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[D] Efficient workflow with colab/jupyter?

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

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.

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?

submitted by /u/zuuuhkrit
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.