result: None

    common_module = task.connect_configuration("../common.py", "common.py")
    if not task.running_locally():
        import shutil
        shutil.copy(common_module, "common.py")

    from common import test_common
    test_common()

it seems that connecting it as config is more convenient than uploading an artifact, because artifacts are deleted when cloning a task. Code is very simple:

That sounds like an interesting hack 😃 I'll try it out, thanks!

I also cannot create a package out of common code, because the package registry is inside the internal network as well

clearml==1.12.2

@<1744891825086271488:profile|RoundElephant20> , any updates?

Is there any way to make sure that task would fail?

but what's the best way to catch the exception? All high-level clearml function calls return normally

Task completes normally. I'm using clearml's aws autoscaler.
Task is started the following way: airflow job run finds an older task, clones it, changes some params and enqueues it.

Tried it on 1.13.1. Same problem. @<1523701087100473344:profile|SuccessfulKoala55> any advice?

my code:

    dataset = Dataset.create(
        dataset_project=PROJECT_NAME,
        dataset_name=f"processed_{mode}",
        dataset_tags=task.get_tags(),
        parent_datasets=None,
        use_current_task=False,
        output_uri=BUCKET,
    )

dataset.add_files(path, verbose=True)
dataset.upload(verbose=True)    dataset.finalize(verbose=True)

from clearml import Task, Dataset

task = Task.init(
    project_name="MyProject",
    task_name="MyTask",
    task_type=Task.TaskTypes.data_processing,
    reuse_last_task_id=False,
    output_uri="

"
)

with open("new_file.txt", "w") as file:
    file.write("Hello, world!")

dataset = Dataset.create(parent_datasets=None, use_current_task=True)
dataset.add_files(".", wildcard="new_file.txt", verbose=True)
dataset.upload(verbose=True)
dataset.finalize(verbose=True)

it was a year ago, so yeah.

Any updates? Should I provide any extra context?

I did similarly at my previous work (we had open source clearml deployed). The problem I described here was not present there. I liked this approach. It was convenient that dataset_id and task_id are the same.

So you'd recommend setting use_current_task=False when creating the dataset in this task or should this be done somehow differently?