pypiserver
pyenv
pypiserver | pyenv | |
---|---|---|
3 | 261 | |
1,694 | 36,723 | |
0.7% | 1.3% | |
7.2 | 8.9 | |
8 days ago | 8 days ago | |
Python | Roff | |
GNU General Public License v3.0 or later | 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.
pypiserver
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How to best include personal modules in other projects?
In our company we setup an internal pypi server https://github.com/pypiserver/pypiserver
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Python Best Practices for a New Project in 2021
> One should probably run their own package server like https://github.com/pypiserver/pypiserver
Never used pypiserver but I’ve had a good experience with https://github.com/devpi/devpi
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?
devpi - Python PyPi staging server and packaging, testing, release tool
Poetry - Python packaging and dependency management made easy
mamba - The Fast Cross-Platform Package Manager
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
Pipenv - Python Development Workflow for Humans.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
miniforge - A conda-forge distribution.
flynt - A tool to automatically convert old string literal formatting to f-strings
virtualenv - Virtual Python Environment builder
Pew - A tool to manage multiple virtual environments written in pure python