PythonCall.jl
poly-match
PythonCall.jl | poly-match | |
---|---|---|
3 | 6 | |
681 | 31 | |
4.0% | - | |
8.6 | 2.3 | |
9 days ago | about 1 month ago | |
Julia | Python | |
MIT License | Apache License 2.0 |
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PythonCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
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Select Python environment for PyCall
https://cjdoris.github.io/PythonCall.jl/stable/ may try this https://cjdoris.github.io/PythonCall.jl/stable/pythoncall/#pythoncall-config
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
poly-match
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Improving Interoperability Between Rust and C++
Not my experience at all. At work we rewrote a small bit of hotspot python in Rust with no issues. This was what we primarily followed: https://ohadravid.github.io/posts/2023-03-rusty-python/
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How to convince my boss that Rust is usable
Take at look at this example, it still uses Python as an interface to Rust code. Maybe you can do something similar to still achieve performance improvements without changing the entire codebase.
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GDScript is fine
People are probably downvoting because it's needlessly hyperbolic and argumentative. Nobody is saying that python isn't faster to iterate with, but they're arguing that it would take months to get negligable performance gains in a lower level language, meanwhile here is a recent post from a company that increased the execution of they're python code by 100x with less than 100 lines of Rust. They also claim that nobody cares if something runs a few milliseconds faster, when we're talking about game dev, where games are frequently judged on how many milliseconds it takes to run game logic between frames.
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Making Python 100x faster with less than 100 lines of Rust
Semi Vectorized code:
https://github.com/ohadravid/poly-match/blob/main/poly_match...
Expecting Python engineers unable to read defacto standard numpy code but meanwhile expect everyone can read Rust.....
Not to mention that the semi-vectorized code is still suboptimal. Too many for loops despite the author clearly know they can all be vectorized.
For example instead the author can just write something like:
np.argmin(
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
The article links to a full implementation, so you should be able to test this.
What are some alternatives?
jnumpy - Writing Python C extensions in Julia within 5 minutes.
jsmpeg - MPEG1 Video Decoder in JavaScript
gopy - gopy generates a CPython extension module from a go package.
NodeCall.jl - Call NodeJS from Julia.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
PyCall.jl - Package to call Python functions from the Julia language
truffleruby - A high performance implementation of the Ruby programming language, built on GraalVM.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
birthday-book-app - Rust in Anger: high-performance web applications
rayon - Rayon: A data parallelism library for Rust
numexpr - Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more