jupytext
nbdev
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jupytext | nbdev | |
---|---|---|
20 | 45 | |
6,389 | 4,714 | |
- | 1.0% | |
8.9 | 6.9 | |
5 days ago | 7 days ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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jupytext
- The Jupyter+Git problem is now solved
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Do you git commit jupyter notebooks?
Jupytext (https://github.com/mwouts/jupytext) has been designed exactly for this
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The hatred towards jupyter notebooks
jupytext is your friend.
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Edit notebooks in Google cloud
So if you run your own jupyter server, -jupy+text can be a great workflow : it takes your notebook synchronized with other formats (python file, makdown, ...), so you can edit your py/md file with neovim, and refresh the browser to execute the notebook.
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Rant: Jupyter notebooks are trash.
Automatically convert ipynb files to py when saving them on JupyterLab
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JupyterLite is a JupyterLab distribution that runs in the browser
The format is only partially invented, it follows Jupytext [0], but adds support for cell metadata. There is no obvious way to get that in fenced codeblocks, especially with the ability to spread it over multiple lines so it plays well with version control.
One more consideration is that it's not "Markdown with code blocks interspersed", one might as well use plaintext or AsciiDoc.
Of course there are tradeoffs.. I wish I had more time to work on it.
[0]: https://github.com/gzuidhof/starboard-notebook/blob/master/d...
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Many write research papers in R Markdown - What is the alternative setup in Python?
Using jupytext (allows you to open .md files as notebooks) + jupyter gives you pretty much the same experience. The main issue is that the cell's output will be discarded. To fix it, you can use ploomber to generate an output HTML, so the workflow goes like this:
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Jupyter Notebooks.
First, the format. The ipynb format does not play nicely with git since it stores the cell's source code and output in the same file. But Jupyter has built-in mechanisms to allow other formats to look like notebooks. For example, here's a library that allows you to store notebooks on a postgres database (I know this isn't practical, but it's a great example). To give more practical advice, jupytext allows you to open .py files as notebooks. So you can develop interactively but in the backend, you're storing .py files.
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Ask HN: Are there any good Diff tools for Jupyter Notebooks?
You can use jupytext to maintain dual .py/.ipynb representation of notebooks and keep both versions in sync:
https://github.com/mwouts/jupytext/blob/main/docs/paired-not...
It works both ways, it can update the .py file each time you save the notebook, or you can edit the .py file and have the jupytext command line tool update the .ipynb.
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/
- 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
- Rant: Jupyter notebooks are trash.
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Fast.ai starts a corporate partnership program
You may know fast.ai as a popular deep learning course. There is also a deep learning library with the same name (https://github.com/fastai/fastai) as well as software development tools like nbdev (https://nbdev.fast.ai/).
fast.ai has been offering education and tools for free for over 7 years, and has been approached by many companies asking for help. This program offers an avenue for business to get relevant professional services and support.
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Reproducible environment in nbdev with Pipenv
If you’re reading this, you’re probably familiar with nbdev, a system for creating Python libraries from Jupyter notebooks.
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nbdev formating and linting
repos: - repo: https://github.com/fastai/nbdev rev: 2.2.10 hooks: - id: nbdev_clean - id: nbdev_export
What are some alternatives?
jupyter - An interface to communicate with Jupyter kernels.
papermill - 📚 Parameterize, execute, and analyze notebooks
rmarkdown - Dynamic Documents for R
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
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]
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
rr - Record and Replay Framework
Jupyter-PowerShell - Jupyter Kernel for PowerShell
nbdime - Tools for diffing and merging of Jupyter notebooks.
vim-ipython-cell - Seamlessly run Python code in IPython from Vim