After you store the model in ClearML server accessing it later becomes almost trivial 🙂

Hi HungryArcticwolf62 ,
from what I understand you simply want to access models afterwards - correct me if I'm wrong.
What I think would solve your problem is the following:
task = Task.init(...., output_uri=True)This should upload the model to the server and thus make it accessible by other entities within the system.
Am I on track?

HungryArcticwolf62 the new clearml-serving is almost out (eta late next week), you can already start playing here:
https://github.com/allegroai/clearml-serving/tree/dev
Example:
train+serve
https://github.com/allegroai/clearml-serving/tree/dev/examples/sklearn

Hi AgitatedDove14 , CostlyOstrich36
Thanks for the links. I see that clearml-serving supports a predefined list of engines, transformer no included. Do you have any documentation on how one would implement an engine and integrate it into the on prem version?

HungryArcticwolf62 transformer model is at the end a pytorch/tf model, with pre/post processing.
the pytorch/tf model inference is done with Triton (probably the most efficient engine today), where clearml runs the pre/post on a different CPU machine (making sure we fully utilize all the HW. Does that answer the question?
Latest docs here:
https://github.com/allegroai/clearml-serving/tree/dev

expect a release after the weekend 😉

HungryArcticwolf62 , I couldn't find something relevant 😞
AgitatedDove14 , wdyt?

Actually, this opens my mind on what I'm trying to achieve. I'm trying to find a way to store the model (will try using the output_uri argument), and also a way to serve models using clearml-serving. Since I don't know yet how clearml-serving works, I wanted first to archive the correct files.