AgitatedDove14 - thanks for the quick reply. automation.Monitor is the abstraction i could use?
Thanks let me try playing with these!
Not able to understand what’s really happening in the links
Also the pipeline ran as per this example - https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_controller.py
The agent ip? Generally what’s the expected pattern to deploy and scale this for multiple models?
AgitatedDove14 - added it in bucket_config.py and sdk.conf but somehow value is not being picked up
I am essentially creating a EphemeralDataset abstraction and creating controlled lifecycle for it such that the data is removed after a day in experiments. Additionally and optionally, data created during a step in a pipeline can be cleared once the pipeline completes
I guess this is a advantage with docker mode. Will try that out as well sometime.
I can contribute as well as needed
Latest version was released 11 hours ago - https://github.com/jpadilla/pyjwt/releases/tag/2.2.0
Ok, got it thanks. Would be cool to let it get untracked as well, especially if we want to as an option
Sorry if it was confusing. Was asking if people have setup pipelines automatically triggered on update to datasets
AgitatedDove14 - where does automation.controller.PipelineController fit in?
now if dataset1 is updated, i want process to update dataset2
Trying to understand these, maybe playing around will help
I just want to change git remote like https://gitserver.com/path/to.git -> mailto:git@gitserver.com :path/to.git
I was thinking such limitations will exist only for published
When did this PipelineDecorator come. Looks interesting 🙂
You mean the job with the exact same arguments ?
Yes
From the code - it’s supposed to not cache if task override is different? I also have task_override that adds a version which changes each run
Or WARNING should be param not found, but using General/param etc
AgitatedDove14 - mean this - says name=None but text says default is General.
AgitatedDove14 - is my understanding right that we have to call pipe.wait() ?
Exact reproduction: