JuliaPy / JuliaPy/PythonCall.jl

JIT Python option and other options to take advantage of

Offen
#496 0 Kommentare 0 Reaktionen 0 zugewiesene Personen Auf GitHub ansehen
enhancement
Vorherrschende Sprache
Julia
Sterne
1.1k
Forks
86
Ø Merge
1 T. 22 Std.
Gemergte PRs (30 T.)
3

Beschreibung

I see you upgraded minimum Python to 3.8, so it got me curious what does it and later versions enable that you could take advantage of:

https://docs.python.org/3.13/whatsnew/3.13.html#experimental-jit-compiler

You download Python automatically, and while the JIT is in the current alpha, it's experimental and not on it seems by default.

I.e. I think it requires a specialized build, and you could support such, optionally. Or by default, at least it will be at some point. Also they are dropping 19 outdated batteries, so it's a smaller download...

In 3.8:
https://docs.python.org/3.8/whatsnew/3.8.html#parallel-filesystem-cache-for-compiled-bytecode-files

https://bugs.python.org/issue33499

Yes, it's an ENV var and your users could just set it, or be ignorant of it. I was thinking should you set it for the users? That might be bad in case the user already set it... Anyway, it could be documented here and/or set for the user, unless you invoke PythonCall in a special way to not disturb it.

I didn't read carefully, but it seems can be much faster (startup) if you opt into it, I can understand if you do not want to change defaults for users, or document to much. Maybe just document 3.13 is preferred, but older down to 3.8 is supported? With some easy way to limit to 3.13 and newer.

Beitragsleitfaden

Für dieses Repository ist kein Beitragsleitfaden indexiert

Rechercherichtung

Start by reviewing the linked Python 3.13 JIT documentation and the Python 3.8 bytecode-cache documentation, then inspect how PythonCall.jl downloads and invokes Python. Determine whether JIT builds, cache-related environment settings, or version guidance should be supported or documented; done means a decided scope and corresponding user-facing behavior or documentation.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Bewertung

Tech-Stack
julia, python
Bereich
tooling
Issue-Typ
Feature
Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Aktivitätsstatus
Veraltet
Klarheit
Muss geklärt werden
Anfängerfreundlichkeit
25/100

Neue Issues direkt in Ihr Postfach

Eine kurze Übersicht über anfängerfreundliche GitHub-Issues.