I’m struggeling how to efficiently use Google’s colab facility.

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My normal workflow is:

1. Fiddle around in JupyterLab untill I have some result.
2. Move code into standalone .py libraries, clean-up the JupyterLab notebook to call the library functions.
3. 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.

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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.

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What is your workflow with colab?