We do not CostlyFox64 , but this is useful for the future 🙂 Thanks!
TimelyPenguin76 I'll have a look, one moment.

AgitatedDove14 The keys are there, and there is no specifically defined user in .gitmodules :
[submodule "xxx"] path = xxx url =I believe this has to do with how ClearML sets up the git credentials perhaps?

It is. Let me see what else I have set up for MinIO in configs, one moment

Am I making sense ?

No, not really. I don't see how task.connect_configuration interacts with our existing CLI? Additionally, the documentation for task.connect_configuration say the second argument is the name of a file, not the path to it? So something is off

We’re using karpenter (more magic keywords for me), so my understanding is that that will manage the scaling part.

Generally the StorageManager seems a bit slow, even a simple StorageManager.list(...) on a local path seems to take a long time

I guess it's mixed. If #340 is resolved, then this initializer task will be a no-op: detach, and init-close new tasks as needed.

It's a bit hard to read when they'll all clustered together:

Let me verify a hypothesis...

So maybe summarizing (sorry for the spam):

• Pipelines:- Pros: Automatic upload and serialization of input arguments
• Cons: Clutter, does not support classes, cannot inject code, does not recognize environment when run from e.g. IPython- Tasks:- Pros: Tidier and matches original idea, recognizes environment even when run from IPython
• Cons: Does not support classes, cannot inject code, does not automatically upload input arguments

Either? 🙂

Unfortunately I can't take a photo of not being able to compare tasks by navigating around the WebUI...

Does that clarify the issue CostlyOstrich36 ?

~ is a bit weird since it's not part of the package (might as well let the user go through clearml-init ), but using ${PWD} works! 👍 👍

(Though I still had to add the CLEARML_API_HOST and CLEARML_WEB_HOST ofc, or define them in the clearml.conf)

Yes 😅 I want ClearML to load and parse the config before that. But now I'm not even sure those settings in the config are even exposed as environment variables?

Exactly, it should have auto-detected the package.

Or well, because it's not geared for tests, I'm just encountering weird shit. Just calling task.close() takes a long time

SmugDolphin23 we've been working with this for 2 weeks now, and it creates a lot of junk in our UI. Is there anyway to have better control over this?

Thanks SuccessfulKoala55 ! Is this listed anywhere in the documentation?
Could I set an environment variable there and then refer to it internally in the config with the ${...} notation?

I see https://github.com/allegroai/clearml-agent/blob/d2f3614ab06be763ca145bd6e4ba50d4799a1bb2/clearml_agent/backend_config/utils.py#L23 but not where it's called 🤔

I think now there's the following:
Resource type Queue (name) defines resource + max instancesAnd I'm looking for:
Resource type "pool" of resources (type + max instances) A pool can be shared among queues

Ah it is.

Maybe @<1523701827080556544:profile|JuicyFox94> can answer some questions then…

For example, what’s the difference between agentk8sglue.nodeSelector and agentk8sglue.basePodTemplate.nodeSelector ?
Am I correct in understanding that the former decides the node type that runs the “scaler” (listening to the given agentk8sglue.queue ), and the latter for any new booted instance/pod, that will actually run the agent and the task?
Read: The former can be kept lightweight, as it does no...

There's a specific fig[1].set_title(title) call.

Hurray! Some success is good 😄

This was changed ~14 months ago, I guess not many people use the local file storage?

https://github.com/allegroai/clearml/commit/95105cbe6a0910ee0c37db707e32549ceaf266eb#diff-9b02810ceabbfd705225d0a7181a53b956b58479e125dd64c5f6c5c8c999e142R373

Eek. Is there a way to merge a backup from elastic to current running server?

The screenshot is small since the data is private anyway, but it's enough to see:
"Metric: untitled 00" "plot image" as the image title The attached histogram has a title ("histogram of ...")

That gives us the benefit of creating "local datasets" (confined to the scope of the project, do not appear in Datasets tabs, but appear as normal tasks within the project)

That's probably in the newer ClearML server pages then, I'll have to wait still 😅

I just ran into this too recently. Are you passing these also in the extra_clearml_conf for the autoscaler?

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…