@<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

I am not sure if it the fact the name of the file ends with .model is an issue - but that would be somewhat crazy design...

But, I guess @<1523701070390366208:profile|CostlyOstrich36> wrote that in a different chat, right?

Well you could start by setting the output_uri to True in Task.init .

I can only say I’ve found ClearML to be very helpful, even given the documentation issue.
I think they’ve been working on upgrading it for a while, hopefully something new comes out soon.
Maybe @<1523701205467926528:profile|AgitatedDove14> has further info 🙂

Hi all, sorry for not being so responsive today 🙏

Heh, well, John wrote that in the first reply in this thread 🙂
And in Task.init main documentation page (nowhere near the code), it says the following -

Either? 🙂

"Messy" is putting it nicely.

missing a configuration option

Which one, where? Any idea? I did not set output_uri - do I have to do that?

I am refering to

• None
• None

@<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're certainly working hard on improving the documentation (and I do apologize for the frustrating experience)

By the way, output_uri is also documented as part of the Task.init() docstring ( None )

@<1523701087100473344:profile|SuccessfulKoala55> : I referenced this conversation in the issue None

I wouldn't put past ClearML automation (a lot of stuff depend on certain suffixes), but I don't think that's the case here hmm

@<1523704157695905792:profile|VivaciousBadger56> regrading: None
Is this a discussion or PR ?
(general ranting is saved for our slack channel 🙂 )

We'll try to add referenced to that in other places as well 👍

It is documented at None ... super deep in the code. If you don't know that output_uri in TASK's (!) init is relevant, you would never know...

@<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.

@<1523704157695905792:profile|VivaciousBadger56> I'm not sure I'm following you - is the issue not being able to upload to the ClearML server or to load the downloaded file?

@<1523701083040387072:profile|UnevenDolphin73> : Thanks, but it does not mention the File Storage of "ClearML Hosted Server".

@<1523701083040387072:profile|UnevenDolphin73> : From which URL is your most recent screenshot?

Yes, you're correct, I misread the exception.
Maybe it hasn't completed uploading? At least for Datasets one needs to explicitly wait IIRC

We have the following, works fine (we also use internal zip packaging for our models):

model = OutputModel(task=self.task, name=self.job_name, tags=kwargs.get('tags', self.task.get_tags()), framework=framework)
model.connect(task=self.task, name=self.job_name)
model.update_weights(weights_filename=cc_model.save())

Exactly 🙂

Hi @<1523704157695905792:profile|VivaciousBadger56> , you can configure Task.init(..., output_uri=True) and this will save the models to the clearml file server

FWIW It’s also listed in other places @<1523704157695905792:profile|VivaciousBadger56> , e.g. None says:

In order to make sure we also automatically upload the model snapshot (instead of saving its local path), we need to pass a storage location for the model files to be uploaded to.
For example, upload all snapshots to an S3 bucket…

Unbelievable! That worked.

From the one you sent - None

I have already been trying to contribute (have three pull requests), but honestly I feel it is a bit weird, that I need to update a documentation about something I do not understand, while I actually try to evaluate if ClearML is the right tool for our company...