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for the async iter every batch vs every round: does it affect training speed or not much? (thinking of the cases where local iterations might be super fast but communication might be slow, which might often be the case) |
hum, not from what I remember. we didn't actively test with slow communications but it didn't seem like a high time cost in comparaison to the training (sub-seconds vs whole seconds). of course, if the network is very flaky, it might come as an issue. |
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ho good catch, I mixed two tailwindcss classes, fixed in the new commits.
gladly, web UI is not really my strongest suit :) |
It's not mine either but I unfortunately have strong opinions on the matter... If that's fine with you I'll make changes in a follow-up PR rather than this one |
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Co-authored-by: Julien Vignoud <33122365+JulienVig@users.noreply.github.com>
Co-authored-by: Julien Vignoud <33122365+JulienVig@users.noreply.github.com>
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on the way to v3, we have some stuff to iron out, tracked in #686.
taking over some from there.
{Trainer,Model}.fitto return async iterators to follow progression of every part of processing. allows for more reactive UI as it refreshes on every batch rather on every round.