pure_numba_alias_sampling VS codon

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.

https://github.com/exaloop/codon/blob/develop/LICENSE

Here are some others: https://github.com/search?q=%22Business+Source+License%22+%2...

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.

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.

If you literally want Python, but compiled ... Look at Codon: https://github.com/exaloop/codon

Another "Python with high-performance compiled builds" would be https://github.com/exaloop/codon.

This is the project being discussed: https://github.com/exaloop/codon

Try Codon? https://github.com/exaloop/codon

https://github.com/exaloop/codon might save you from the rewrite.