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

@<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.

Unbelievable! That worked.

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

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

It should store it on the fileserver, perhaps you're missing a configuration option somewhere?

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

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

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

@<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?

@<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> : If I do, what should I configure how?

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…

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

But we do use S3

Exactly 🙂

@<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.

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 -

"Messy" is putting it nicely.

@<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".

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

@<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.

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...

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 🙂

From the one you sent - None

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

Either? 🙂

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