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

Exactly 🙂

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 -

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

@<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.

@<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.

@<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?

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

@<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?

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

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

But we do use S3

@<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

@<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?

Either? 🙂