Open bw4sz opened 2 years ago
Hey @bw4sz , I would like to work on this issue, Can you please guide me?
What have you done so far?
It seems like moving to Hugging Face for model distribution is the most common solution here. If we use the pytorch integration:
https://huggingface.co/docs/hub/models-uploading#upload-a-pytorch-model-using-huggingfacehub
then we can use the from_pretrained()
method and pass in any of our models (and potentially others models as well; we can wrap from_pretained
in use_release
as well for backward compatibility):
model.from_pretrained("weecology/deepforest-trees")
I setup an org account https://huggingface.co/weecology and can add @bw4sz & @henrykironde if this sounds like the general way to go.
@henrykironde this will be an issue i'll connect to alive/dead model.
@ethanwhite, @henrykironde and I were discussing that as the number of trained models increase, we probably want some more refined way of versioning them and calling them. Right now they are saved alongside the release tags. Each new model would get a "use_release" method, with redundant code, etc.
An example release looks like:
https://github.com/weecology/DeepForest/releases/tag/1.0.0
Which is fine. When use_release is called, the model checks github for the latest release. A couple problems.