161–170 of 199 posts
What’s the current best-practice workflow for using with git? I see jupytext discussed in the comments but this saves the .py exported file. Is there a good clean method for storing tbe .ipynb files into git? It maybe is easier without the cell outputs?
I have been using VSCode notebooks with .ipynb file extensions, this gives me many advantages as I am able to configure things I'm not able in JupyterLab. I also have access to a very rich ecosystem of plugins. If there is anyone aware of VSCode as a solution but keeps using JupyterLab, could they explain why?
I've not been back to full-fledged Jupyter since getting in to VSCode. Most of my analytical work now is done in .py files, broken up into blocks with `#%%`. Real notebooks feel really clunky since adopting the approach.
I have been using VSCode notebooks with .ipynb file extensions, this gives me many advantages as I am able to configure things I'm not able in JupyterLab. I also have access to a very rich ecosystem of plugins. If there is anyone aware of VSCode as a solution but keeps using JupyterLab, could they explain why?
I've not been back to full-fledged Jupyter since getting in to VSCode. Most of my analytical work now is done in .py files, broken up into blocks with `#%%`. Real notebooks feel really clunky since adopting the approach.
Personally I've since gone full literate programming mode to the point that I care far more about the narrative and documentation (of methods and results) that I will build and modify tools rather than go back to the Matlab way. I have been looking at Quarto but haven't had the time to see if I can transition my existing (and target/ideal) workflows.
I know it gets a lot of hate but ipynb have a lot of advantages as a format for building small custom tools for modification/transformation. Most of the complaints ultimately seem to boil down to not having tools that do what you want. Only want to diff the code cells? That's easy in a python utility that loads the notebook and looks at it intelligently. You can also use pre-commit to modify the notebook and strip out things that don't belong in git.
(Also nbdev... exists... and is a good example of how tools can help. Unfortunately it's too tied to GitHub functionality and the developer is a GitHub zealot who is oddly brittle and takes offense and demands justification if anyone mentions not wanting to rely on GitHub)
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Really interesting setup. What kind of monthly expense does this run? And on a separate but related note, does it change the way you think about how you spend your time coding? (Assuming the costs do ramp up with usage such that time literally does equal money?)
I’ve no idea what the cost is since the company pays for it — I do need the horsepower to run some really large models and I suspect most people don’t need this kind of spec. But for my company it’s just part of the cost of doing business. There’s no IT and I can provision instances of any type (subject to limits) at any time.
What’s the current best-practice workflow for using with git? I see jupytext discussed in the comments but this saves the .py exported file. Is there a good clean method for storing tbe .ipynb files into git? It maybe is easier without the cell outputs?
I keep experimenting with Jupyter in the context of telemetry/fault analysis and then hitting a wall with it where: - I get an analysis that I like, but there isn't a good way to share it with others, so I end up just taking screenshots. - There isn't a good way to take the same analysis and plug new data into it, other than to copy-paste the entire notebook. - The process to "promote" fragments of a notebook into be…
What do you use to collect telemetry data?
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> The process to "promote" fragments of a notebook into being reusable functions seemed very high-friction: basically you're rewriting it as a normal Python package and then adding that to Jupyter's environment. Don't get in that situation to begin with. Pop an `%load_ext` `%autoreload 2` at the top and just write functions in an imported .py file from the get go.
Ok, but let's also acknowledge that the mental model and levels of abstraction increased significantly, which presents a barrier to people without any prior software engineering experience, who are just getting into Python for "data science".
Anyone got experience with the real time collaboration and have feedback?
I'm surprised I had to scroll this far down for anyone to ask this question. I've been trying to work out exactly how it works myself for the last hour, and there is no clear indicator on how to activate this mode. No documentation either, barring: https://github.com/jupyterlab/jupyter_collaboration What am I supposed to be doing here?
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Have you tried pyCharm? I believe even community (free) edition should have support You are looking for
Pycharm's notebook debugger is top notch, but their notebook implementation is, imo, clunky and slow.
The VS Code implementation of notebooks has too much vertical space for me.
I keep experimenting with Jupyter in the context of telemetry/fault analysis and then hitting a wall with it where: - I get an analysis that I like, but there isn't a good way to share it with others, so I end up just taking screenshots. - There isn't a good way to take the same analysis and plug new data into it, other than to copy-paste the entire notebook. - The process to "promote" fragments of a notebook into be…
> I get an analysis that I like, but there isn't a good way to share it with others, so I end up just taking screenshots. What would be your preferable way of sharing your analysis with others? - You can turn jupyter notebooks into pdfs directly in the jupyter UI. - You can upload them to Gitlab/Github and share the link to the rendered result. - You can upload them to Colab/Binder/Kaggle and let people play with the…
But it sounds from both your comment and the many sibling replies that there are a number of tools now directly addressing this space, so I should definitely re-evaluate what is available.