I am doing Task.init but it’s not adding expected libraries imported in the script or from requirements.txt

CynicalBee90 - on the platform agnostic aspect - dvc does it with the CLI right? It that what made you give a green checkmark for it?

Do we support GPUs in a) docker mode b) k8s glue?

Is it not possible to say just look at my requirements.txt file and the imports in the script?

Got it, thanks

AgitatedDove14 is it possible to get the pipeline task running a step in a step? Is task.parent something that could help?

Yeah, when doing:

task.set_base_docker( "nvidia/cuda:11.4.1-cudnn8-runtime-ubuntu20.04", docker_setup_bash_script=script, )

AgitatedDove14 - mean this - says name=None but text says default is General.

The helm chart installs a agentservice, how is that related if at all?

I have a wrapper SDK over clearml that includes default conf and others are loaded from secret manager / env vars as needed

Gitlab has support for S3 based cache btw.

Yeah please if you can share some general active ones to discuss both algos and engineering side

With the human activity being a step where some manual validations, annotations, feedback might be required

Ok the doc needs fix

Is Task.current_task() creating a task?

As in run a training experiment, then a test/validation experiment to choose best model etc etc and also have a human validate sample results via annotations all as part of a pipeline

I would prefer controlled behavior than some available version being used. Here triggered a bunch of jobs that all went fine and even evaluations were fine and then when we triggered a inference deploy it failed

How is clearml-session intended to be used?

Thanks

Looks like Task.current_task() is indeed None in this case. Bit of log below where I print(Task.current_task()) as first step in the script

Environment setup completed successfully Starting Task Execution: None

AgitatedDove14 either based on scenario

AgitatedDove14 - it does have boto but the clearml-serving installation and code refers to older commit hash and hence the task was not using them - https://github.com/allegroai/clearml-serving/blob/main/clearml_serving/serving_service.py#L217

Any idea why the Pipeline Controller is Running despite the task passing?

It completed after the max_job limit (10)

Awesome

irrespective of what I actually have installed when running the script?

Ok i did a pip install -r requirements.txt and NOW it picks them up correctly

Thoughts AgitatedDove14 SuccessfulKoala55 ? Some help would be appreciated.

Can you point me at relevant code in ClearML for the autoconnect so that I can understand exactly what's happening

Would this be a good use case to have?