Would it also be loaded on e.g. Task.running_locally() ?

https://github.com/allegroai/clearml-server/issues/134

Now, the original pyhocon does support include statements as you mentioned - https://github.com/chimpler/pyhocon

Thanks! I'll wait for the release note/docs update 😁

It misses the repository information of course, but the 'configuration/Args' were logged. So something weird in identifying the repository

Because setting env vars and ensuring they exist on the remote machine during execution etc is more complicated 😁

There are always ways around, I was just wondering what is the expected flow 🙂

Setting the endpoint will not be the only thing missing though, so unfortunately that's insufficient 😞

No need for S3 or other resources?

Done https://github.com/allegroai/clearml-docs/issues/294

` # test_clearml.py
import pytest
import shutil
import clearml

@pytest.fixture
def clearml_task():
clearml.Task.set_offline_mode(True)
task = clearml.Task.init(project_name="test", task_name="test")
yield task
shutil.rmtree(task.get_offline_mode_folder())
clearml.Task.set_offline_mode(False)

class ClearMLTests:
def test_something(self, clearml_task):
assert True run with pytest test_clearml.py `

UPDATE: Apparently the quotation type matters for furl ? I switched the ' to \" and it seems to work now

Since the additional credentials are available to the autoscaler when it boots up (via the config file), I thought it could use those natively?

Does that make sense CostlyOstrich36 ? Any thoughts on how to treat this? For the time being I'm also perfectly happy to include something specific to extra_clearml_conf , but I'm not sure how to set the sdk.aws.s3.credentials to be a list of dictionaries as needed

Would be great if it is 😍 We have few files that change frequently and are quite large in size, and it would be quite a storage hit to save all of them

I also tried setting agent.python_binary: "/usr/bin/python3.8" but it still uses Python 2.7?

I believe it is maybe a race condition that's tangent to clearml now...

Aw you deleted your response fast CostlyOstrich36 xD

Indeed it does not appear in ps aux so I cannot simply kill it (or at least, find it).
I was wondering if it's maybe just a zombie in the server API or similar

1.1.4

Yes, exactly. I have not yet had a chance to try this out -- should it work?

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?

I'm not sure I follow, how would that solution look like?

SuccessfulKoala55 could this be related to the monkey patching for logging platform? We have our own logging handlers that we use in this case

Oh and clearml-agent==1.1.2

That's what I thought too, it should only look for the CLEARML_TASK_ID environment variable?

Samoja ongelmia täälläkin 😅

I'm running tests with pytest , it consumes/owns the stream

CostlyOstrich36 I'm not sure what is holding it from spinning down. Unfortunately I was not around when this happened. Maybe it was AWS taking a while to terminate, or maybe it was just taking a while to register in the autoscaler.

The logs looked like this:

1. Recognizing an idle worker and spinning down.
   2022-09-19 12:27:33,197 - clearml.auto_scaler - INFO - Spin down instance cloud id 'i-058730639c72f91e1'2. Recognizing a new task is available, but the worker is still idle.
   ` 2022-09...

Is Task.create the way to go here? 🤔

It's removed now, magic of asking for help and doing nothing 😄

Could also be related to K8, so pinging JuicyFox94 just in case 😉