kushalkolar

5 ms·

1. 1.▲
   by kushalkolar 2y ago

   I just commented above, see this: https://github.com/vispy/jupyter_rfb/issues/95#issuecomment-...
2. 2.▲
   by kushalkolar 2y ago

   I just remembered, I think there is something weird with Google's servers or the network because performance was very poor even with a custom Google Cloud instance running jupyterlab, see this: https://github.com/vispy&
3. 3.▲
   by kushalkolar 2y ago

   OK now try to do this in 3D with arbitrary projections and interactivity! And guess what, you'd create a rendering engine :) My earlier reply has a link to how GPUs actually push pixels to the screen. There are also some excellent blog
4. 4.▲
   by kushalkolar 2y ago

   Setting the value of a pixel in an image is very different from drawing objects like lines, this is a good introduction: https://graphicscompendium.com/intro/01-graphics-pipeline
5. 5.▲
   by kushalkolar 2y ago

   In the meantime you can use the rendering engine pygfx to create them directly :)
6. 6.▲
   by kushalkolar 2y ago

   Take a look at the examples gallery, it differs greatly from matplotlib. But I guess that will be subjective
7. 7.▲
   by kushalkolar 2y ago

   Fastplotlib is very different from bokeh and holoviz, and has different use cases. Bokeh and holoviz send data to a JS front end that draws (to the best of my knowledge), whereas fastplotlib does everything on the python side and uses jupyt
8. 8.▲
   by kushalkolar 2y ago

   I don't think it's ready yet and I think it might be private at the moment, Cyrille can comment more on it. But if I understand correctly it's a protocol for serializing graphical objects, pretty neat idea.
9. 9.▲
   by kushalkolar 2y ago

   non-jupyter notebook implementations have their quirks, eventually we hope to make a more universal jupyter-rfb kind of library, perhaps using anywidget. Anywidget is awesome: https://github.com/manzt/anywidget People
10. 10.▲
    by kushalkolar 2y ago

    > the data is available on a machine in a cluster rather than on the local machine of a user jupyter-rfb lets you do remote rendering for this, render to a remote frame buffer and send over a jpeg byte stream. We and a number of our scie
11. 11.▲
    by kushalkolar 2y ago

    I do not know ruby but sometimes that's an opportunity to try and make one which others will also find useful :)
12. 12.▲
    by kushalkolar 2y ago

    Thanks, and the purpose was to show what's possible on modest hardware that most people have. We have created gigabytes of graphics that live on the gpu for more complex use cases and they remain performant, but you need a gaming gpu
13. 13.▲
    by kushalkolar 2y ago

    This is exactly why we use jupyter-rfb, I often have large datasets on a remote cluster computer and we perform remote rendering. see: https://fastplotlib.org/ver/dev/user_guide/faq.html#what-fra...
14. 14.▲
    by kushalkolar 2y ago

    Hi! I've seen some of your work on wgpu-py! Definitely let us know if you need help or have ideas, if you're on the main branch we recently merged a PR that allows events to be bidirectional.
15. 15.▲
    by kushalkolar 2y ago

    Rendering frames and saving them to disk can be done with rendercanvas but we haven't exposed this in fastplotlib yet: https://github.com/pygfx/rendercanvas/issues/49
16. 16.▲
    by kushalkolar 2y ago

    Yea the security issue is why I'm pretty sure you can't do it on WGPU, but Vulkan and cupy can fully run locally so it doesn't have the same security concern.
17. 17.▲
    by kushalkolar 2y ago

    Thanks! Yup our focus is not publication figures, matplotlib and seaborn cover that space pretty well.
18. 18.▲
    by kushalkolar 2y ago

    This looks cools thanks! Makes me wonder if there's any way to do that with WGPU if WGPU is interfacing with Vulkan, probably not easy if possible I"m guessing. WGPU has security protections since it's designed for the browse
19. 19.▲
    by kushalkolar 2y ago

    We offer a lot of ways to slice colors, set cmaps and cmap transforms, they are really useful in neuroscience: https://fastplotlib.org/ver/dev/_gallery/line/line_colorslic... https://fastplotl
20. 20.▲
    by kushalkolar 2y ago

    Thanks! That is a great question and one that I've we've been battling with as well. As far as we know, this is not possible due to the way different contexts are set up on the GPU https://github.com/pygfx/pyg
21. 21.▲
    by kushalkolar 2y ago

    Almar made blog posts about the line shader he wrote! https://almarklein.org/triangletricks.html https://almarklein.org/line_rendering.html A big shader refactor was done in this PR: https://gith
22. 22.▲
    by kushalkolar 2y ago

    Thanks! I used pyqtgraph for many years and love what can be done by it, we started off wanting to build something like it but based on WGPU and not bound to Qt.
23. 23.▲
    by kushalkolar 2y ago

    Thanks Ivo! Just to add on, colab is weird and not performant, this PR outlines our attempts to get jupyter-rfb working on colab: https://github.com/vispy/jupyter_rfb/pull/77
24. 24.▲
    by kushalkolar 2y ago

    Different use cases :) Plotly doesn't give the performance and interactive tools required for many neuroscience visualizations. We also focus more on the primitive graphics and, at least not yet, on the more complex "composite&quo
25. 25.▲
    by kushalkolar 2y ago

    In fastplotlib at the end of the day everything is wgpu under the hood, and as the other poster correctly pointed out about numpy being fortran and C wrappers.
26. 26.▲
    by kushalkolar 2y ago

    Almar did some work on speeding up imports a year ago: https://github.com/fastplotlib/fastplotlib/pull/431 but we haven't benchmarked it yet
27. 27.▲
    by kushalkolar 2y ago

    Thanks! > When would you reach for a different library instead of fastplotlib? Use the best tool for your usecase, we're focused on GPU accelerated interactive visualization. Our use cases broadly are developing ML algorithms, user-
28. 28.▲
    by kushalkolar 2y ago

    I dunno why you'd say this, neither of us are fans of LLMs and most of this was written before LLMs were a thing :)