I would like to confirm just in case.
In the desired behavior, reuse_last_task_id=True forces it for any intervals?

I don't mean continuous training but I want to know about your plans for it 😋

oh I got it. my codes output models and the task catch it automatically.

In my case, I write codes and run single batch train-val, which contains model saving, in developing phase. I want TRAINS to overwrite the dev runs for keeping dashboard clean.

YummyWhale40 no idea what the pytorch-lighting guys did there. let me check a the actual code.

YummyWhale40 from the code snippet, it seems like the argument is passed.
"reuse_last_task_id=True" is the default, and it means that if the previous run of the task did not create any artifacts/models and was executed 72 hours ago (configurable), The Task will be reset (i.e. all logs cleared) and will be reused in the current run.

YummyWhale40 you mean like continue training?
https://github.com/allegroai/trains/issues/160

maybe the arguments is simply passed to Task.init()
self._trains = Task.init( project_name=project_name, task_name=task_name, task_type=task_type, reuse_last_task_id=reuse_last_task_id, output_uri=output_uri, auto_connect_arg_parser=auto_connect_arg_parser, auto_connect_frameworks=auto_connect_frameworks, auto_resource_monitoring=auto_resource_monitoring )

if you have any idea to reuse id even if models are outputted, please tell me thx