self-contained-runnable-py
DISCONTINUED
pip-tools
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self-contained-runnable-py | pip-tools | |
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3 | 58 | |
- | 7,421 | |
- | 1.4% | |
- | 8.9 | |
- | 3 days ago | |
Python | ||
- | BSD 3-clause "New" or "Revised" License |
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self-contained-runnable-py
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Underappreciated Challenges with Python Packaging
The approach I prefer is to not mess with setuptools etc at all in the first place, and simply make a nice executable package.
e.g. https://github.com/tpapastylianou/self-contained-runnable-py...
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How to create a Python package in 2022
The title should be: How to create a "Python DISTRIBUTION package".
The term "python package" means something entirely different (or at the very least is ambiguous in a pypi/distribution context).
To add to the confusion, creating a totally normal, runnable python package in a manner that makes it completely self-contained such that it can be "distributed" in a standalone manner, while still being a totally normal boring python package, is also totally possible (if not preferred, in my view).
Shameless plug: https://github.com/tpapastylianou/self-contained-runnable-py...
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Show HN: Hatch 1.0.0 – Modern, extensible Python project management
Shameless plug: I use my own template, which organises things as runnable projects.
https://github.com/tpapastylianou/self-contained-runnable-py...
It serves my purposes very well (which is creating projects that represent standalone experiments).
Sharing in case someone else here finds it useful.
More recently I've modified this a bit to also generate nice html reports straight from the __main__.py file, independently of the underlying python code, and use this as lab books (where each lab book contains a single analysis and its report). I'll upload this template separately when I find the time.
pip-tools
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Pyenv – lets you easily switch between multiple versions of Python
> Why is the "requirements.txt" file a stupid flat listing of all transitive dependencies with pinned versions? It makes it harder to change library versions even if there are no true conflicts.
My friend, here is what you seek: https://github.com/jazzband/pip-tools
requirements.txt is flat because it's really the output of `pip freeze`. It's supposed to completely and exactly rebuild the environment. Unfortunately it's far too flexible and people abuse it by putting in only direct dependencies etc.
If you're writing packages, you don't need a requirements.txt at all, by the way. Package dependencies (only direct dependencies) live in pyproject.toml with the rest of the package config. requirements.txt (and pip tools) are only for when you want to freeze the whole environment, like for a server deployment.
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lockfiles for hatch projects
For all my projects I found myself regenerating manual lock files using complex shell commands with pip-compile to get a reproducible environments across devices using a custom pre-install-command. I finally decided that instead of hacking together the same solution on all my projects I would build a plugin that handles this complexity for me.
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Setting up Django in a Better Way in 5 Minutes and Understanding How It Works
Instead of venv, we are using pip-tools in this starter kit. pip-tools take things further in dependency management. Check out what pip-tools does in their official GitHub repo. In short, it helps your project find the best match for the dependent packages. For example, you might need two packages A and B in your project that requires same package C under the hood. But A requires any version of C from 1.0.1 to 1.0.10 and B requires any version of C from 1.0.7 to 1.0.15. Pip tools will automatically compile the version of 'C' that suits for both of your packages.
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just-pip-tools: An example of managing python dependencies as layered lock files with just and pip-tools
I've created a small project called just-pip-tools that combines pip-tools and just to manage Python dependencies in a layered approach. This isn't a magic bullet; it's a set of files you can adapt to your needs.
- Single-file scripts that download their dependencies
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What are people using to organize virtual environments these days?
pip-tools
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How to know what a package depend on when pip is installing it?
I recommend generating a lockfile to document this information, as you might do with pip-tools.
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A not so unfortunate sharp edge in Pipenv
Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.
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Show HN: Panoptisch – A recursive dependency scanner for Python projects
I've been using pip-compile from https://github.com/jazzband/pip-tools for this use case; a standard project Makefile defines "make update" which pip-compiles the current requirements, and "make install" installs the frozen requirements list.
This way I can install the same bill of materials every time
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Vent: I'm tired of the 1001 libraries of virtual environments.
I like pip-tools for this use case—it lets you write a separate requirements file with “loose” dependencies, which then gets compiled to a requirements.txt with pinned version numbers (it essentially becomes your “lock” file). It also supports layered dependencies, which allows you to create “dev dependencies”.
What are some alternatives?
Poetry - Python packaging and dependency management made easy
PDM - A modern Python package and dependency manager supporting the latest PEP standards
Pipenv - Python Development Workflow for Humans.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
pip - The Python package installer
miniforge - A conda-forge distribution.
hatch - Modern, extensible Python project management
wheel - Adoption analysis of Python Wheels: https://pythonwheels.com/
dephell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump version.
Curdling - Concurrent package manager for Python
conda-lock - Lightweight lockfile for conda environments
pyenv - Simple Python version management