PySR
Pluto.jl
PySR | Pluto.jl | |
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7 | 78 | |
1,911 | 4,880 | |
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9.6 | 9.5 | |
4 days ago | 2 days ago | |
Python | JavaScript | |
Apache License 2.0 | MIT License |
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PySR
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Potential of the Julia programming language for high energy physics computing
> Yes, julia can be called from other languages rather easily
This seems false to me. StaticCompiler.jl [1] puts in their limitations that "GC-tracked allocations and global variables do not work with compile_executable or compile_shlib. This has some interesting consequences, including that all functions within the function you want to compile must either be inlined or return only native types (otherwise Julia would have to allocate a place to put the results, which will fail)." PackageCompiler.jl [2] has the same limitations if I'm not mistaken. So then you have to fall back to distributing the Julia "binary" with a full Julia runtime, which is pretty heavy. There are some packages which do this. For example, PySR [3] does this.
There is some word going around though that there is an even better static compiler in the making, but as long as that one is not publicly available I'd say that Julia cannot easily be called from other languages.
[1]: https://github.com/tshort/StaticCompiler.jl
[2]: https://github.com/JuliaLang/PackageCompiler.jl
[3]: https://github.com/MilesCranmer/PySR
- Symbolicregression.jl ā High-Performance Symbolic Regression in Julia and Python
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[D] Is there any research into using neural networks to discover classical algorithms?
I first learned about it with PySR https://github.com/MilesCranmer/PySR, they have an arxiv paper with some use cases as well.
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Symbolic Regression is NP-hard
I encourage everyone to read this paper. It's well written and easy to follow along. To the uninitiated, SR is the problem of finding a mathematical (symbolic) expression that most accurately describes a dataset of input-output examples (regression). The most naive implementation of SR is basically a breath first search starting from the simplest program tree: x -> sin(x) -> cos(x) ... sin(cos(tan(x))) until timeout. However, we can prune out equivalent expressions and, in general, the problem is embarrassingly parallel which alludes to some hope that we can solve this pretty fast (check out PySR[1] for a modern implementation). I find SR fascinating because it can be used for model distillation: learn a DNN approximation and "distill" it to a symbolic program.
Note that the paper talks about the decision version of the SR problem. ie: can we discover the global optimum expression. I think this proof is important for the SR community but not particularly surprising (to me). However, I'm excited by the potential future work for this paper! A couple of discussion points:
* First, SR is technically a bottom up program synthesis problem where the DSL (math) has an equivalence operator. Can we use this proof to impose stronger guarantees on the "hyperparameters" for bottom up synthesis. Conversely, does the theoretical foundation of the inductive synthesis literature [2] help us define tighter bounds?
* Second, while SR itself is NP hard, can we say anything about the approximate algorithms (eg: distilling a deep neural network to find a solution[3])? Specifically, what proof tell us about the PAC learnability of SR?
Anyhow, pretty cool seeing such work getting more attention!
[1] https://github.com/MilesCranmer/PySR
[2] https://susmitjha.github.io/papers/togis17.pdf
[3] https://astroautomata.com/paper/symbolic-neural-nets/
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āMachine Scientistsā Distill the Laws of Physics from Raw Data
I found it curious that one of the implementations of symbolic regression (the "machine scientist" referenced in the article) is a Python wrapper on Julia: https://github.com/MilesCranmer/PySR
I don't think I've seen a Python wrapper on Julia code before.
- Is it possible to create a Python package with Julia and publish it on PyPi?
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[D] Inferring general physical laws from observations in 300 lines of code
This is really neat! Since you're interested in this subject, you may also appreciate PySR and the corresponding paper which uses Graph Neural Networks to perform symbolic regression.
Pluto.jl
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Potential of the Julia programming language for high energy physics computing
I thought that notebook based development and package based development were diametrically opposed in the past, but Pluto.jl notebooks have changed my mind about this.
A Pluto.jl notebook is a human readable Julia source file. The Pluto.jl package is itself developed via Pluto.jl notebooks.
https://github.com/fonsp/Pluto.jl
Also, the VSCode Julia plugin tooling has really expanded in functionality and usability for me in the past year. The integrated debugging took some work to setup, but is fast enough to drop into a local frame.
https://code.visualstudio.com/docs/languages/julia
Julia is the first language I have achieved full life cycle integration between exploratory code to sharable package. It even runs quite well on my Android. 2023 is the first year I was able to solve a differential equation or render a 3D surface from a calculated mesh with the hardware in my pocket.
- Pluto.jl: Simple, reactive programming environment for Julia
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Ask HN: Why don't other languages have Jupyter style notebooks?
Re Julia there is also pluto.jl that is another notebook-like environment for julia. It's been a few years since I played with it but it looked cool, for example it handles state differently so you don't get into the same messes as with ipython notebooks. https://plutojl.org/
- Pluto: Simple Reactive Notebooks for Julia
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Looking for a Julia gui framework with a demo like EGUI
For this, Notebooks are often used. Julia offers a uniquely nice and interactive Pluto notebook for the web https://github.com/fonsp/Pluto.jl
- Excel Labs, a Microsoft Garage Project
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IPyflow: Reactive Python Notebooks in Jupyter(Lab)
I believe this is what Pluto sets out to do for Julia.
I used it as part of the āComputational Thinkingā with Julia course a year or two back. Even then the beta software was very good and some of the demos the Pluto dev showed were nothing short of amazing
https://plutojl.org/
- For Julia is there some thing like VSCode's python interactive window?
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What have you "washed your hands of" in Python?
I think what you want is Pluto!
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Show HN: Out of order execution in Jupyter notebooks is a solved problem
I like how Pluto.jl handles this:
> Pluto offers an environment where changed code takes effect instantly and where deleted code leaves no trace. Unlike Jupyter or Matlab, there is no mutable workspace, but rather, an important guarantee:
> At any instant, the program state is completely described by the code you see.
[1] https://github.com/fonsp/Pluto.jl
What are some alternatives?
GeneticAlgorithmPython - Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
vim-slime - A vim plugin to give you some slime. (Emacs)
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
rmarkdown - Dynamic Documents for R
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Weave.jl - Scientific reports/literate programming for Julia
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Dash.jl - Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
IJulia.jl - Julia kernel for Jupyter
python-bigsimr
Tables.jl - An interface for tables in Julia