Yes, as I wrote above 😄

I think you're interested in the Monitor class:)

Perfect now 👌 (also nice cleanup of default_new_data_root duplicate code :D)

Nothing I can spot --

ClearML results page: 

ClearML pipeline page: 

Launching the next 2 steps
Launching step [...]
Launching step [...]
Launching step: ...
Parameters:
{...}
Configurations:
{}
Overrides:
{}
Launching step: ...
Parameters:
{...}
Configurations:
{}
Overrides:
{}
ClearML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring
2023-02-21 13:53:48
ClearML Monitor: Could not detect iteration reporting, falling back to itera...

I tried that, unfortunately it does not help 😞

One way to circumvent this btw would be to also add/use the --python flag for virtualenv

CostlyOstrich36 so internal references are not resolved somehow? Or, how should one achieve:

def my_step(): from ..utils import foo foo("bar")

I'm not sure about the intended use of connect_configuration now.
I was under the assumption that in connect_configuration(configuration, name=None, description=None) , the configuration is only used in local execution.
But when I run config = task.connect_configuration({}, name='General') (in remote execution), the configuration is set to the empty dictionary

I'm not sure, I'm not getting anything (this is the only thing I could fin that's weird about this project).
It has a space in the name, has no subprojects, and it just doesn't show up anywhere 🤔

Hmmm, what 😄

` # 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 `

Yes

Let me try that real quick

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

Sure! It looks like this

Does that make sense SmugDolphin23 ?

Dynamic pipelines in a notebook, so I don’t have to recreate a pipeline every time a step is changed 🤔

After the task was initialized? 🤔

Nope, no other config files

Can I query where the worker is running (IP)?

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?

Thanks! That's what I thought, but then I get
2021-12-21 22:08:35,376 - clearml.storage - ERROR - Failed uploading: Parameter validation failed: Invalid bucket name "": Bucket name must match the regex "^[a-zA-Z0-9.\-_]{1,255}$" or be an ARN matching the regex "^arn:(aws).*:(s3|s3-object-lambda):[a-z\-0-9]*:[0-9]{12}:accesspoint[/:][a-zA-Z0-9\-.]{1,63}$|^arn:(aws).*:s3-outposts:[a-z\-0-9]+:[0-9]{12}:outpost[/:][a-zA-Z0-9\-]{1,63}[/:]accesspoint[/:][a-zA-Z0-9\-]{1,63}$"

So some UI that shows the contents of users.get_all ?

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)

Yeah I was basically trying to avoid clutter in the Pipelines page. But see my other thread for the background, maybe you have some good input there? 🙏

Would be nice if the second one was a toggle-able feature (either per use or in the server settings) maybe?

Most of these are configurations (specific for an execution, but one such configuration defines multiple tasks). Some models might be uploaded if the user does not use our built-in link to ClearML model fetching 😄

As the meme goes, well yes but actually no, since the input path is provided via argparse? I'm also not sure how this would help debug from the WebUI - you can't really see the contents of a zipped file/the configuration tab is too messy for such a nested configuration as the one we have. It's best suited as an artifact.

EDIT: Or am I missing something? Point being, when the remote execution begins, the entry point tries to run e.g. python train.py --config_file path/to/local/file.yaml ...

Hm. Is there a simple way to test tasks, one at a time?

@<1523701827080556544:profile|JuicyFox94> we have it up and running, hurray 🙂
One thing I noticed in the k8s logs is frequent warnings about Python 3.6..? Is the helm chart built with that Python version?

/usr/lib/python3/dist-packages/secretstorage/dhcrypto.py:15: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.
from cryptography.utils import int_...