pyenv | pip | |
---|---|---|
261 | 108 | |
36,817 | 9,289 | |
1.5% | 0.6% | |
8.9 | 9.8 | |
12 days ago | 2 days ago | |
Roff | Python | |
MIT 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.
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.
pip
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How to Create Virtual Environments in Python
Whenever you are working on a Python project that has external dependencies installed with pip, it is strongly recommended to first create a virtual environment.
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Boring Python: dependency management (2022)
Unfortunately that feature is easy to break: https://github.com/pypa/pip/issues/9644
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pip VS instld - a user suggested alternative
2 projects | 9 Dec 2023
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sudo pip install should be illegal
I think I did my part https://github.com/pypa/pip/issues/6409
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Can't seem to install Python YAML support
$ sudo pip install y$ sudo pip install yaml WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly. ERROR: Could not find a version that satisfies the requirement yaml (from versions: none) ERROR: No matching distribution found for yaml
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Bun v0.6.0 – Bun's new JavaScript bundler and minifier
What are you implying will happen?
Using the build-in tools, you can save the exact versions of dependencies (i.e. a lock file) using "pip freeze >dependencies.txt". This should give you the exact same set of packages in two years' time.
If you want to be even more sure, you can also store hashes in the lock file. This has to be generated by a separate tools at the moment [1][2] but can be consumed by the built-in tools [3], so "pip install -r requirements.txt" is still all you need in two years' time.
[1] https://github.com/pypa/pip/issues/4732
[2] https://pip-tools.readthedocs.io/en/latest/#using-hashes
[3] https://pip.pypa.io/en/stable/topics/secure-installs/#hash-c...
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My Goldilocks Python Setup: pyenv, pipx, and pip-tools
Here’s the issue, https://github.com/pypa/pip/issues/11664. I think the idea would be to have some file/json description of environment that could be passed to pip to allow it to fully cross compile. They are open to supporting it just needs contributor to be found to implement it and go through review/discussion.
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Remote Code Execution Vulnerability in Google They Are Not Willing to Fix
To be fair the only alternative is fixing Python, and even then you still would have to wait a good 5 years at least for all the old Python versions to dwindle.
It doesn't look like the fixing effort is progressing very quickly: https://github.com/pypa/pip/issues/8606
To their credit, at least they didn't close it "works as intended" which I imagine a lot of projects would.
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Pip 23.1 Released - Massive improvement to backtracking
Another good benchmark to trying to resolve apache-airflow[all]==1.10.13 using the state of PyPi on 2020-12-02, I give instructions here on how to reproduce that workflow: https://github.com/pypa/pip/issues/11836. Including a benchmark how how many extra packages your resolver should visit.
- will upgrading pip break things?
What are some alternatives?
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
Pipenv - Python Development Workflow for Humans.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
miniforge - A conda-forge distribution.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
virtualenv - Virtual Python Environment builder
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
Pew - A tool to manage multiple virtual environments written in pure python
wheel - Adoption analysis of Python Wheels: https://pythonwheels.com/