m1_huggingface_diffusers_demo VS jupyter-collaboration

The trick is that you have to deactivate the virtual environment and then resource it after adding Jupyter to that virtual environment.

Most shells cache executable paths, so the path for jupyter will be the global path, not the one for your virtual environment. This is unfortunately not at all obvious and leads to very hard to track down bugs that seem to disappear and reappear if you aren't familiar with the issue.

I have a recipe here which always works: https://github.com/nlothian/m1_huggingface_diffusers_demo#se...

If you don't have requirements.txt then do this: `pip3 install jupyter` for that line, then `deactivate` and `source ./venv/bin/activate`.

This is how I did it on an M1 in September: https://github.com/nlothian/m1_huggingface_diffusers_demo

I think it probably needs updating now, but it should give you something to start with.

On my M1 MAx with 32 GB I'm getting 1.5 iterations/second (ie, ~30 seconds for the standard 50 iterations) using this example: https://github.com/nlothian/m1_huggingface_diffusers_demo

Out of interest I've been running a bunch of the huggingface version of StableDiffusion using the M1 accelerated branch on my M1 Max[1]. I'm getting 1.54 it/s compared to 2.0 it/s for a Nvidia T4 Tesla on Google Collab.

T4 Tesla gets 21,691 queries/second for for ResNet, compared to 81,292 q/s for the new H100, 41,893 q/s for the A100 and 6164 q/s for the new Jetson.

So you can expect maybe 15,000 q/s on a M1 Max. But some tests seem to indicate a lot less[2] - not sure what is happening there.

[1] Setup like this: https://github.com/nlothian/m1_huggingface_diffusers_demo

[2] https://tlkh.dev/benchmarking-the-apple-m1-max#heading-resne...

https://github.com/jupyterlab/jupyter-collaboration

And then vscode sessions and thus macros could be recorded from ide events and appended to a jupyter_ydoc: https://github.com/jupyter-server/jupyter_ydoc

Do you have an example of how this works with another tool/language?

I don't know if I understood it correctly but maybe you could:

- Upload your notebook to Github, then create a url with Binder (part of the jupyter ecosystem) directly to an editing/fiddling playground: https://mybinder.org/

- If by user-local you mean on their own machine, they can clone your repo and run their own jupyterlab to fiddle

- If everything should stay on your own computer/server, you could share a link to your own jupyterlab and collaborate with others in real-time: https://jupyterlab-realtime-collaboration.readthedocs.io/en/...