We have a read-only user with personal access token for these things, works seamlessly throughout and in our current on premise servers... So perhaps something missing in the autoscaler definitions?

When I use the APIClient to fetch the tags for the project, I get an empty collection of system tags:

<projects.GetProjectTagsResponse: {
    "tags": [],
    "system_tags": []
}>

So the pipeline runs successfully, I can find all the different tasks, but I cannot see them in the Pipelines tab…

Ah I see, if the pipeline controller begins in a Task it does not add the tags to it…

Thanks Alon. In the full/official documentation the clearml-data CLI is not mentioned anywhere, so perhaps it should be refreshed 😉

I think we're referring to different things here.
I won't be using the UI (and neither will my team).
But as mentioned, we've used DVC before and it adds a lot of junk metadata files to each GitHub PR (many dvc.yaml , dvc.lock and .gitignore files). We're trying to avoid that as much as possible, hence my question about GitHub pull...

I'm trying to decide if ClearML is a good use case for my team 🙂
Right now we're not looking for a complete overhaul into new tools, just some enhancements (specifically, model repository, data versioning).

We've been burnt by DVC and the likes before, so I'm trying to minimize the pain for my team before we set out to explore ClearML.

I think so, it was just missing from the official documentation 🙂 Thanks!

If everything is managed with a git repo, does this also mean PRs will have a messy metadata file attached to them?

FYI @<1523701087100473344:profile|SuccessfulKoala55> (or I might be doing something wrong), but it seems the python migration code comes with carriage returns, so it fails on linux by default (one has to tr -d '\r' to use it)

EDIT: And also it defaults to /opt/allegro/data rather than the recommended /opt/clearml/data which is suggested when installing the server 🤔

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

But we do use S3

Any thoughts @<1523701070390366208:profile|CostlyOstrich36> ?
I wouldn’t want to run the entire notebook, just a specific part of it.

@<1537605940121964544:profile|EnthusiasticShrimp49> It’ll take me still some time to find the MVC that generated this, but I do have the ClearML experiment page for it. I was running the thing from ipython , and was trying to create a task from a function:

I guess in theory I could write a run_step.py , similarly to how the pipeline in ClearML works… 🤔 And then use Task.create() etc?

I'm trying to build an easy SDK that would fit DS work and fit the concept of clearml pipelines.
In doing so, I'm planning to define various Step classes, that the user can then experiment with, providing Steps as input to other steps, etc.

Then I'd like for the user to be able to run any such step, either locally or remotely. Locally is trivial. Remotely is the issue. I understand I'll need to upload additional data to the remote instance, and pull a specific artifact back to the notebo...

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…

CostlyOstrich36 That looks promising, but I don't see any documentation on the returned schema (i.e. workers.worker_stats is not specified anywhere?)

Either? 🙂

From the one you sent - None

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 🙂

I have seen this quite frequently as well tbh!

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

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

AgitatedDove14 Unfortunately not, the queues tab shows only the number of tasks, but not resources used in the queue . I can toggle between the different workers but then I don't get the full image.

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

It was really easy with the attached code, really 👍

I would only maybe suggest adding in the documentation, that if one uses the default recommended install location, then the script can be run without any command line arguments.
I had to momentarily look at the code to see the default paths match my own (though I could've also looked at --help default values 😛 )

At least as far as I can tell, nothing else has changed on our systems. Previous pip versions would warn about this, but not crash.

Created this for follow up, SuccessfulKoala55 ; I'm really stumped. Spent the entire day on this 🥹
https://github.com/allegroai/clearml-agent/issues/134

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 -

1.3.2