Oh, well, no, but for us that would be one way solution (we didn't need to close the task before that update)

The overall flow I currently have is e.g.
Start an internal task (not ClearML Task; MLOps not initialized yet) Call some pre_init function with args so I can upload the environment file via StorageManager to S3 Call some start_run function with the configuration dictionary loaded, so I can upload the relevant CSV files and configuration file Finally initialize the MLOps (ClearML), start a task, execute remotely
I can play around with 3/4 (so e.g. upload CSVs and configuratio...

Yeah that works too. So one can override the queue ID but not the worker 🤔

Added the following line under volumes for apiserver , fileserver , agent-services :
- /data/clearml:/data/clearml

TimelyPenguin76 CostlyOstrich36 It seems a lot of manual configurations is required to get the EC2 instances up and running.
Would it not make sense to update the autoscaler (and example script) so that the config.yaml that's used for the autoscaler service is implicitly copied to the EC2 services, and then any extra_clearml_conf are used/overwritten?

No need for S3 or other resources?

Doh, thanks!

Right and then for text (file path) use some regex or similar for extraction, and for dictionary simply parse the values?

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

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

But it does work on linux 🤔 I'm using it right now and the environment variables are not defined in the terminal, only in the .env 🤔

Okay so the only missing thing of the puzzle I think is that it would be nice if this propagates to the autoscaler as well; that then also allows hiding some of the credentials etc 😮

I opened a GH issue shortly after posting here. @<1523701312477204480:profile|FrothyDog40> replied (hoping I tagged the right person).

We need to close the task. This is part of our unittests for a framework built on top of ClearML, so every test creates and closes a task.

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?

Yes exactly, but I guess I could've googled for that 😅

Copy the uncommitted changes captured by ClearML using the UI, write to changes.patch , run git apply changes.patch 👍

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

Nice, that works 🙂 Thanks!

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

1.8.3; what about when calling task.close() ? We suddenly have a need to setup our logging after every task.close() call

Uhhh, but pyproject.toml does not necessarily entail poetry... It's a new Python standard

So the ..data referenced in the example above are part of the git repository?
What about setting the working_directory to the user working directory using Task.init or Task.create ?

That's up and running and is perfectly fine.

AgitatedDove14

hmmm... they are important, but only when starting the process. any specific suggestion ?
(and they are deleted after the Task is done, so they are temp)

Ah, then no, sounds temporary. If they're only relevant when starting the process though, I would suggest deleting them immediately when they're no longer needed, and not wait for the end of the task (if possible, of course)

Thanks for the reply CostlyOstrich36 !
Does the task read/use the cache_dir directly? It's fine for it to be a cache and then removed from the fileserver; if users want the data to stay they will use the ClearML Dataset 🙂

The S3 solution is bad for us since we have to create a folder for each task (before the task is created), and hope it doesn't get overwritten by the time it executes.

Argument augmentation - say I run my code with python train.py my_config.yaml -e admin.env...

Maybe create_function_task ? 🤔

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

Sure! It looks like this

Any updates @<1523701087100473344:profile|SuccessfulKoala55> ? 🙂

Yes

So basically I'm wondering if it's possible to add some kind of small hierarchy in the artifacts, be it sections, groupings, tabs, folders, whatever.