codon
pure_numba_alias_sampling
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codon | pure_numba_alias_sampling | |
---|---|---|
34 | 1 | |
13,809 | 3 | |
0.9% | - | |
7.9 | 10.0 | |
13 days ago | about 6 years ago | |
C++ | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
codon
- Should I Open Source my Company?
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Python running on the Dart VM?
I found at least one project that managed to compile python AOT to LLVM https://github.com/exaloop/codon. Even if LLVM is more expressive than Dart Kernel, that should at least be some evidence that this might not be too impractical.
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Codon: Python Compiler
Their fannkuch benchmark seems to be a bit dishonest. They claim an enormous perf delta on https://exaloop.io/benchmarks.html but fannkuch uses factorial a lot and they define factorial with a very small (n=20) table: https://github.com/exaloop/codon/blob/fb461371613049539654c1...
Disclaimer: I've worked on several Python runtimes and compilers, but I'm not by any means out to get Codon. Just happened across this by accident while looking at their inline LLVM, which is neat.
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The father of Swift made another baby: Mojo: looks to be based on Python using MLIR
If you literally want Python, but compiled ... Look at Codon: https://github.com/exaloop/codon
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Mojo – a new programming language for all AI developers
Another "Python with high-performance compiled builds" would be https://github.com/exaloop/codon.
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MIT Turbocharges Python’s Notoriously Slow Compiler
This is the project being discussed: https://github.com/exaloop/codon
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Is there a way to use turn a project into a single executable file that doesn't require anyone to do anything like install Python before using it?
Try Codon? https://github.com/exaloop/codon
- Since when did Python haters spread out everywhere? Maybe DNF5 would be faster because of ditched it, maybe.
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Budget HomeLab converted to endless money-pit
https://github.com/exaloop/codon might save you from the rewrite.
- What are your thoughts on Codon compiler having a paid licence?
pure_numba_alias_sampling
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Numba: A High Performance Python Compiler
It’s not suitable for all use cases.
But I highly highly recommend it if you need to do somewhat complex calculations iterating over numpy arrays for which standard numpy or scipy functions don’t exist. Even then, often we were surprised that we could speed up some of those calculations by placing them inside numba.
Edit: ex of a very small function I wrote with numba that speeds up an existing numpy function (note - written years ago and numba has undergone quite some amount of changes since!): https://github.com/grej/pure_numba_alias_sampling
Disclosure - I now work for Anaconda, the company that sponsors the numba project.
What are some alternatives?
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
Numba - NumPy aware dynamic Python compiler using LLVM
autograd - Efficiently computes derivatives of numpy code.
Cython - The most widely used Python to C compiler
hn-search - Hacker News Search
taichi - Productive, portable, and performant GPU programming in Python.
qha - A Python package for calculating thermodynamic properties under quasi-harmonic approximation, using data from ab-initio calculations
julia - The Julia Programming Language
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
ideas4 - An Additional 100 Ideas for Computing https://samsquire.github.io/ideas4/