@<1523701070390366208:profile|CostlyOstrich36>
My training outputs a model as a zip file. The way I save and load the zip file to make up my model is custom made (no library is directly used), because we invented the entire modelling ourselves. What I did so far:
output_model = OutputModel(task=..., config_dict={...}, name=f"...")
output_model.update_weights("C:\io__path\...", is_package=True)
and I am trying to load the model in a different Python process with
mymodel = task.models['output'][0]
mymodel = mymodel.get_local_copy(extract_archive=True, raise_on_error=True)
and I get in the clearml cache a .
training.pt file, which seems to be some kind of archive. Inside I have two files named data.pkl
and version
and a folder with the two files named 86922176
and 86934640
.
I am not sure how to proceed after trying to use pickle, zip and joblib. I am kind of at a loss. I suspect, my original zip file might be somehow inside, but I am not sure.
Sure, we could simply use the generic artifacts sdk, but I would like to use the available terminological methods and functions.
How should I proceed?
We'll try to add referenced to that in other places as well 👍
We're certainly working hard on improving the documentation (and I do apologize for the frustrating experience)
It should store it on the fileserver, perhaps you're missing a configuration option somewhere?
@<1523701087100473344:profile|SuccessfulKoala55> Also, I think that - in this case, but also in other cases - the issue is not just the documentation, but also the design of the SDK.
@<1523704157695905792:profile|VivaciousBadger56> It seems like whatever you pickled in the zip file relies on some additional files that are not pickled.
@<1523701087100473344:profile|SuccessfulKoala55> : That is the link I posted as well. But this should be mentioned also at places where it is about about the external or non-external storage. Also it should be mentioned everywhere we talk about models or artifacts etc. Not necessarily in details, but at least with a sentence and a link.
@<1523701087100473344:profile|SuccessfulKoala55> I think I might have made a mistake earlier - but not in the code I posted before. Now, I have the following situation:
- In my training Python process on my notebook I train the custom made model and put it on my harddrive as a zip file. Then I run the code
output_model = OutputModel(task=task, config_dict={...}, name=f"...")
output_model.update_weights(weights_filename=r"C:\path\to\mymodel.zip", is_package=True)
-
I delete the "C:\path\to\mymodel.zip", because it would not be available on my colleagues' computers.
-
In a second process, the model-inference process, I run
mymodel = task.models['output'][-1]
mymodel = mymodel.get_local_copy(extract_archive=True, raise_on_error=True)
and get the error
ValueError: Could not retrieve a local copy of model weights 8ad4db1561474c43b0747f7e69d241a6, failed downloading
I do not have an aws S3 instance or something like that. This is why I would like to store my mymodel.zip file directly on the ClearML Hosted Service. The model is around 2MB large.
How should I proceed?
@<1523701083040387072:profile|UnevenDolphin73>
@<1523701083040387072:profile|UnevenDolphin73> : I see. I did not make the connection that output_uri=True
is what I was missing. I thought this was the default. But the default is actually "None", which is different than "True".
@<1523701083040387072:profile|UnevenDolphin73> : I do not get this impression, because during update_weights
I get the message
2023-02-21 13:54:49,185 - clearml.model - INFO - No output storage destination defined, registering local model C:\Users..._Demodaten_FF_2023-02-21_13-53-51.624362.model
Well you could start by setting the output_uri
to True
in Task.init
.