jupyterlab-gitplus
nbstripout
jupyterlab-gitplus | nbstripout | |
---|---|---|
7 | 4 | |
110 | 1,147 | |
0.0% | - | |
1.2 | 7.6 | |
about 1 year ago | about 2 months ago | |
TypeScript | Python | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jupyterlab-gitplus
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Difftastic, a structural diff tool that understands syntax
If you are in need of a diff tool for jupter notebooks use https://www.reviewnb.com/ and for word documents use https://www.simuldocs.com/
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The Jupyter+Git problem is now solved
- GitHub PR code reviews with ReviewNB[4]
Alternatively, if you don't care about cell outputs then Jupytext[5]
Disclaimer: I built ReviewNB. It's a completely bootstrapped business, 5 years in the making and now used by leading DS teams at Meta, AWS, NASA JPL, AirBnB, Lyft, Affirm, AMD, Microsoft & more (https://www.reviewnb.com/#customers)
[1] https://github.com/jupyterlab/jupyterlab-git
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While you wait for GitHub to finish building Jupyter Notebook reviews
Already a GitHub plugin that does this very nicely: ReviewNB
- Rich Jupyter Notebook Diffs on GitHub... Finally.
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[Noob question] Why are notebooks not used in production ?
For version control: https://www.reviewnb.com/ helps. Agree with the rest but some experimental notebooks are useful to track/version control.
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Nbdev: Create delightful software with Jupyter Notebooks
It's not focused on collaboration, but it does add some critical pieces that otherwise make Jupyter development frustrating when working with a team. Specifically: `nbdev_prepare` ensures that diffs are as small as possible, by removing and standardising notebook metadata; and `nbdev_fix` fixes merge conflicts so that they are cell-level, rather than line level, so they can be opened and fixed in notebooks.
Something else we've found helpful for collaboration (not associated - just happy users) is this: https://www.reviewnb.com/ . It means we can get a nice notebook-based PR workflow.
Real-time collaboration is available in Jupyter nowadays: https://jupyterlab.readthedocs.io/en/stable/user/rtc.html . nbdev doesn't have any extra functionality for it, however -- but it should work fine in this environment.
- Ask HN: Are there any good Diff tools for Jupyter Notebooks?
nbstripout
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Tips for using Jupyter Notebooks with GitHub
If you'd like to automatically remove empty / tagged cells or retroactively apply this filter to your git history, you can read the nbstripout documentation on GitHub.
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Ask HN: Are there any good Diff tools for Jupyter Notebooks?
I used something as a precommit hook in the past that remove plots and other rendered content and only kept text and code in git index. I'm almost sure it was https://github.com/kynan/nbstripout but it's been a while and I could be wrong.
Once the hook was in place git diff worked well enough to not need any other diffing tool.
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Notebooks suck: change my mind
As far as versioning, I use nbstripout (notebook strip out) I think there are alternatives too.
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NumPy 1.20 Released
You can use it with source control, I do it for about 18 notebooks I use on a daily basis:
https://github.com/kynan/nbstripout
What are some alternatives?
jupyter-vim-binding - Jupyter meets Vim. Vimmer will fall in love.
vscode-jupyter - VS Code Jupyter extension
nbdime - Tools for diffing and merging of Jupyter notebooks.
livebook - Automate code & data workflows with interactive Elixir notebooks
clerk - ⚡️ Moldable Live Programming for Clojure
jupyterlab-git - A Git extension for JupyterLab
pluggy - A minimalist production ready plugin system
pyro - Deep universal probabilistic programming with Python and PyTorch
Jupyter Notebook (IPython) - Multi-user server for Jupyter notebooks
notebooks - Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️