SuccessfulKoala55
I can see the issue your are referring to regarding the execution of the triton docker image, however as far as I am aware, this was not something I explicitly specified. The ServingService.launch_service() method from the ServingService Class from the clearml-serving package would appear to have both specified:

` def launch_engine(self, queue_name, queue_id=None, verbose=True):
# type: (Optional[str], Optional[str], bool) -> None
"""
Launch serving engine on a specific queue
:param queue_name: Queue name to launch the engine service running the inference on.
:param queue_id: specify queue id (unique stand stable) instead of queue_name
:param verbose: If True print progress to console
"""

    # todo: add more engines
    if self._engine_type == 'triton':
        # create the serving engine Task
        engine_task = Task.create(
            project_name=self._task.get_project_name(),
            task_name="triton serving engine",
            task_type=Task.TaskTypes.inference,
            repo=" ` ` ",
            branch="main",
            commit="ad049c51c146e9b7852f87e2f040e97d88848a1f",
            script="clearml_serving/triton_helper.py",
            working_directory=".",
            docker="nvcr.io/nvidia/tritonserver:21.03-py3 --ipc=host -p 8000:8000 -p 8001:8001 -p 8002:8002",
            argparse_args=[('serving_id', self._task.id), ],
            add_task_init_call=False,
        )
        if verbose:
            print('Launching engine {} on queue {}'.format(self._engine_type, queue_id or queue_name))
        engine_task.enqueue(task=engine_task, queue_name=queue_name, queue_id=queue_id) `