r/MachineLearning · · 1 min read

repo2nb 0.2.0, convert a GitHub repo into a Kaggle/Colab notebook (dependency resolution, reverse mode, incremental sync) [P]

Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.

repo2nb is an open-source CLI that converts a GitHub repo into a runnable Kaggle or Colab notebook: walks the file tree, resolves dependencies, and generates cells, instead of you doing that by hand for a repo you didn't write (a paper's code, a tutorial, someone else's experiment).

0.2.0 highlights:

  • Dependency resolution tries poetry export, then uv export, then requirements.txt, then falls back to an AST import scan if none of those exist. Output is always a plain %pip install cell regardless of which path it took, so poetry/uv are only ever needed locally at generation time, not on Kaggle/Colab.
  • Reverse mode (repo2nb reverse <notebook>) reconstructs the original repo from a generated notebook, using the per-cell path/hash metadata every generated cell now carries. Validates against directory traversal and won't write into a non-empty directory without --force.
  • Incremental sync (repo2nb sync <repo>) does one-directional (repo to notebook) updates: added files get new cells, edited files update in place, deleted files get removed. --dry-run previews the diff.
  • Added a Colab target with its own auth cell (google.colab.userdata.get) rather than reusing the Kaggle secrets flow.

Install: pip install repo2nb

Repo: https://github.com/David-Magdy/repo2nb

Curious whether the dependency-resolution fallback order (poetry > uv > requirements.txt > import scan) matches what people actually run into, or if there's a common setup it'd get wrong.

Any feedback or opinions are much welcomed!

submitted by /u/PolarIceBear_
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