amakelov

6 ms·

1. 1.▲
   Late Takes on OpenAI O1(alexirpan.com)

   4 points by amakelov 2y ago|0 comments
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   by amakelov 2y ago

   Thanks! And thanks for sharing the pointer - I think I've seen `mltrace` at some point in the past. The tool has some similarities, but seems different from `mandala` on a philosophical level - `mltrace` seems more opinionated and doma
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   by amakelov 2y ago

   Thanks Rachit! Great running into you after all these years! Being aware of types is certainly a must in a more performance-critical implementation; this project is not at this stage though, opting for simplicity and genericity instead. I&#
4. 4.▲
   by amakelov 2y ago

   Forgot to mention: yes, the dependency tracking is transitive, i.e. if your @op calls a @track-decorated function, which in turn calls another @track-decorated function, then both dependencies will show up, etc.
5. 5.▲
   by amakelov 2y ago

   Thanks for sharing! This is a great project. It is quite close to the memoization part of `mandala` and I'll add it to the related work in the README. I think the similarities are: - using `joblib` to hash arbitrary objects (which is a
6. 6.▲
   by amakelov 2y ago

   Great question! The versioning system does something essentially equivalent to what you describe. It currently works as follows: - When a call to an `@op` is executed, it keeps a stack of @track-decorated functions that are called (you can
7. 7.▲
   by amakelov 2y ago

   Thanks! Yes I think a caching solution like this is great for notebooks, because it makes it very cheap to re-run the whole thing (as long as you reasonably organized long-running computations into `@op`s), overcoming the notorious state pr
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   by amakelov 2y ago

   Oh also totally missed the Borges mention the first time - I'm a big fan of his stories!
9. 9.▲
   by amakelov 2y ago

   Thanks! Indeed, despite the fact that the main goal is to track ML experiments, the approach taken in `mandala` has a lot in common with e.g. time-travel debugging ( https://en.wikipedia.org/wiki/Time_travel_debugging ).
10. 10.▲
    by amakelov 2y ago

    This blog port gives an overview of the core dependency tracking logic: https://amakelov.github.io/blog/deps/
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    by amakelov 2y ago

    Ah, yes, the notorious state problem in notebooks. In your project, do you find the dependencies statically or dynamically?
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    by amakelov 2y ago

    Great question - personally, I mostly use it from notebooks, and I think it's a great fit for that. Bundling experiment tracking with incremental computation makes a lot of sense in a notebook (or any other interactive) environment, be
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    by amakelov 2y ago

    Thanks! Indeed, the ultimate (but very ambitious from the point of view of coordination and infrastructure) vision would be to build the "planetary computer" where everyone can contribute and every computation is transparently rep
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    by amakelov 2y ago

    In order, 1. Yes, you can choose to create a persistent storage by passing `db_path` to `Storage()`. The current implementation is just an SQLite file. To run on many machines, you don't really need to be able to re-import from a dataf
15. 15.▲
    Show HN: Mandala – Automatically save, query and version Python computations(github.com)

    100 points by amakelov 2y ago|30 comments
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    Tidy Computations(amakelov.github.io)

    2 points by amakelov 2y ago|0 comments
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    by amakelov 3y ago

    This is neat and self-contained! But as someone running experiments with a high degree of interactivity, I often have an orthogonal requirement: add more computations to the same cell without recomputing previous computations done in the
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    by amakelov 3y ago

    I see two concerns here: - inputs/outputs being high volume: the inputs/outputs that are large are often also things that don't change over the course of a project (e.g. a dataset or a model). So you don't really need to
19. 19.▲
    by amakelov 3y ago

    Author here - thank you for the compliment! Always happy to answer any questions (the docs are admittedly quite sparse) and hear about what people like/dislike about the library!
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    Practical dependency tracking for Python function calls(amakelov.github.io)

    2 points by amakelov 3y ago|0 comments
21. 21.▲
    by amakelov 3y ago

    The dependency tracking and the graph/SQL thing are largely independent pieces. The graph/SQL thing is used to query the memoization tables of the memoized functions by "joining them along a given computational graph". T
22. 22.▲
    by amakelov 3y ago

    Hi, author here. Sorry about the confusion - this blog post's intention was to give a more programming-language-themed introduction to the project (discussion on r/programminglanguages is here: https://www.reddit.com&#x