect that a big part of my annoyance about the current state of the data space is that parts of the ecosystem were designed with the needs of data scientists in mind, and other parts of the ecosystem were designed with the needs of data engineers in mind, and it's all been jammed together in a way that makes sure nobody can ever be happy.

Re: What’s wrong with computational notebooks?

#24

Post by willj »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

Might be worthy trying out nodebook [1] which at least enforces the forward directionality you mentioned.

Also polynote by Netflix, as a user below mentioned.

[1] https://github.com/stitchfix/nodebook

Re: What’s wrong with computational notebooks?

#25

Post by fhennig »

stared wrote: post #6

(A frequent Jupyter Notebook user here. For data exploration, and teaching deep learning - then Colab is indispensable.) The main question is: what are the alternatives, for data exploration (and sharing its results). Similarly, for data science tool demos, Notebooks shine. IMHO the problem is not in the notebooks, but in how they are being used (i.e. the workflow). By writing scripts in py files, and using notebooks…

Yes, the article mentions users copy pasting snippets from their personal "library". Well, that could just be made into an actual library of functions to call.

I'm currently at uni enrolled in an AI/ML degree, and there are a lot of people with no previous exposure to programming. It's just that most people don't know that these things are possible, don't want to learn another tool (IDE) and are not interested in longevity of the code, just in the results. This shouldn't sound like me complaining, I totally understand. I think a lot of the stuff could be solved with just better tooling, but a familiarity with software development is definitely helpful.

Also a while back streamlit (https://www.streamlit.io/) was here on HN and since then I've been meaning to try it. I think this could be a good approach to bring together the best of both worlds.

Re: What’s wrong with computational notebooks?

#26

Post by fryguy »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

My problem with notebooks is that I feel like the natural mental model for them is a spreadsheet mental model, not a REPL mental model. Under that assumption, changing a calculation in the middle means that all of the cells that depend on that calculation would be updated, but instead you need to go and manually re-run the cells after it that depend on that calculation (or re-run the entire notebook) to see the effect on later things. Keeping track of the internal state of the REPL environment is tricky, and my notebooks have usually just ended up being convenient REPL blocks rather than a useful notebook since that's the workflow it emphasizes.

Re: What’s wrong with computational notebooks?

#27

Post by ivalm »

mumblemumble wrote: post #23
ivalm wrote: post #17

Earlier quoted context omitted.

If the interpreter state contains large variables checkpointing might not be viable (eg I have dataframes that are 100s of GB/large fractions of total available memory, reading/writing from hard drive all the time would be relatively slow. If you can save deltas I guess it wouldn't be too space inefficient but I imagine still slow). At the same time, I do like the idea of an append only notebook where you can: 1. Onl…

I want a notebook situation where the platform understand sampling, so that, while I'm doing my EDA and initial development and generally doing the kinds of work that are appropriate to do in notebook, I'm never working with 100GB data frames. I suspect that a big part of my annoyance about the current state of the data space is that parts of the ecosystem were designed with the needs of data scientists in mind, and…

You can sample data if you want already (or sequentially load partial data, which is what I usually do if I just want to test basic transformations), but if you need to worry about rare occurrences (and don't know the rate) then sampling can be dangerous. For example, when validating data there are edge cases that are very rare (ie sometimes I catch issues that are less than one record per billion), it can be hard to catch them without looking at all of the data.

Re: What’s wrong with computational notebooks?

#28

Post by hinkley »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

A couple years out of college we finally took a hard look at the credit cards and realized we had fucked up.

We were gonna buckle down, pay the cards down hard for a while, 'color' our money so we both had discretionary spending separate from, say, the power bill. She had much more Excel experience than I did so she worked up a spreadsheet.

It was bad. We had worked up some 'fair' notion of proportionality and she basically had no spending money and mine was pretty bleak. So I redid the numbers from scratch with split that was better for her. In the new spreadsheet she has much more spending money and... hold on, I've got a bit more too? I looked at her spreadsheet repeatedly and I never did figure out where a couple hundred bucks got lost. I went back to sanity checking mine instead to make sure I wasn't wrong. It checked out.

I wonder sometimes how often small companies discover they've been running in the red instead of the black, because some cell got zeroed out, a sum didn't cover an entire column, or embezzlement is encoded straight into the spreadsheet without anyone noticing.

There's gotta be a better way.

Re: What’s wrong with computational notebooks?

#29

Post by karlicoss »

stared wrote: post #6

(A frequent Jupyter Notebook user here. For data exploration, and teaching deep learning - then Colab is indispensable.) The main question is: what are the alternatives, for data exploration (and sharing its results). Similarly, for data science tool demos, Notebooks shine. IMHO the problem is not in the notebooks, but in how they are being used (i.e. the workflow). By writing scripts in py files, and using notebooks…

I recommend nbstripout https://github.com/kynan/nbstripout

It eases most of the pain regarding version control. You can use it as a 'git filter', so only inputs would be shown in diffs and committed (and also works with interactive adding!), while keeping outputs in your working tree.

Re: What’s wrong with computational notebooks?

#30

Post by autokad »

azhenley wrote: post #10

Co-author of the study here. Let me know if you have any questions or how you overcome some of the problems we identified!

As a data scientist, I used all of the notebooks and didn't find any of the problems listed with databricks.

I don't get to use it in my current role, miss it a lot.

Post reply on HN