Guides for dependency-management
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Why requirements.txt isn't enough
If you're using only requirements.txt to manage your dependencies, you're in trouble. -
The minimal conda cheatsheet
The smallest conda cheatsheet you'll find around. -
Overview of python dependency management tools
I briefly describe each tool (pip, venv, pip-tools, pyenv, conda, pipenv, poetry and Docker), why it’s created and the problems it’s tackling. You can find a table summarizing all the information and the usual setups people use. -
Importing packages in Jupyter notebooks
Seeing “ImportError: No module named tensorflow” but you know you installed it? Sometimes you can import packages from the console, but not from the Jupyter notebook? !pip install tensorflow sometimes just does not work?
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The difference between conda and pip and how not to break your environment again?
One of the first things I’ve noticed while coming into Machine Learning Engineer role was package management mess. People often ask me “Should I use conda or pip to install packages?”. “Is conda just python with preinstalled packages?”. “I am getting some compiler errors. I thought we’re using just python?!”.