The custom callback I have used is:
` class MyClearMLCallback(ClearMLCallback):
def init(self, *args, **kwargs):
self._task_name = kwargs.pop("task_name", None)
self._project_name = kwargs.pop("project_name", None)
super().init(*args, **kwargs)

def setup(self, args, state, model, tokenizer, **kwargs):
    if self._clearml is None:
        return
    if state.is_world_process_zero:
        logger.info("Automatic ClearML logging enabled.")
        if self._clearml_task is None:
            self._clearml_task = self._clearml.Task.init(
                project_name=self._project_name,
                task_name=self._task_name,
                auto_connect_frameworks={"tensorboard": False, "pytorch": False},
                output_uri=True,
            )
            self._initialized = True
            logger.info("ClearML Task has been initialized.")

        self._clearml_task.connect(args, "Args")
        if hasattr(model, "config") and model.config is not None:
            self._clearml_task.connect(model.config, "Model Configuration") `