pre-commit VS Poetry

Today we are going to look at how to setup Black (a python code formatter) and pre-commit (a package for handling git hooks in python) to automatically format you code on commit.

# See https://pre-commit.com for more information # See https://pre-commit.com/hooks.html for more hooks repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v3.2.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-toml - id: check-added-large-files - repo: local hooks: - id: tox lint name: tox-validation entry: pdm run tox -e test,lint language: system files: ^src\/.+py$|pyproject.toml|^tests\/.+py$ types_or: [python, toml] pass_filenames: false - id: tox docs name: tox-docs language: system entry: pdm run tox -e docs types_or: [python, rst, toml] files: ^src\/.+py$|pyproject.toml|^docs\/ pass_filenames: false - repo: https://github.com/pdm-project/pdm rev: 2.10.4 # a PDM release exposing the hook hooks: - id: pdm-lock-check - repo: https://github.com/jumanjihouse/pre-commit-hooks rev: 3.0.0 hooks: - id: markdownlint

Pre-commit hooks act as the first line of defense in maintaining code quality, seamlessly integrating with linters and code formatters. They automatically execute these tools each time a developer tries to commit code to the repository, ensuring the code adheres to the project's standards. If the hooks detect issues, the commit is paused until the issues are resolved, guaranteeing that only code meeting quality standards makes it into the repository.

Pre-commit Hooks: Pre-commit is a tool that can be set up to enforce coding rules and standards before you commit your changes to your code repository. This ensures that you can't even check in (commit) code that doesn't meet your standards. This allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks.

Ah, fair enough!

On my team we use pre-commit[0] a lot. I guess I would define the history to be something like "has this commit ever been run through our pre-commit hooks?". If you rewrite history, you'll (usually) produce commits that have not been through pre-commit (and they've therefore dodged a lot of static checks that might catch code that wasn't working, at that point in time). That gives some manner of objectivity to the "history", although it does depend on each user having their pre-commit hooks activated in their local workspace.

[0]: https://pre-commit.com/

Pre-commit is a framework for managing and maintaining multi-language pre-commit hooks. It supports hooks for various programming languages. Using this framework, you only have to specify a list of hooks you want to run before every commit, and pre-commit handles the installation and execution of those hooks despite your project’s primary language.

You can learn more here: pre-commit.com

Note this includes support for pre-commit.

As a nice next step, you could also add mypy to check your type hints are consistent, and automate running all this via pre-commit hooks set up with… pre-commit.

You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:

The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:

Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?

https://github.com/python-poetry/poetry/issues/6409

Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.

Not sure where I would stand if I fully investigated it tho.

[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...

We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.

Here are the two main packaging issues I run into, specifically when using Poetry:

1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.

2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.

I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.

Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.

[0] https://github.com/python-poetry/poetry/issues/2740

[1] https://github.com/python-poetry/poetry/issues/2184

[2] https://pypi.org/project/pypiserver/

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

For the release of txtToWeb, I opted for Poetry as my release tool and TestPyPI as the package registry. Poetry's simplicity and TestPyPI's environment for testing releases were crucial factors in my decision.