You don't need to commit output into the git. I used pre-commit filter in git, where it will strip all output from the notebook before it was committed into repository. This allowed us to review the code changes of notebooks.
Git and Jupyter Notebooks Guide
11–20 of 49 posts
Re: Git and Jupyter Notebooks Guide
#12Earlier quoted context omitted.
What happens to the outputs in this case? I found the outputs to be both the most useful parts of notebooks, but also the most troublesome for diffing and versioning.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
If you want to store (e.g. base64-encoded) outputs in Markdown, you're going to have to scroll past a lot of data to get to the next input cell in a notebook.
Jupyter notebooks store which Jupyter kernel they were run with to generate the outputs.
nbformat (.ipynb with inlined base64 outputs) isn't a sufficient package format: https://github.com/jupyter/enhancement-proposals/pull/103#is...
Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/
Re: Git and Jupyter Notebooks Guide
#13Curious that they discuss several options, but ignore the totally obvious one: just use jupytext [0]. Jupytext is a (tiny) jupyter extension that reads/writes notebooks as python files, with text cells being represented as comments. With jupytext, you do away with the stupid .ipynb format. As long as you don't need to save the cell outputs, which is the case for version control, jupytext is the way to go. People: pip…
Euporie (my terminal Jupyter notebook editor) also supports Jupytext
Re: Git and Jupyter Notebooks Guide
#14Earlier quoted context omitted.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
Sometimes I work on software development, and this mindset («the only valuable asset is the code») makes total sense. But if I work on analytics / datascience projects, the analysis including outputs could be time consuming to run, validate, and visualize. In these cases, it might be required to version the outputs. I’ve never used jupyter for taking notes in a lab setting, but with more and more instruments being co…
Precisely this. When your output is something like research data, or even just something that generally takes a long human time to complete (hours vs Microseconds) it makes a lot of sense to version and keep outputs, at least on major "versions".
Re: Git and Jupyter Notebooks Guide
#15The native GitHub feature in preview will make this a lot better for those able to use it
https://github.blog/changelog/2023-03-01-feature-preview-ric...
Re: Git and Jupyter Notebooks Guide
#16Earlier quoted context omitted.
What happens to the outputs in this case? I found the outputs to be both the most useful parts of notebooks, but also the most troublesome for diffing and versioning.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
"unholy" "cleaner" "stateful" "bizarre" "bad taste" "hygiene"
I'm not sure whether you're unaware or just feigning ignorance, but notebooks are frequently used to share partial results, often in the context of "research", however you may interpret it. Imagine a grad student or data scientist preparing some code and plots to show during a weekly meeting.
In this context, the only thing that matters is quick progress and advancing understanding of a problem. The highly loaded words you employ while blasting the idea of uploading Jupyter notebooks are not relevant here. Wasting time on these things is seen as a bad thing. It's clear why someone using notebooks this way would want the interaction with Git and GitHub to be as seamless as possible: uploading something to GitHub is a very easy way to share it, even if this isn't the platonic ideal.
It will probably cause you some pain, but I've known people to commit binary objects and PDFs to git to accomplish the same ends. ;-)
Re: Git and Jupyter Notebooks Guide
#17Curious that they discuss several options, but ignore the totally obvious one: just use jupytext [0]. Jupytext is a (tiny) jupyter extension that reads/writes notebooks as python files, with text cells being represented as comments. With jupytext, you do away with the stupid .ipynb format. As long as you don't need to save the cell outputs, which is the case for version control, jupytext is the way to go. People: pip…
What happens to the outputs in this case? I found the outputs to be both the most useful parts of notebooks, but also the most troublesome for diffing and versioning.
I use Jupytext since years. It allows me to have three types of synced notebook versions: 1) .ipynb (for opening/running), 2) .md (formatted code+comments, without outputs) and 3) *.py (python formatted, code+comments).
I commit the Markdown-version, but I also use the py-version of notebooks for chained notebook imports. Allows me to split larger notebooks into multiple smaller ones. Both of these options are a blessing and Jupytext works super-robust.
Finally, when I want to archive (and share) notebooks _with_ outputs once in a while, I have a cell at the end to convert (nbconvert) to HTML, and I commit this html file. The Markdown-version remains as a clean basis for commit history. The HTML file is much better suited for sharing and archiving than the ipynb file.
Re: Git and Jupyter Notebooks Guide
#18Earlier quoted context omitted.
What happens to the outputs in this case? I found the outputs to be both the most useful parts of notebooks, but also the most troublesome for diffing and versioning.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
I think it depends a lot on what your git repository is.
If it's specifically source code for anything that's intended to run, then avoiding including the outputs is a smart move. But then, if that's the case, there's a good chance you'd just be committing a .py file.
I like notebooks because they include output alongisde input. For example, Peter Norvig's Pytudes are all brilliant, quick notebooks that solve a particular puzzle[0]. The code itself might not be that interesting to run (unless you really want to confirm his strategy for wordle checks out) but reading through the notebooks makes for a great experience of simultaneously understanding his thought process, and seeing the solution.
I do a bunch of generative art stuff and have recently been experimenting with using notebooks as quick sketches[1]. I really like the workflow and end up with something like a journal that isn't necessarily intended to be ran repeatedly, but read over, where I can see the visual output created, as well as the method for it.
[0] Norvig's extremely cool pytudes, wordle example: https://github.com/norvig/pytudes/blob/main/ipynb/Wordle.ipy... [1] My not anywhere near as cool as Norvig's pytudes example: https://github.com/benrutter/jupyter-sketches
Re: Git and Jupyter Notebooks Guide
#19Earlier quoted context omitted.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
I think it depends a lot on what your git repository is. If it's specifically source code for anything that's intended to run, then avoiding including the outputs is a smart move. But then, if that's the case, there's a good chance you'd just be committing a .py file. I like notebooks because they include output alongisde input. For example, Peter Norvig's Pytudes are all brilliant, quick notebooks that solve a parti…
You don't lose the outputs, they just aren't committed into Git. So for each new clone, you'd need to regenerate the outputs, but on a single clone, the outputs exist and are persistent in the .ipynb form of the notebook (which is not committed). You are correct that the .py version of the notebook is exactly what Git ends up tracking, with the .ipynb being essentially a build product.
(Note that the jupytext paradigm does assume that the outputs can always be recomputed as a function of the inputs. I consider that a best practice, but some might disagree.)
Re: Git and Jupyter Notebooks Guide
#20Earlier quoted context omitted.
What happens to the outputs in this case? I found the outputs to be both the most useful parts of notebooks, but also the most troublesome for diffing and versioning.
Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources. The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or…
How do you capture things like charts or tables produced from long-running notebooks? Do you have a separate system to keep track of these? I prize notebooks with outputs in our data science repo since I can see the results of our analyses years later without having to re-run the notebook. In some cases, the notebooks don't even run anymore since our environment has moved on, but I can still see the graphs and read the text that was associated with that analysis.