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

@<1523701083040387072:profile|UnevenDolphin73> : If I do, what should I configure how?

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 -

@<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?

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...

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())

Heh, good @<1523704157695905792:profile|VivaciousBadger56> 😁
I was just repeating what @<1523701070390366208:profile|CostlyOstrich36> suggested, credits to him

@<1523701083040387072:profile|UnevenDolphin73>

Exactly 🙂

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...

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

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

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

The documentation is messy, I’ve complained about it the in the past too 🙈

@<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)

2. I delete the "C:\path\to\mymodel.zip", because it would not be available on my colleagues' computers.

3. 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> : How do you figure? In the past, my colleagues and I just shared the .zip file via email / MS Teams and it worked. So I don't think so.

@<1523704157695905792:profile|VivaciousBadger56> It seems like whatever you pickled in the zip file relies on some additional files that are not pickled.

Do you mean "exactly" as in "you finally got it" or in the sense of "yes, that was easy to miss"?

Either? 🙂

🙂

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

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

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

@<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

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

@<1523701083040387072:profile|UnevenDolphin73> : I do not see any way to download the model manually from the web app either. All I see is the link to the file on my harddrive (see shreenshot).

The second process says there is not file at all. I think, all that happened is that the update_weights only uploaded the location of the .zip file (which we denote as a .model file) on my harddrive, but not the file itself.

From the one you sent - None

Hi all, sorry for not being so responsive today 🙏

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 🙂