[D] Efficient workflow with colab/jupyter?
I’m struggeling how to efficiently use Google’s colab facility.
My normal workflow is:
- Fiddle around in JupyterLab untill I have some result.
- Move code into standalone .py libraries, clean-up the JupyterLab notebook to call the library functions.
- Create testcases that test the standalone .py libraries, refactor code more.
This tandem between Jupyter and a traditional .py IDE helps to get code that is clean and testable. My notebooks tend to be messy, they aren’t unit-tested and might not work anymore in the future.
This doesn’t work that well with Google colab – it’s not that easy to move code to libraries and have it available in Google colab. But I would like to use the computing power that comes with colab.
What is your workflow with colab?