41–50 of 224 posts
I haven't had to write python in awhile but are there reasons to use virtualenv etc. instead of a docker container?
That said, main reason to do it is that python is a language of idioms and this is one. It's easier to take the trail than break your own.
I haven't had to write python in awhile but are there reasons to use virtualenv etc. instead of a docker container?
For deployment, Docker solves a lot of problems but there's a significant cost for local development.
I haven't had to write python in awhile but are there reasons to use virtualenv etc. instead of a docker container?
I wish you weren't getting downvoted for this. docker-compose is honestly no more complicated of a thing than virtualenv to use. That said, main reason to do it is that python is a language of idioms and this is one. It's easier to take the trail than break your own.
I can see Docker being slower if virtualized, but on Linux, it’s just a fancier BSD jail, no?
there are way too many python dep/env managers/things pipenv pyenv mkvirtualenv virtualenv pipsi venv pew conda virtualenvwrapper i'm sure i'm forgetting like 5. this is like https://xkcd.com/927/ for the record i use pyenv and virtualenv (although playing with ML i'm using conda)
Virtualenv creates an application directory. Pip install the required packages into it.
Every time I use Python I miss NPM and package.json.
You miss a package manager layered on top of the one already in your OS, one that's seemingly run by people with no clue what they're doing, is rife with security issues and can break your entire system as we saw today? I have no clue why a sane person would run npm.
Earlier quoted context omitted.
As a Python developer, I just stay with pip and virtualenv, but I share the smh/wtf sentiment here. The data science folks enjoy conda but I don't see that being useful to me anywhere else. I feel like every other year someone will invent a new pip env . pyvenv (see comment section, edited) is now deprecated and venv is recommended (shipped as part of Python 3.6 installation) is another confusion. Lest not forget the…
Conda is especially useful if you want to use scientific packages on Windows.
Probably exciting for VS users.
I’m using Anaconda because it was recommended in a step by step tutorial for playing with deep learning. What would be involved in removing it from my system and moving instead to this set of tools? Not necessarily looking for s step by step answer, just for general suggestions. My guess is: find out which python the deep learning tools are using, remove Anaconda, and reinstall the python version needed, using the to…
I’m using Anaconda because it was recommended in a step by step tutorial for playing with deep learning. What would be involved in removing it from my system and moving instead to this set of tools? Not necessarily looking for s step by step answer, just for general suggestions. My guess is: find out which python the deep learning tools are using, remove Anaconda, and reinstall the python version needed, using the to…
Anaconda might be nicer if the packages you need are C-based and would need compiling on your platform. Depends on your use-case. For experimentation/playing around, stick with what works for you. But if a project required me to install Anaconda, I wouldn’t take it seriously. And pipenv is pretty easy to use, too. So if you plan on distributing it or open-sourcing it, understanding how most other people manage dependencies ouside of conda is going to be useful.