oh wow, I didn't see delete_artifacts_and_models option
I guess we'll have to manually find old artifacts that are related to already deleted tasks
what if cleanup service is launched using ClearML-Agent Services container (part of the ClearML server)? adding clearml.conf to the home directory doesn't help
What is the recommended way of providing S3 credentials to cleanup task?
cleaml.conf or OS environment (AWS_ACCESS_KEY_ID ...)
we're using the latest version of clearml, clearml agent and clearml server, but we've been using trains/clearml for 2.5 years, so there are some old tasks left, I guess 😃
what if cleanup service is launched using ClearML-Agent Services container
The easiest is to use the container args and pass the AWS credentials as env variables:-e AWS_ACCESS_KEY_ID=abcd -e ....Make sense ?
what if for some old tasks I get WARNING:root:Could not delete Task ID=a0908784a2a942c3812f947ec1f32c9f, 'Task' object has no attribute 'delete'? What's the best way of cleaning them?
This seems like an old SDK no?
two more questions about cleanup if you don't mind:
what if for some old tasks I get WARNING:root:Could not delete Task ID=a0908784a2a942c3812f947ec1f32c9f, 'Task' object has no attribute 'delete'? What's the best way of cleaning them? What is the recommended way of providing S3 credentials to cleanup task?
DilapidatedDucks58
did you check:
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py
we already have cleanup service set up and running, so we should be good from now on