Solving Sudoku in Python Packaging

[simonw]

I love this so much. I dug around a bit and figured out how it works - I have an explanation (with an illustrative diagram) here: https://simonwillison.net/2024/Oct/21/sudoku-in-python-packa...

Figuring out how it works is a great way to learn a bit more about how Python packaging works under the hood. I learned that .whl files contain a METADATA file listing dependency constraints as "Requires-Dist" rules.

I ran a speed comparison too. Using the uv pip resolver it took 0.24s - with the older pip-compile tool it took 17s.

[TeMPOraL]

Tangent, but I wondered what libuv had to do with speeding up Python packaging, and it turns out nothing. I wonder why someone choose to name a pip replacement in a way that effectively collides with several tools and libraries across many languages...

[seanw444]

Wow, uv really is fast.

[zahlman]

People keep trying to sell the speed of such solutions as a killer feature for uv, but I think I must not be anywhere near the target audience. The constraint-solving required for the sorts of projects I would typically work on is not even remotely as complex, while I'm bottlenecked by a slow, unreliable Internet connection (and the lack of a good way to tell Pip not to check PyPI for new versions and only consider what's currently in the wheel cache).

[ilyagr]

How does it encode the idea of having all the numbers on each line/square?

[visarga]

That's why it feels like installing a ML repo is like sudoku. You install everything and at the last step you realize your neural net uses FlashAttention2 which only works on NVIDIA compute version that is not deployed in your cloud VM and you need to start over from scratch.

[hskalin]

Sometimes I just change the version of the package in requirements to fit with others and pray that it works out (a few times it does)

[pjc50]

See the discussion on why sqlite insists on vendoring its build dependencies as far as possible and not using, say, CMake.

[austinjp]

This describes the day I wasted on Monday before I gave up and wrote some damn deterministic code instead of using some damn AI.

[nicman23]

honestly if the ml does not have a docker image - not compose no build an image- i do not even bother any more

[anthk]

Guix fixes that in the spot.

[chatmasta]

Here’s the same thing in Poetry (2022): https://www.splitgraph.com/blog/poetry-dependency-resolver-s...

[teschmitt]

Was just about to say: I've seen this before but building it with a universally usable requirements.txt is even cooler.

[echoangle]

> Solving the versions of python package from your requirements is NP-complete, in the worst case it runs exponentially slow. Sudokus are also NP-complete, which means we can solve sudokus with python packaging.

Is that actually sufficient? Can every system that’s solving something that’s NP-complete solve every other NP-complete problem?

[tzs]

> Can every system that’s solving something that’s NP-complete solve every other NP-complete problem?

Others have given the answer (yes) and provided some links. But it is nice to have an explanation in thread so I'll have a go at it.

The key idea is the idea of transforming one problem to another. Suppose you have some problem X that you do not know how to solve, and you've got some other problem Y that you do know how to solve.

If you can find some transform that you can apply to instances of X that turns them into instances of Y and that can transform solutions of those instances of Y back to solutions of X, then you've got an X solver. It will be slower than your Y solver because of the work to transform the problem and the solution.

Now let's limit ourselves to problems in NP. This includes problems in P which is a subset of NP. (Whether or not it is a proper subset is the famous P=NP open problem).

If X and Y are in NP and you can find a polynomial time transformation that turns X into Y then in a sense we can say that X cannot be harder than Y, because if you know how to solve Y then with that transformation you also know how to solve X albeit slower because of the polynomial time transformations.

In 1971 Stephen Cook proved that a particular NP problem, boolean satisfiability, could serve as problem Y for every other problem X in NP. In a sense then no other NP problem can be harder than boolean satisfiability.

Later other problems were also found that were universal Y problems, and the set of them was called NP-complete.

So if Python packaging is NP-complete then every other NP problem can be turned into an equivalent Python packaging problem. Note that the other problem does not have to also be NP-complete. It just has to be in NP.

Sudoku and Python Packaging both being NP-complete means it goes both ways. You can use a Python package solver to solve your sudoku problems and you can use a sudoku solver to solve your Python packaging problems.

[zahlman]

>Can every system that’s solving something that’s NP-complete solve every other NP-complete problem?

Yes, by definition (https://en.wikipedia.org/wiki/NP-completeness , point 4).

[rolisz]

Yes, NP complete means that every other NP problem is reducible to it.

[arjvik]

I think for Sudoku to be NP-Complete, it needs to be generalized to arbitrary board sizes (at the very least)

[empath75]

https://www.youtube.com/watch?v=6OPsH8PK7xM

This video I think makes it obvious why that's true in a pretty intuitive way. I posted it a few days ago as a link and it never got traction.

SAT is the equivalent of being able to find the inverse of _any_ function, because you can describe any function with logic gates (for obvious reasons), and any collection of logic gates that describes a function is equivalent to a SAT problem. All you need to do is codify the function in logic gates, including the output you want, and the ask a SAT solver to find the inputs that produce that output.

[yochem]

No way pip actually is a really inefficient SAT solver!

[stabbles]

For a long time it was not because there was no backtracking.

Now it is just an exhaustive, recursive search: for the current package try using versions from newest to oldest, enqueue its dependencies, if satisfied return, if conflict continue.

[fernandotakai]

uv actually talks about this in their resolver docs https://docs.astral.sh/uv/reference/resolver-internals/

[alentred]

This is BRILLIANT ! I knew of a trend to implement lots of different things at compile-time (in Scala and Haskell communities at least) - definitely fun and quirky, but it never seemed that "special". This one, it has an air of old-school computer magic around it, probably because it is so elegant and simple.

[mi_lk]

See also this 2008 post using Debian package system to solve Sudoku:

https://web.archive.org/web/20160326062818/http://algebraict...

[ziofill]

but how does it know the constraints?

[thangngoc89]

This is the content of sudoku_0_0-1-py3-none-any.whl. So when the (0,0) cell is 1, none of the cells in the same row, column and subgrid should be 1.

    Requires-Dist: sudoku_0_1 != 1
    Requires-Dist: sudoku_0_2 != 1
    Requires-Dist: sudoku_0_3 != 1
    Requires-Dist: sudoku_0_4 != 1
    Requires-Dist: sudoku_0_5 != 1
    Requires-Dist: sudoku_0_6 != 1
    Requires-Dist: sudoku_0_7 != 1
    Requires-Dist: sudoku_0_8 != 1
    Requires-Dist: sudoku_1_0 != 1
    Requires-Dist: sudoku_2_0 != 1
    Requires-Dist: sudoku_3_0 != 1
    Requires-Dist: sudoku_4_0 != 1
    Requires-Dist: sudoku_5_0 != 1
    Requires-Dist: sudoku_6_0 != 1
    Requires-Dist: sudoku_7_0 != 1
    Requires-Dist: sudoku_8_0 != 1
    Requires-Dist: sudoku_0_1 != 1
    Requires-Dist: sudoku_0_2 != 1
    Requires-Dist: sudoku_1_0 != 1
    Requires-Dist: sudoku_1_1 != 1
    Requires-Dist: sudoku_1_2 != 1
    Requires-Dist: sudoku_2_0 != 1
    Requires-Dist: sudoku_2_1 != 1
    Requires-Dist: sudoku_2_2 != 1

[jsnell]

The constraints are going to be static and independent of the puzzle. So I expect they're encoded in the package dependencies. So for example version 1 of the package sudoku_0_0 will conflict with all of: version 1 of sudoku_[0-8]_0; version 1 of sudoku_0_[0-8]; version 1 of [012]_ [012].

[IshKebab]

Yeah they missed out the actual interesting bit from the readme...

[worewood]

This is type of cool hacking I like to see. Kudos! (Or better, Sukodus :) )

[niyonx]

How did you even think of that? Nice!

[revskill]

This is a hack.

[jessekv]

And why I come here for... er, news.

[anthk]

Now, in MicroLisp, Common Lisp and maybe Emacs' Elisp too:

http://www.ulisp.com/show?33J9