Thanks a lot! I do not have problem executing the pipeline remotely, I have problam executing it locally.

Is there a possibility that it was using Elastic before (through some logging driver) but that it defaulted to using json.log (default) logger driver now?

Thanks, I will check!

I have GCP instance with official clearml image.

from clearml import StorageManager, Dataset

dataset = Dataset.create(
    dataset_project="Project", dataset_name="Dataset_name"
)

files = [
    'file.csv',
    'file1.csv',
]

for file in files:
    csv_file = StorageManager.get_local_copy(remote_url=file)
    dataset.add_files(path=csv_file)


# Upload dataset to ClearML server (customizable)
dataset.upload()
# commit dataset changes
dataset.finalize()

I solve problem by adding container argument

--network host 

I am running clearml server on gcp, but I didn't exposed ports instead I ssh to machine and do port forwarding to localhost. The problem is localhost on my machine is not same as localhost inside docker on worker. If I check dataset, files are stored in localhost, but actually it is not localhost. Didn't fond the solution yet how to properly setup hostname for dataserver. Any ideas?

Found solution! None

One more question 🙂
How can I force clearML not to install requirements before running task? (already have everything installed on docker machine)

Looks like that docker-compose down && docker-compose up flush console output. I upgraded server to 1.16.2-502, didn't had that problem before. Any idea?

Hey, I had similar problem. Take a look here: None

As I understood, pipeline controller is a task, and it blocks queue. I solved problem by adding one more agent.

I was digging around a bit, it seems that for the worker containers use default logging, that is: they use json.log files stored in /var/lib/docker/container/<hash> folders. When I do up/down of docker compose, these container folders are purged and with them my console log is gone.

Here is basic example:

from clearml import PipelineController

def step_function(param):
    print("Hello from function!")
    print("Param:", param)

if __name__ == '__main__':
    repo = '

'
    repo_branch = 'main'
    working_dir = 'pipelines'

    pipe = PipelineController(
        name='Test',
        project='Test',
        version='0.0.1',
        add_pipeline_tags=False,
        repo=repo,
        repo_branch=repo_branch,
        working_dir=working_dir
    )
    p...

I checked web ui, in execution section for pipeline there is repo_url, commit id... but in tasks's execution section repo_url filed is blank.

@<1523701070390366208:profile|CostlyOstrich36> any idea? 🙂

Yes, there is one agent. As I said, I am able to execute task, but have problem with pipeline

Tnx!

Didn't had that problem, sorry I can not help you. 😢

Ok, thanks for explanation. So pipeline controller is in Running state, while task 1 is in pending state. The solution will be to add one more agent?

It works! Thanks man, you save my day!!

Thanks for very fast replay!

Looks like that there was problem with elastic search docker container. Everything was good after restarting machine.