I've been answering there as well 🤕

Pinging about this still, unresolved 🤔

ClearML does not capture our internal libraries and so our functions (pipeline steps) crash with missing modules.

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 😛 )

Well, -ish. Ideally what we're after is one of the following:
Couple a task with a dataset. Keep it visible in it's destined location. Create a dataset separately from the task. Have control over its visibility and location. If it's hidden, it should not affect normal UI interaction (most annoying is having to click twice on the same project name when there are hidden datasets, which do not appear in the project view)

I’ve tracked it down further, it seems the pigar utility does not apply any smart logic there.
The case we have is the following -

• We have a monorepo, but all modules/libs share a common namespace foo ; so e.g. working on module mod , we use from foo.mod import …
• This then looks for a module called foo , even though it’s just a namespace
• In the dist-info requirement, it seems any hyphen, dot, etc are swapped for an underscore, so our site-packages represents this as `foo_m...

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

Just a side note - the 1.1.1 notice keeps popping up even though the server is at 1.1.1 (and I've cleared browser cache etc)

I think I may have brought this up multiple times in different ways :D
When dealing with long and complicated configurations (whether config objects, yaml, or otherwise), it's often useful to break them down into relevant chunks (think hydra, maybe).

In our case, we have a custom YAML instruction !include , i.e.
` # foo.yaml
bar: baz

bar.yaml

obj: !include foo.yaml
maybe_another_obj: !include foo.yaml `

AFAIK that's the only way right now (see my comment here - https://clearml.slack.com/archives/CTK20V944/p1657720159903739?thread_ts=1657699287.630779&cid=CTK20V944 )
Or then if you have the ClearML paid service, I believe there is a "vaults" service, right AgitatedDove14 ?

This is with:
Task.set_offline_mode(True) task = Task.init(..., auto_connect_streams=False)

The only thing I could think of is that the output of pip freeze would be a URL?

Yes that's what I thought, thanks for confirming.

I commented on your suggestion to this on GH. Uploading the artifacts would happen via some SDK before switching to remote execution.
When cloning a task (via WebUI or SDK), a user should have an option to also clone these input artifacts or simply linking to the original. If linking to the original, then if the original task is deleted - it is the user's mistake.

Alternatively, this potentially suggests "Input Datasets" (as we're imitating now), such that they are not tied to the original t...

-ish, still debugging some weird stuff. Sometimes ClearML picks ip and sometimes ip2 , and I can't tell why 🤔

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

Thanks!

That would be nice :)

Let me know if you do; would be nice to have control over that 😁

So a missing bit of information that I see I forgot to mention, is that we named our packages as foo-mod in pyproject.toml . That hyphen then get’s rewritten as foo_mod.x.y.z-distinfo .

foo-mod @ git+

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...

Thanks @<1537605940121964544:profile|EnthusiasticShrimp49> ! That’s definitely the route I was hoping to go, but the create_function_task is still a bit of a mystery, as I’d like to use an entire class with relevant logic and proper serialization for inputs, and potentially I’ll need to add more “helper functions” (as in the case of DataTransformationStep , for example). Any thoughts on that? 🤔

My suspicion is that this relates to https://clearml.slack.com/archives/CTK20V944/p1643277475287779 , where the config file is loaded prematurely (upon import ), so our dotenv.load_dotenv() call has not yet registered.

I think also the script path in the created task will cause some issues, but let’s see…

Sounds like incorrect parsing on ClearML side then, doesn't it? At least, it does not fully support MinIO then

I don't imagine AWS users get a new folder named aws-key-region-xyz-bucket-hostname when they download_folder(...) from an AWS S3 bucket, or do they? 🤔

Basically when running remotely, the first argument to any configuration (whether object or string, or whatever) is ignored, right?

Yes exactly that AgitatedDove14
Testing our logic maps correctly, etc for everything related to ClearML

I'm also getting the following warning, I guess it's some ClearML dependency?
IPython could not be loaded!

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.

Running a self-hosted server indeed. It's part of a code that simply adds or uploads an artifact 🤔

Yeah 🤔 🤔 they did. I'll give your suggested fix a go on Monday!