Hi StaleMole4 ,
Can you share the segment of your task where you used ClearML? What code gets ignored?
Also, can you try using the latest ClearML Agent version (0.17.x)?

Hi SuccessfulKoala55 , thanks for the quick reply!

This is a stand-alone code, that creates a task, clones it, and enqueues the cloned task:
` def enqueue_task(trains_prms, task_name, task_type):

# Connect to trains
task = Task.init("OMD", task_name='Temp controller for ' + task_name, task_type=task_type) # , task_type=Task.TaskTypes.controller

# Select queue
trains_queue = "cpu_queue"

# Connect parameters
assert "is_controller_run" not in trains_prms
trains_prms["is_controller_run"] = True
trains_prms = task.connect(trains_prms)
print("Connected to trains")

if trains_prms["is_controller_run"]:

    # Controller work
    task.close()

    # Get a reference to the task to pipe to.
    next_task = Task.get_task(task_id=task.id)

    # Clone the task to pipe to. This creates a task with status Draft whose parameters can be modified.
    cloned_task = Task.clone(source_task=next_task, name=task_name)

    # Get the original parameters of the Task, modify the value of one parameter,
    #   and set the parameters in the next Task
    cloned_task_parameters = cloned_task.get_parameters()
    cloned_task_parameters["is_controller_run"] = False
    cloned_task.set_parameters(cloned_task_parameters)

    # Enqueue the Task for execution. The enqueued Task must already exist in the clearml platform
    print('Enqueue task: ' + task_name)
    Task.enqueue(cloned_task.id, queue_name=trains_queue)
    print('Enqueued task: ' + task_name)
    is_controller_run = True

    # Controller is finished
    return is_controller_run, trains_prms, -1

# Not the controller
return trains_prms["is_controller_run"], trains_prms, task `

OK, and where do you call this function?

For debug purposes, in main:
if __name__ == "__main__": prms = enqueue_task({}, "Test task", Task.TaskTypes.custom) print(prms) print("Finished main")And the "print" gets ignored in the enqueued task

It appears to be working now (though I'm still debugging).
Setting the conda environment directly appeared to have solved it:
task.set_base_docker(conda_env)

Thanks for the help!