python-build-standalone
pyenv
python-build-standalone | pyenv | |
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11 | 261 | |
1,544 | 36,723 | |
- | 3.2% | |
9.1 | 8.9 | |
8 days ago | 6 days ago | |
Python | Roff | |
BSD 3-clause "New" or "Revised" License | 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.
python-build-standalone
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Mise is a polyglot tool version manager
It also replaces "just" as a task manager for me which is very pleasant.
The fact that the python plugin uses precompiled Python binaries by default instead of building them from source remove common issues I had with the asdf's python plugin at work with missing dependencies.
Just so you know, I encountered two little quirks that needed a fix:
- [Backspace Key Doesn't work in Python REPL](https://github.com/indygreg/python-build-standalone/blob/mai...)
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Pyenv – lets you easily switch between multiple versions of Python
These builds are an alternative: https://github.com/indygreg/python-build-standalone
Those are what Rye and hatch use.
Drawbacks: late availability of patch versions, various quirks from how they are built (missing readline, missing some build info that self-compiled C python modules might need.)
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Show HN: Pywebview 5
Bundling Python isn't too bad if you find the right tools for it.
I really like https://github.com/indygreg/python-build-standalone and https://github.com/indygreg/PyOxidizer
A bundled, built standalone Python can be 16 to 32MB (including the full standard library, which you can strip down to just the bits you use to save size). Not tiny, but probably not worth switching programming languages over.
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ModuleNotFoundError, but it's there
I'm trying to build a "portable" Python package based on those available from https://github.com/indygreg/python-build-standalone/releases.
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Briefcase: Convert a Python project into a standalone native application
I'm a huge fan of https://github.com/indygreg/python-build-standalone which provides Python builds that CAN be moved around and work independently of any other Python installation.
I used that for my own Python+Electron app, which I wrote about here: https://til.simonwillison.net/electron/python-inside-electro...
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alternative to poetry/pip/pipenv/pyenv/venv/virtualenv/pdm/hatch/…
I used to build my own Pythons that are the same everywhere, now I use indygreg's Python builds. Rye will automatically download and manage Python builds from there. No compiling, no divergence.
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As if there weren't enough packaging tools already: mitsuhiko/rye: an experimental alternative to poetry/pip/pipenv/venv/virtualenv/pdm/hatch/…
One interesting tidbit is that it completely ignores your system Python installations, and instead uses precompiled installations of Python by indygreg from PyOxidizer. This means you don't have to deal with installing Python. It just auto downloads the right builds.
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How to install any version of Python on Northeastern's Linux server
wget https://github.com/indygreg/python-build-standalone/releases/download/20220630/cpython-3.10.5+20220630-x86_64_v3-unknown-linux-gnu-install_only.tar.gz -O - | tar -xz && mv python PortablePython
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Switching from pyenv, rbenv, goenv and nvm to asdf – yujinyuz
The lack of Ruby support instantly rings an alarm for me because CPython (on POSIX) also is not relocatable, but is listed as support. Turns pit Hermit is actually using a third-party build script[1] instead of the official one. While the python-build-standalone project is quite awesome and indeed is useful for a lot of things, it has enough quirks I would recommend against any generic package distributor to advertise as Python for general use. This in turn makes me lose most confidence on Hermit, unfortunately.
Be careful if you’re also interested in Hermit. These kinds of things bit you up way down the road when you least expect them to.
[1] https://github.com/indygreg/python-build-standalone
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How to make sure a python program runs on a computer that might not have internet connection to download the external libraries used?
If you really want to be sure, you can download an install_only standalone Python build from https://github.com/indygreg/python-build-standalone/releases and install the libraries with the included pip. Then just tar it again to archive it, and use the included python to run your project. The downloaded wheel you get with pip wheel may depend on the Python version so you just save the wheels you must make sure the Python point version is exactly the same.
pyenv
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Install Asdf: One Runtime Manager to Rule All Dev Environments
If you have a requirement for multiple, specific Python versions, why not just use pyenv?
https://github.com/pyenv/pyenv
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Setup and Use Pyenv in Python Applications
For more information visit: pyenv repository
- Pyenv – lets you easily switch between multiple versions of Python
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How to Create Virtual Environments in Python
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.
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How to debug Django inside a Docker container with VSCode
Python version manager pyenv
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Integrating GPT in Your Project: Create an API for Anything Using LangChain and FastAPI
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.
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Manage your Python Project End-to-End with PDM
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.
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Introducing Flama for Robust Machine Learning APIs
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.
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Is KDE Desktop really snappier than XFCE these days as claimed?
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.
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Python Versions and Release Cycles
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.
What are some alternatives?
iron.nvim - Interactive Repl Over Neovim
Poetry - Python packaging and dependency management made easy
eclectica - ☀️ Cool and eclectic version manager for any language
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
semver - Semantic Versioning Specification
Pipenv - Python Development Workflow for Humans.
Visual Studio Code - Visual Studio Code
miniforge - A conda-forge distribution.
evcxr
virtualenv - Virtual Python Environment builder
vscode-jupyter - VS Code Jupyter extension
Pew - A tool to manage multiple virtual environments written in pure python