faster-cpython VS pyenv

It is unclear to me whether Python 3.12 will receive significant improvements. Based on the information from https://github.com/faster-cpython/benchmarking-public, it appears that there may be a 2% performance enhancement. Is this the anticipated result, or are there additional developments awaiting merger?

Initially, the "Shannon Plan" (https://github.com/markshannon/faster-cpython/blob/master/pl...) aimed for a 50% improvement with each release. Has this goal been deemed unattainable, or are there adjustments being made to the plan?

Yes, that's the JIT part of the plan. Sections of code will be compiled, "at runtime". Those sections of compiled code will be tied together with interpreted code. It will be somewhere between rare to impossible to have a fully compiled program, without interpreter glue.

see: https://github.com/markshannon/faster-cpython/blob/master/plan.md

Yes, the Shannon plan, which is actively being worked on by a team headed by Guido, includes JIT work in stages 3 and 4

The goal with faster cpython is for small compounding improvements with each point release[0]. So in the end it should be much more than a tiny improvement.

[0] https://github.com/markshannon/faster-cpython/blob/master/pl...

The Shannon Plan. Announced by Guido at the 2021 Python Language summit, funded by Microsoft.

Well, good news then, it's in the planning!

If you have a requirement for multiple, specific Python versions, why not just use pyenv?

https://github.com/pyenv/pyenv

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.