But it doesn't seem to answer how it works in Jupyter notebooks, or if it does at all. Is the GPU acceleration done "client-side" (JavaScript?) or "server-side" (in the kernel?) or is there an option for both?
Because I've used supposedly fast visualization libraries in Google Colab before, but instead of updating at 30 fps, it takes 2 seconds to update after a click, because after the new image is rendered it has to be transmitted via the Jupyter connector and network and that can turn out to be really slow.
Thanks. Yeah I've been baffled as to why just interactive Matplotlib with a Colab kernel is so slow. The Colab CPU is fast (enough), the network is fast, I haven't been able to figure out where the bottleneck is either.
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/jupyter_rfb/issues/95#issuecomment-...
Is google colab slower than an equivalently powerful kernel running on a remote jupyter kernel? Are you running into network problems, or is it something specific to colab?
But it doesn't seem to answer how it works in Jupyter notebooks, or if it does at all. Is the GPU acceleration done "client-side" (JavaScript?) or "server-side" (in the kernel?) or is there an option for both?
Because I've used supposedly fast visualization libraries in Google Colab before, but instead of updating at 30 fps, it takes 2 seconds to update after a click, because after the new image is rendered it has to be transmitted via the Jupyter connector and network and that can turn out to be really slow.