self-contained-runnable-py
pip-audit
self-contained-runnable-py | pip-audit | |
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3 | 22 | |
- | 920 | |
- | 1.4% | |
- | 8.8 | |
- | 4 days ago | |
Python | ||
- | Apache License 2.0 |
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.
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-audit
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
Next up is making sure, none of the dependencies used throughout the project brings with it any already identified security issue. The makefile target audit, invokes the handy tool pip-audit.
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Show HN: One makefile to rule them all
Here is my "one true" Makefile for Python projects[1]. The skeleton gets tweaked slightly each time, but it's served me well for 4+ years.
[1]: https://github.com/pypa/pip-audit/blob/main/Makefile
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Pyscan: A command-line tool to detect security issues in your python dependencies.
Why use this over the established https://pypi.org/project/pip-audit/ ?
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How Attackers Can Sneakily Slip Malware Packages Into Poetry.lock Files
https://pypi.org/project/pip-audit/ details usage and the GitHub Action install.
- How to improve Python packaging, or why 14 tools are at least 12 too many
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Underappreciated Challenges with Python Packaging
If it's pure Python, the only packaging file you need is `pyproject.toml`. You can fill that file with packaging metadata per PEP 518 and PEP 621, including using modern build tooling like flit[1] for the build backend and build[2] for the frontend.
With that, you entire package build (for all distribution types) should be reducible to `python -m build`. Here's an example of a full project doing everything with just `pyproject.toml`[3] (FD: my project).
[1]: https://github.com/pypa/flit
[2]: https://github.com/pypa/build
[3]: https://github.com/pypa/pip-audit
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Auditing your python environment
- repo: https://github.com/trailofbits/pip-audit rev: v2.4.3 hooks: - id: pip-audit args: [ "-r", "requirements.txt" ] ci: # Leave pip-audit to only run locally and not in CI # pre-commit.ci does not allow network calls skip: [ pip-audit ]
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How to create a Python package in 2022
This is really nicely written; kudos to the author for compiling a great deal of information in a readable format.
If I can be forgiven one nitpick: Poetry does not use a PEP 518-style[1] build configuration by default, which means that its use of `pyproject.toml` is slightly out of pace with the rest of the Python packaging ecosystem. That isn't to say that it isn't excellent, because it is! But you the standards have come a long way, and you can now use `pyproject.toml` with any build backend as long as you use the standard metadata.
By way of example, here's a project that's completely PEP 517 and PEP 518 compatible without needing a setup.py or setup.cfg[2]. Everything goes through pyproject.toml.
[1]: https://peps.python.org/pep-0518/
[2]: https://github.com/trailofbits/pip-audit/blob/main/pyproject...
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I think the CTX package on PyPI has been hacked!
Checking could be done if something like this eventually shows up in safety or pip-audit.
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Open-source way to scan dependencies for CVEs?
Something like python's pip-audit. For commercial solutions I know there's Snyk and Jfrog we can always purchase, but I'm interested to see if there's an open-source tool that can do this.
What are some alternatives?
tox-poetry-installer - A plugin for Tox that lets you install test environment dependencies from the Poetry lockfile
ochrona-cli - A command line tool for detecting vulnerabilities in Python dependencies and doing safe package installs
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
git-hooks.nix - Seamless integration of https://pre-commit.com git hooks with Nix.
hatch - Modern, extensible Python project management
npm-esbuild-audit
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
setup-dvc - DVC GitHub action
aura - Python source code auditing and static analysis on a large scale
squelch
tan - The uncompromising Python code formatter