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Launch HN: Replicate (YC W20) – Version control for machine learning

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Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#51

Post by breck »

Jugurtha wrote: post #48
breck wrote: post #11

nit: "Throw away your spreadsheet" scares me a little. I love spreadsheets, and think there are 100x+ more users of spreadsheets than notebooks (though the overlap of notebook users and ML users is probably close to 1, so I see your point). I would always save my experiment results so they were ready to analyze in spreadsheets (and other vis tools).

Do you not think that using spreadsheets for ML experiment tracking is a symptom of broken tooling? I'm asking because one of the reasons we're building our platform[0] with automatic experiment tracking, collaborative notebooks, and a bunch of things, is because experiment tracking was inconsistent between team members. Differences in tools used: (spreadsheets, flat files, logs, pen and paper, human memory). Forgett…

> Once you do that, the "spreadsheet" can just be a "view" of the underlying data. Something you can export as, but not the thing itself.

Yes, and sorry because rereading my comment I wasn't clear, but this is what I meant. I view Spreadsheets as the view (but also an editor of the view), but I don't mean people should be working with XLS files (TSVs seem to work the best— I personally hate tabs but may have lost that battle). I do think though of Spreadsheets as the primary view, and so always design my data structures with the understanding of "how will this interoperate with spreadsheets". JSON is the pits.

IMO 2-D DSLs with Spreadsheets as the primary view/editor paradigm are the future.

I think Spreadsheets are 1, if not 2 OOM better than notebooks for doing actual work (notebooks can be good for presenting results in a narrative):

    - non-linear for both humans and machines (allows for really creative and fast out-of-order parsing techniques on the machine side)
    - concise signal with high information density
    - unlimited cursors
    - fantastically easier for version control and multi-player experiences
I maintain a list of all data science tools to try and stay on top of best practices, and when I see a tool is notebook first, I think "good, less work for me to track this one because they aren't getting the core things right yet." (Though often times notebooks will have really innovative orthogonal features).

Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#52

Post by Jugurtha »

breck wrote: post #51
Jugurtha wrote: post #48

Earlier quoted context omitted.

Do you not think that using spreadsheets for ML experiment tracking is a symptom of broken tooling? I'm asking because one of the reasons we're building our platform[0] with automatic experiment tracking, collaborative notebooks, and a bunch of things, is because experiment tracking was inconsistent between team members. Differences in tools used: (spreadsheets, flat files, logs, pen and paper, human memory). Forgett…

> Once you do that, the "spreadsheet" can just be a "view" of the underlying data. Something you can export as, but not the thing itself. Yes, and sorry because rereading my comment I wasn't clear, but this is what I meant. I view Spreadsheets as the view (but also an editor of the view), but I don't mean people should be working with XLS files (TSVs seem to work the best— I personally hate tabs but may have lost tha…

I think we may be talking about different things. My first reply was based on my understanding you saved results in spreadsheets.

breck wrote:

>I would always save my experiment results so they were ready to analyze in spreadsheets (and other vis tools).

I should have elicited further before making assumptions. My assumption was that you were referring to results of training machine learning models. Is this correct?

If my assumption is correct, how do you go about training machine learning models?

Also, one question concerning your "scare" of "Throw away your spreadsheets"... Do you mean that you'd like a tool that exports results to spreadsheets or something in that direction?

I think we are addressing different problems, and a large part of that is due to assumptions I have made earlier.

Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#53

Post by breck »

Jugurtha wrote: post #52
breck wrote: post #51

Earlier quoted context omitted.

> Once you do that, the "spreadsheet" can just be a "view" of the underlying data. Something you can export as, but not the thing itself. Yes, and sorry because rereading my comment I wasn't clear, but this is what I meant. I view Spreadsheets as the view (but also an editor of the view), but I don't mean people should be working with XLS files (TSVs seem to work the best— I personally hate tabs but may have lost tha…

I think we may be talking about different things. My first reply was based on my understanding you saved results in spreadsheets. breck wrote: > I would always save my experiment results so they were ready to analyze in spreadsheets (and other vis tools). I should have elicited further before making assumptions. My assumption was that you were referring to results of training machine learning models. Is this correct?…

> My assumption was that you were referring to results of training machine learning models. Is this correct?

I would set my hyper params in a spreadsheet, which would kick off training runs on a cluster, and report the results back in the spreadsheet hours/days later(really TSVs, but my UI was a spreadsheet). And repeat.

This was primitive stuff, and haven't done DL in a while, so not even sure if hyperparam tuning is still a thing or if that is all automated now.

Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#54

Post by Jugurtha »

breck wrote: post #53
Jugurtha wrote: post #52

Earlier quoted context omitted.

I think we may be talking about different things. My first reply was based on my understanding you saved results in spreadsheets. breck wrote: > I would always save my experiment results so they were ready to analyze in spreadsheets (and other vis tools). I should have elicited further before making assumptions. My assumption was that you were referring to results of training machine learning models. Is this correct?…

> My assumption was that you were referring to results of training machine learning models. Is this correct? I would set my hyper params in a spreadsheet, which would kick off training runs on a cluster, and report the results back in the spreadsheet hours/days later(really TSVs, but my UI was a spreadsheet). And repeat. This was primitive stuff, and haven't done DL in a while, so not even sure if hyperparam tuning i…

How did you do that? Were you using some kind of hooks or polling for the spreadsheet state and triggering training jobs whenever it had changed?

Also, how did you deal with changing which hyperparameters you used or which algorithms you used? Did you make a spreadsheet per project per model?

Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#55

Post by bfirsh »

mlthoughts2018 wrote: post #37
bfirsh wrote: post #35

Earlier quoted context omitted.

Yeah, I agree this space is crowded. But we’ve found so few ML researchers/engineers are actually using these tools. This could either be that people aren’t aware of them yet, or that they’re not good enough. I think it’s a mix of both, honestly, but we’re betting that there’s more of the latter in the mix. :) I could do comparisons of each of these tools, and some of them are solving quite different problems, but th…

Can you share any of the data or market research on this? I am an ML manager in a large ecommerce firm and we stand up our own feature store, experiment tracking system and model training diagnostic / metric system (a la Tensorboard). It is exceedingly easy to DIY, I’ve been doing that stuff DIY with teams of less than 8 engineers for many years. What I’ve seen is that most vendor solutions aren’t flexible enough. Ma…

Sorry yeah -- not saying nobody's using MLflow. We see teams and organizations using MLflow. What we don't see though is individual researchers/engineers/data scientists pick it up and use it.

From the people we have talked to who use MLflow, we hear it gets the job done, but individual contributors don't love it.

We really believe that widespread adoption comes from making something individuals love and use every day. That's the reason Docker was so successful, for example.

The lack of flexibility really resonates. That's the reason we're trying to be small and not too opinionated. We're something we can drop into your in-house system as a component, kinda like lots of deployment systems are built around Docker.

Re: Launch HN: Replicate (YC W20) – Version control for machine learning

#56

Post by breck »

Jugurtha wrote: post #54
breck wrote: post #53

Earlier quoted context omitted.

> My assumption was that you were referring to results of training machine learning models. Is this correct? I would set my hyper params in a spreadsheet, which would kick off training runs on a cluster, and report the results back in the spreadsheet hours/days later(really TSVs, but my UI was a spreadsheet). And repeat. This was primitive stuff, and haven't done DL in a while, so not even sure if hyperparam tuning i…

How did you do that? Were you using some kind of hooks or polling for the spreadsheet state and triggering training jobs whenever it had changed? Also, how did you deal with changing which hyperparameters you used or which algorithms you used? Did you make a spreadsheet per project per model?

I forget the exact details, but my guess is I made my own DSLs and then "executed" them.

Something like:

https://jtree.treenotation.org/designer/#grammar%0A%20inferr......

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