So you can get the experiment details using:
from clearml.backend_api.session.client import APIClient client = APIClient() task_data = client.tasks.get_by_id(<task-id>)

yes we saved those in the hyper parameters

Hi CrookedMonkey33 , which configurations are you talking about, exactly? Are there the hyper-parameters? or the configuration objects?

I would like to recreate an experiment after saving its configurations, to do that that I need to load those configurations in another notebook, right now the only way I managed to do that is by saving those configurations as an artifact and load that artifact, but it is less convenient than loading a configuration.

Hi CrookedMonkey33 , can you elaborate a bit more on what you want done?