I was thinking that the current "best" model/architecture may change fairly frequently. Obviously you wouldn't want to download 100MB every time the application starts, but maybe amortized every time there is a significant jump would be good.
Anyway, I haven't seen anything like this, so was curious.
Comments
Question:
Does anyone know of a library for loading models/weights from a registry of some sort?
Yes - pretty much every Deep Learning library. Caffe, Torch, Theano, TensorFlow etc (that's kinda what this link is about?)
Just use Keras on top of TensorFlow as shown at this link.
Well, this is fairly manual. More like:
my_model = registry.get("tensorflow://github.com/asdf/models/imagine/latest") ... my_model.push("...")
That's hundreds and hundreds of MBs you are downloading. It should never change, so it hardly seems a critical piece of functionality.
I guess someone could build it, sort of like the datasets you can download in SciKit or R, or the trained models in NLTK/Spacy.
In-fact I've almost come the full circle on this and think it might be a good idea.
Weird - I didn't think people on the internet could change their mind.
Heh :).
I was thinking that the current "best" model/architecture may change fairly frequently. Obviously you wouldn't want to download 100MB every time the application starts, but maybe amortized every time there is a significant jump would be good.
Anyway, I haven't seen anything like this, so was curious.
You mean something like this:
https://github.com/spacy-io/sputnik
?