41–50 of 341 posts
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
This is a truly, truly terrible idea. It adds several failure modes, some subtle so you can go a long way in a state of error, just so beginners can type `python` instead of e.g. `python3.10`. Many developers, not just me, have a similar setup: we use virtual environments everywhere, and if you aren't in one, `python` doesn't even resolve to a symbol. If I want to write a quick script with no dependencies, I directly…
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
Earlier quoted context omitted.
These builds are an alternative: https://github.com/indygreg/python-build-standalone Those are what Rye and hatch use. Drawbacks: late availability of patch versions, various quirks from how they are built (missing readline, missing some build info that self-compiled C python modules might need.)
I think PEP711 ( https://peps.python.org/pep-0711/ ) is (eventually) a better alternative, because it builds on top of the proven manylinux approach to binary compatibility.
Not only does the format need to exist but the service of building and publishing them is needed too.
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
python -m venv myenv
. myenv/bin/activate
Is anyone is the AI/ML area finding success with anything other than conda, where installation of CUDA/CUDnn is required? Although I often have to pip install a lot of packages, I find conda's nvidia/pytorch/conda-forge channels are still by far the easiest way to get a deep learning stack up and running, and so I just stick with conda environments. I've tried poetry in the past but getting the NVidia deep learning s…
For anything related to CUDA/CuDNN, use one of NVIDIA base Docker images. Then whether you use Conda / Pip / Poetry / Pipenv does not matter much. Not at all a Conda fan myself and avoid it like the plague
This link shows which package versions come in which Docker tag and is invaluable: https://docs.nvidia.com/deeplearning/frameworks/support-matr...
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
Using python and python libraries only from your package manager (like APT) for a specific OS version.
Micromamba as well, lightweight version of conda/miniconda.
Tools you can use to make sure the Python program you wrote keeps working: requirements.txt, pip, pipenv, pyenv, virtualenv, pyenv-virtualenv, virtualenvwrapper, pyenv-virtualenvwrapper, venv, pyvenv, conda, miniconda, poetry, docker, nix. Which ones did I miss? Which of them actually ensure your program always works the same as when you first wrote it, without asterisks?
Only pdm and poetry generate cross-platform lock files by default as far as I know, but there are a lot of people trying to solve this problem right now.
It's not an easy problem to solve. Python's package management predates package managers from most other programming languages and Python itself predates Linux. There is a lot of baggage so change is very slow.