I am getting the following error when I try the above:
` /opt/conda/envs/torch_38/lib/python3.8/site-packages/clearml/backend_api/session/client/client.py in new_func(self, *args, **kwargs)
374 @wrap
375 def new_func(self, *args, **kwargs):
--> 376 return Response(self.session.send(request_cls(*args, **kwargs)))
377
378 new_func.name = new_func.qualname = action

TypeError: init() missing 1 required positional argument: 'items' `

Hi AgitatedDove14 , I am not uploading anything explicitly, and when I look at the UI Models tab I can only see the regular "{Project Name} - epoch={#}" and in addition "{Project Name} - {project_id}" so I am not sure what is really uploaded.. from the name of it it sounds like model weights and buffers (non-trainable)

AgitatedDove14 Thanks, I am aware of the auto_connect_frameworks and it makes sense for torch.save (and probably other stuff) however if it is called about 10 times during model initialization it seem excessive and I would be happy to avoid this behavior during model initialization if possible. do you know what is actually triggering the uploads?

thanks AgitatedDove14 , I will be happy to test it, however I didn't understand it fully.
I can see how it works in the single machine case, however if I want multiple machines syncing with the optimizer, for pulling the sampled hyper parameters and reporting results, I can't see how it would work

if I can't "pull", execute, report tasks from the same persistent python script it doesn't solve the problem of avoiding rerunning some heavy setup for a lightweight trial