PDM VS pyflow

# 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

PDM is a solution that allows for easy creation and management of python projects. Some of the key features that will improve the management of python projects include:

You'd need a proper dependencies management tool like PDM or Poetry to exhaustively resolve and lock down all the transitive dependencies if you want to have anything closed to reproducible build.

Are you working on a Python project that uses pdm for dependency management and dotenv for local environment variable and secrets management? Do you find it frustrating when CLI tools like pgcli don't automatically pick up your .env file, forcing you to resort to npm install -g dotenv-cli? I've got a more convenient solution for you!

I first learned about PDM from a blog post written by one of the PDM contributers. The post was about OOPifying argparse to allow for easy creation/modification of subcommands that exist as their own classes/files, and to avoid maintaining a single long script with an endless number of subparser.add_argument(...) lines.

That's more or less what PEP 582 plans to do, but it's been stalled and mired in discussions for years. The PDM tool went ahead and implemented it though if you want to use it: https://github.com/pdm-project/pdm

pdm – A modern Python package and dependency manager

pdm and PEP 582 enter the chat

I'm liking PDM for a while now. Quicker than Poetry and built according to the Python package spec in mind and not as an afterthought. While it was originally meant to work with PEP 582, it works with virtual environments too (now default).

https://github.com/pdm-project/pdm

Very cool! Of note, I made something along these lines a few years ago, although with a slightly broader scope to also include managing and installing python versions. I abandoned it due to lack of free time, and edge cases breaking things. The major challenge is that Python packages that aren't wheels can do surprising things due to setup.py running arbitrary code. (https://github.com/David-OConnor/pyflow)

Pyflow

Pyflow takes care of the use when you need pyenv to isolate different python versions, pipx to isolate some global python-based tools, and isolated, reproducible builds per project with on tool. I highly recommend people to give it go.

https://github.com/David-OConnor/pyflow#a-thoroughly-biased-...

Pyflow is a similar implementation of PEP582. NGL I wonder if it's better because of how good Rust stuff is. Probably a lot faster. Looks like you can install it via Pypi. I should've tested it before moving to PDM. Though it seems dev is a bit slow. Hmmm.

I’m personally complaining that pip is so much behind cargo. I have some hope with Pyflow though.

I literally stumbled into this issue again today. Has anyone leveraged Pyflow before? It looks pretty slick for keeping things organized. I don't do heavy dev work, just need something to keep things generally tidy. Was curious if anyone had used it and their opinion on it.

PDM is pretty new so it’s not entirely clear how it’ll play out but if you’re interested in PEP 582 then it’s really that or pyflow.

It's a good safeguard, and it's going in the direction of the other initiatives to make python package management default behavior saner.

PEP 852 is the another one to follow up: https://www.python.org/dev/peps/pep-0582/

It basically uses the concept of node_modules, making python interpreters local any local __pypackages__ directory. There are 2 differences though:

- unlike JS, python can only have one version of one lib

- but since having several versions of python often matters, you may have several __pypackages__/X.Y sub dirs to catter to each of them

It does also force you to use "-m" to use commands, which is the best practice anyway. I hope it will make jupyter fix "-m" on windows for them because that's a blocker for beginners.

If you are not already using "-m", start now. It solves a lot of different problems with running python cli programs.

E.G: instead of running "black" or "pylint", do "python -m black" or "python -m pylint". Or course you may want to chose a specific version of python, so "python3.8 -m black" for unix, or "py -3.8 -m black" on windows.

To test out __pypackages__, give a try to the pdm project: https://github.com/pdm-project/pdm

At last, some other tools that I wish people knew more about that solves packaging issues:

- pyflow (https://github.com/David-OConnor/pyflow): it's a package manager like poetry, but it also install whatever python you want like pyenv. Except it provides the binary, no need to compile anything. It's a young project, but I wish it succeeds because it's really a great concept.

- shiv (shiv.readthedocs.io/): it leverage the concept of zipapp, meaning the ability that python has to execute python inside a zip file. It's a successor to pex. Basically it lets you bundle your code + all deps from virtualenv inside a zip, like a Java .war file. You can then run the resulting zip, a .pyz file, like if it was a regular .py file. It will unzip on the first run automatically. It makes deployment almost as easy as with golang.

- nuitka (shiv.readthedocs.io/): take your code and all dependancies, turn them into C, and compiles it. Although it does require a bit of setup, since it needs headers and a compiler, it results reliably in a standalone compiled executable that will run on the same architecture with no need for anything else. Also it will speed up your Python program, up to 4 times.