this is how task gets created:

def create_clearml_task(
    project_name,
    task_name,
    script,
    args,
    docker_args="",
    docker_image_name="<docker image name>",
    add_task_init_call=True,
    requirements_file=None,
    **kwargs):
    print(
        "Creating task: project_name: {project_name}, task_name: {task_name}, script:{script} and args: \n {args}"
        .format(
            project_name=project_name,
            task_name=task_name,
            script=script,
            args=args,
        ))
    arg_tuples = args_to_tuples(args)
    # Remove the argument to execute on clearML before queueing up otherwise we will just keep calling
    # remote execution recursively without ever doing the work.
    unset_clearml_execute(arg_tuples)
    return Task.create(
        argparse_args=arg_tuples,
        project_name=project_name,
        task_name=task_name,
        script=script,
        add_task_init_call=add_task_init_call,
        repo='git@<repo>.git',
        packages=find_current_packages() if requirements_file is None else None,
        requirements_file=requirements_file,
        docker=docker_image_name,
        commit=get_current_commit(),
        docker_bash_setup_script=bash_setup_string,
        docker_args="-v /home:/home -v /data:/data -v /mnt:/mnt -v /etc/aws:/etc/aws --shm-size 50G"
        + docker_args,
        **kwargs)

===============================================

if args.clearml_taskname is not None and args.clearml_execute is not None:
        args_except_execute = {k: v for k, v in vars(args).items() if k != "clearml_execute"}
        task = create_clearml_task(project_name=project_name,
                                   task_name=args.clearml_taskname,
                                   script="train.py",
                                   args=args_except_execute,
                                   docker_image_name=docker_img,
                                   requirements_file=requirements_file,
                                   add_task_init_call=False)
        task.connect(config_dict)
        Task.enqueue(task, queue_name=args.clearml_execute)
        sys.exit(0)

# inside main:
task = Task.init(project_name, clearml_taskname)
task.connect(config_dict)

i import Task from clearml and I also use PyTorch lightning's TensorboardLogger