Pyjion VS Pyston

Looks like it was started at Microsoft, the tonybaloney/Pyjion was forked from microsoft/Pyjion.

Pyjion is a Microsoft project that adds a JIT leveraging CoreCLR.

https://github.com/microsoft/Pyjion was a really nice attempt to marry CPython with JIT in a rather generic way, but it was abandoned quickly.

There are articles like https://instagram-engineering.com/dismissing-python-garbage-... which seem to confirm some problems. One of the people involved with this Cinder project was an original author of https://github.com/microsoft/Pyjion (it's now being revamped by a new dev.) Dino had deep .NET/CLR implementation experience and had been the lead for IronPython which I think spawned the interest in perf improvements through Cinder. I hope this effort is more successful for him.

Just for reference,

* Nuitka[0] "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."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

and if you need more speed you can try https://www.pypy.org/ or https://github.com/tonybaloney/Pyjion or https://www.pyston.org/

What efforts have been done come with limitations. PyPy is mostly compatible. Pyston seems mostly compatible but offers only modest speedups. IronPython and Jython run on the .NET and Java runtimes, respectively. They’re JITed as a consequence of that, but that also means they’re stuck in those environments and don’t work with CPython modules that use native code.

https://github.com/pyston/pyston/blob/69b190003f14dfd2f6d276...

Seems easier to use the C functions to do this, rather than rely on system commands.

Releases · pyston/pyston

Honestly, even that seems trivial? By my reading of https://github.com/pyston/pyston#installing-packages , the only impact is that when you install (compiled) libraries they need to be recompiled, just like if you use Alpine (which is also ABI-incompatible because it uses musl libc), which is a little bit of pain at build/packaging time but doesn't actually break anything (i.e. there are no libraries that you can't use, just libraries with an extra compile step) and doesn't affect runtime behavior at all.