This might work (I have to admit I haven't had the time to test, please let me know if it works, so we could push it as a cool new feature 🙂 )
` class LocalClearmlJob(ClearmlJob):
def init(self, *args, **kwargs):
super(LocalClearmlJob, self).init(*args, **kwargs)

def launch(self, queue_name=None):
    # type: (str) -> bool
    if self._is_cached_task:
        return False

    # create the subprocess
    cmd = self.task.data.execution.script.entrypoint
    python = sys.executable
    env = dict(**os.environ)
    env['CLEARML_TASK_ID'] = env['TRAINS_TASK_ID'] = self.task.id
    env['CLEARML_LOG_TASK_TO_BACKEND'] = 1
    env['CLEARML_SIMULATE_REMOTE_TASK'] = 1
    p = subprocess.Popen(args=[python, cmd], cwd=os.getcwd(), env=env)
    return True

an_optimizer = HyperParameterOptimizer(max_number_of_concurrent_tasks=1, ...)
an_optimizer.set_default_job_class(LocalClearmlJob)
an_optimizer.start()
an_optimizer.wait()
an_optimizer.stop() `
This will code will spin a subprocess running the original Task as if it is running by the agent, only locally.
This means the optimizer can control the parameters, and you are running all jobs locally.
wdyt?