MLFlow users, what would you want from an integration with GitLab?

1. incubation-engineering

   1 18 - -

   I took a look at few different options, the main issue is that GitLab is ruby, while most options (like nbdime) are in python. It also needs to work by default, so zero effort for the user. What I did was create a markdown from each version, cleaning up a bit metadata and some noisy outputs, and diff them (https://gitlab.com/gitlab-org/incubation-engineering/mlops/rb-ipynbdiff). It's an MVP, but it works well enough and allows for diffing output as well (I will be adding some metadata back soon too). The next step is create a semantic diff algorithm over the JSON tree, and actually render the diffs per cell.

2. CodeRabbit

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3. databooks

   2 2 111 6.6 Python

   A CLI tool to reduce the friction between data scientists by reducing git conflicts removing notebook metadata and gracefully resolving git conflicts.

   

   If you're working on diffs for Jupyter Notebooks, it's worth looking into this: https://github.com/datarootsio/databooks

4. orchest

   3 44 4,112 4.5 TypeScript

   Build data pipelines, the easy way 🛠️

   

5. ploomber

   4 121 3,558 6.4 Python

   The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

   

   I recommend checking out ploomber, great integration with notebooks and Git!

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