jupyterlab-gitplus
nbdev
jupyterlab-gitplus | nbdev | |
---|---|---|
7 | 45 | |
110 | 4,755 | |
0.0% | 0.9% | |
1.2 | 6.5 | |
about 1 year ago | 10 days ago | |
TypeScript | Jupyter Notebook | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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?
nbdev
- The Jupyter+Git problem is now solved
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What is literate programming used for?
One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev
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GitHub Accelerator: our first cohort and what's next
- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.
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Startups are in first batch of GitHub OS Accelerator
9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/
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Start learning python for a Statistician with SAS experience and little R experience
See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.
- FastKafka - free open source python lib for building Kafka-based services
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isn't this just too much for a take home assignment?
You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff
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Any experience dealing with a non-technical manager?
nbdev: jupyter notebooks -> python package
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Resources to bridge the gap between jupyter notebooks and regular python development
Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)
- Rant: Jupyter notebooks are trash.
What are some alternatives?
jupyter-vim-binding - Jupyter meets Vim. Vimmer will fall in love.
papermill - 📚 Parameterize, execute, and analyze notebooks
vscode-jupyter - VS Code Jupyter extension
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
livebook - Automate code & data workflows with interactive Elixir notebooks
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
jupyterlab-git - A Git extension for JupyterLab
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
pyro - Deep universal probabilistic programming with Python and PyTorch
rr - Record and Replay Framework
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.
Jupyter-PowerShell - Jupyter Kernel for PowerShell