deptry VS Poetry

deptry – Find unused, missing and transitive dependencies in a Python project

deptry – A command line utility to check for obsolete, missing and transitive dependencies in a Python project

We are happy to share that deptry 0.10.0 has been released! Deptry is a command line tool to check for issues with dependencies in a Python project, such as obsolete or missing dependencies.

In this latest release, Some major improvements were added to the way deptry reports issues by [Mathieu Kniewallner](https://github.com/mkniewallner). You can find the full release notes [here](https://github.com/fpgmaas/deptry/releases/tag/0.10.0).

If you're interested in learning more about deptry, be sure to check out the [Documentation](https://fpgmaas.github.io/deptry/) and the [GitHub repository](https://github.com/fpgmaas/deptry).

Let us know if you have any questions or feedback!

Since PEP-621 does not specify a recommended way to define development dependencies, everything is expected to be a regular dependency. See here.

Thanks for your comment :) src was used purely as an example. By default, the tool scans for .py files in all directories recursively. But for example, in this issue someone put their source code in crop directory and thus called the tool with deptry crop/, which is not how the argument is supposed to be used.

Your project could use some additional documentation. Now the only way for me to find out how to use it is through the 'open colab' button. You could consider adding an example to the README. I personally always try to add a documentation website, which is really easily done with e.g. mkdocs or Sphinx. For an example, you could check out my most recent project deptry.

I have recently been working on a project called `deptry`, a command line tool to check for issues in the dependencies of Python projects. It can be used to find obsolete, missing, transitive and misplaced development dependencies. It supports the following types of projects:

- Projects that use Poetry and a corresponding pyproject.toml file

- Projects that use a requirements.txt file according to the pip standards

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* Documentation: https://fpgmaas.github.io/deptry/

* GitHub repository: https://github.com/fpgmaas/deptry

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I am quite happy with the project in its current form, but I also realise there is still a lot of room left for improvement. Therefore, I hope some people are willing to give it a try and provide me with feedback. So; if you have a project with a long list of dependencies and a little bit of spare time on your hands, please give it a try and let me know what you think!

If you encounter any issues, find a bug, or have any other form of feedback, please don't hesitate to raise an issue in the GitHub repository, or leave a comment here.

Kind regards,

Florian

P.S. Many thanks to Hirokazu Takaya (https://github.com/lisphilar) for incorporating it in the CI/CD pipeline of his project covid19-sir (https://github.com/lisphilar/covid19-sir). It provided me with very valuable early feedback.

- Projects that use a _requirements.txt_ file according to the [pip](https://pip.pypa.io/en/stable/user_guide/) standards

* [*Documentation*](https://fpgmaas.github.io/deptry/)

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:

Poetry

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.

I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github