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_...

We're wondering how many on-premise machines we'd like to deprecate. For that, we want to see how often our "on premise" queue is used (how often a task is submitted and run), for how long, how many resources it consumes (on average), etc.

Consider e.g:

# steps.py

class DataFetchingStep:
    def __init__(self, source, query, locations, timestamps):
        # ...
    def run(self, queue=None, **kwargs):
        # ...

class DataTransformationStep:
    def __init__(self, inputs, transformations):
        # inputs can include instances of DataFetchingStep, or local files, for example
        # ...
    def run(self, queue=None, **kwargs):
        # ...

And then the following SDK usage in a notebook:

from steps imp...

Ah, the API server /users.get_all , I see!

An internal project I've accidentally made with a hidden tag while playing around with the ClearML internal code.

There used to be a good example but it's now missing. I'm not sure what does Use only for automation (externally), otherwise use Task.connect_configuration mean when e.g. looking at Task.set_configuration_object , etc.

Could you clarify a bit, CostlyOstrich36 or AgitatedDove14 ?

I think ClearML boots up only afterwards, so those environment variables may not be available yet.
You should set them manually in the bootstrap code unfortuantely.

Managed now 🙂 Thank you for your patience!
I edited the previous post with some suggestions/thoughts

Hm, just a small update - I just verified and it does indeed work on linux:

` import clearml
import dotenv

if name == "main":
dotenv.load_dotenv()
config = clearml.backend_api.Config.load() # Success, parsed with environment variables `

For example:

my-repo @ git+

The network is configured correctly 🙂 But the newly spun up instances need to be set to the same VPC/Subnet somehow

There's not much (or anything) in the log to provide...

` (.venv) 15:42 [0:user@server$~] CLEARML_CONFIG_FILE=~/agent_clearml.conf clearml-agent daemon --queue default on_prem --detached --order-fairness
Environment variables set from configuration: ['AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY', 'AWS_DEFAULT_REGION']
...

Illustrating the first bullet point

🤔

Then that did not work, but I'll look into it again soon!

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

Anything else you’d recommend paying attention to when setting the clearml-agent helm chart?

I'm working on the config object references 😉

Hi AgitatedDove14 !

Ah, thanks! I'll use the artifacts for linking.

We've forgone the "use current task" already because it indeed made things even more difficult (the task that was used is then automatically hidden by this automatic renaming of dataset tasks).
The current implementation (since 1.6.3 I think) creates the issues in the linked comment (with images to visualize).

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

Without knowing anything, I'm assuming maybe ClearML patches plt.title and not Axes.set_title ?