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JupyterLab 4.0

blog.jupyter.org

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Re: JupyterLab 4.0

#141

Post by zelphirkalt »

i_am_toaster wrote: post #133

Was a nightmare making extensions in the previous version. The documentation was very scattered/cluttered between different jupyterlab versions. Don’t think I’ll be coming back to it for 4.0.

Making extensions is really a lot about looking at their own packages in their repos and looking at the API docs, figuring things out from procedure names and sometimes useless comments, that merely rehash the procedure name as a phrase.

Additionally they often point you at their terrible discourse forum for asking questions. More often that not I don't see a good answer there either, when I merely search for one. I think their gitter channel has worked best for me so far, when they did not point me to that forum.

Typescript also helps a bit when compiling.

Sometimes I visit an old bookmark, that looked like part of official docs by the URL, but find it 404ing. Ultimately I agree, that good and accessible docs is not the project's strong side.

I guess I will have to figure some things out again soon when updating extensions to version 4.

Re: JupyterLab 4.0

#142

Post by prometheon1 »

mikepurvis wrote: post #13

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 code themselves.

- You can turn jupyter notebooks into beautiful websites/documents with: https://quarto.org/docs/tools/jupyter-lab.html

- You can add jupyter notebooks to you docs using nbsphinx: https://nbsphinx.readthedocs.io/en/0.9.1/

- You can turn jupyter notebooks into interactive web apps with voila: https://github.com/voila-dashboards/voila

- You can turn jupyter notebooks into presentations with rise: https://github.com/voila-dashboards/voila

Re: JupyterLab 4.0

#143

Post by specproc »

0cf8612b2e1e wrote: post #125
specproc wrote: post #67

Earlier quoted context omitted.

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 must be missing something because I am not immediately seeing the value-add. Do you prefer the separation of input/output or is it something else. I believe all of the debug, extensions, and hinting work the same as the standard notebook.

I'm glad you asked, because you made me think about why. Initially, I guess I just thought it was kinda neat and stuck with it, but on reflection this is what I personally feel I get out of it:

- Same interface for analysis, scripting, and building more complex multi-file pipelines. I can also use the #%% notation to break up and debug scripts, which is probably teaching me all sorts of bad habits but it's something I find helpful.

- Similarly, as another commenter in this thread notes, .ipynbs just don't play as nicely with the other dev tools (e.g., Git, Black) and generally feel like second-class citizens in VSCode.

- I much prefer having the VSCode interactive window on the right, as opposed to having my output dumped out below my code block. I now find using the classic notebook style makes the document much longer and harder to navigate, particularly as I work with text a lot and I'm often outputting large chunks of text for inspection.

This noted, I think this is all possible because I'm rarely producing my final products in notebook format. Neither my boss nor the stakeholders I typically present to can (or have any inclination to) read code, so I don't really need a format others can execute or inspect. I just take the charts and figures and dump to presentations and other normie-friendly documents.

Re: JupyterLab 4.0

#144

Post by MichaelMoser123 »

eesmith wrote: post #135
MichaelMoser123 wrote: post #95

Earlier quoted context omitted.

in python you can turn nested dictionaries and other data structures into json, but only if the data structures doesn't include circular references. I use that a lot. something like >>> d = { "a":1, "b":[1,2,3] } >>> import json >>> print( json.dumps(d, indent=2) ) { "a": 1, "b": [ 1, 2, 3, ] }

Try pprint: >>> d = { "a":1, "b":[1,2,3] } >>> d["d"] = d >>> import pprint >>> pprint.pprint(d) {'a': 1, 'b': [1, 2, 3], 'd': }

thanks, great!

Re: JupyterLab 4.0

#145

Post by henrikgs »

prometheon1 wrote: post #142
mikepurvis wrote: post #13

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…

I'm guessing the las one should link here https://rise.readthedocs.io/en/stable/ and not to voila two times?

Re: JupyterLab 4.0

#146

Post by airocker »

specproc wrote: post #67
rollinDyno wrote: post #22

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.

You cannot put pictures and YouTube videos in #%% .

Re: JupyterLab 4.0

#147

Post by specproc »

airocker wrote: post #146
specproc wrote: post #67

Earlier quoted context omitted.

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.

You cannot put pictures and YouTube videos in #%% .

I have to confess I've never had much need for YouTube videos in my code. Pics are just output as far as I'm concerned.

Naturally, if you need these things then .ipynb makes sense.

Re: JupyterLab 4.0

#148

Post by riialist »

wenc wrote: post #77
digdugdirk wrote: post #72

Earlier quoted context omitted.

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.

VSCode is free if you self-host it. There are corporate tiers with virtual desktops etc, and you can pay for services as GitHub pilot if you want.

Anyhow there a wealth of free extensions to customize it and the setup is really straightforward. I have version management git in a private GitHub project for version management. You can add extensions for rendering graphs in good quality and importing and exporting stuff is easy.

I have not been able to figure out why some people prefer to use Jupiter notebook as it is.

Re: JupyterLab 4.0

#149

Post by zokier »

digdugdirk wrote: post #72
wenc wrote: post #47

Earlier quoted context omitted.

I too use VS Code as my Jupyter platform (running remotely on a powerful EC2 instance with 32 CPUs and 256GB RAM — my own desktop is a 7 year old Intel core i7 with 8GB RAM). VS Code’s Remote extension is amazing, works over any SSH host and seamlessly blends local and remote. It’s also fast since the UI is local while the filesystem and execution is remote. The experience is a lot better than JupyterLab (which I am…

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?)

32 vCPU / 256 GB instances like r6a.8xlarge is about $900/month (r6ad which has local disk is about $100/month more), I don't see there being much other major costs with such setup?

Re: JupyterLab 4.0

#150

Post by Desustorm »

prometheon1 wrote: post #142
mikepurvis wrote: post #13

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…

To add to this, shamelessly self-promoting, Notebooker (https://github.com/man-group/notebooker) is a neat way of scheduling your Jupyter notebooks as parametrisable reports whose results are presented in a little web GUI (either as static HTML, PDF, or as reveal.js slideshow renders)
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