Use one conda per project to stay away from the trouble! It isolates different python and package versions so they do not interact with each other. (It actually does far more, but we’ll stop here 🤓)Ĭonda can do that because is a package manager, but also an environment manager.
It compiles packages before publishing to the package repository so you don’t get compiler errors.It gives you the power to use any python version you want!.It allows you to use different versions of the same package.it does not know anything about python versions - python 2, python 3? Python 3.5 or 3.6? It can’t help with that.Ĭonda does far more than pip! It’s made for people like you, like me, people doing machine learning and data science.for many packages, it uses your compiler, which could be incompatible - this is where your compiler errors are coming from.it does not support multiple versions of same package installed - that’s why you can’t have to projects using different versions of same package!.The difference is that pip does just that and nothing else! It downloads and installs packages you want to use.
Pip (recursive “pip Installs Packages”) is a Python package installer. I can guarantee it does not have to be like that!Īfter you read what pip and conda do and how they work, you won’t be breaking your environment again. Everybody is mix and matching words pip, conda and install until it starts working! And it usually works, until it breaks your environment completely.Īnd you do not know why it broke so you remove and install all packages from scratch. I thought we’re using just python?!”.Īnd I did not know what to answer. “Is conda just python with preinstalled packages?”. People often ask me “Should I use conda or pip to install packages?”. One of the first things I’ve noticed while coming into Machine Learning Engineer role was package management mess.
0 kommentar(er)
