Comments (6)
Good catch, I had not noticed it became this big until I cloned it yesterday. It was foolish of me to include all those wheels, but it was before scikit-ued was available on conda-forge.
I'm going through the BFG website. Seems like a good tool. Once I have successfully rewritten the history, I will let you know.
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@KOLANICH Thanks to your suggestions, I was able to remove all wheels from the git history. Cloning a fresh version of the repo confirms that the size (of the .git folder) has been reduced from 180MiB down to 20MiB.
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You still have binaries in the repo, for example https://github.com/LaurentRDC/scikit-ued/tree/master/docs/source/_themes/sphinx_rtd_theme/static/fonts and https://github.com/LaurentRDC/scikit-ued/blob/master/external/cif2cell-1.2.10/PyCifRW-3.3.tar.gz, pdf and I remember there were some images somewhere. Binaries should be handled by LFS.
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I switched the large files to LFS, but then the CI stopped working. I've decided against this complication. I might try this down the road, but right now it isn't worth the trouble.
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Thank you for the clarification.
switched the large files to LFS
LFS is not for large files, it is for any binary files which are treated by git inefficiently.
but then the CI stopped working.
It requires git-lfs be preinstalled in the build environment. Or you can install it manually and then manually get the objects.
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-.md filter=lfs diff=lfs merge=lfs -text
-.sh filter=lfs diff=lfs merge=lfs -text
-*.rst filter=lfs diff=lfs merge=lfs -text
You are doing it wrong. LFS is only for binary (non-text) files like jpeg, png and bmp images (but svg and vrml are text formats, so shouldn't be handled by lfs), wav sound, archives and other files which don't diff well .... Text files (source code, markdown, svg, OpenSCAD) are handled by git itself well. Using LFS for them is waste of resources and probably troubles with merging.
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Related Issues (10)
- Slow import HOT 1
- silent relicensing to a viral license is not good HOT 2
- where are the tutorials data sets? HOT 3
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- baseline_dt doesn't work well for data with high spot and noisy background HOT 5
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