the 2nd option looks good. would everyone's credentials be displayed on the server though?
UpsetTurkey67 The single set of online documentation ( https://clear.ml/docs/latest/docs ), denotes OSS/Free-SaaS/Paid features as such. For example: https://clear.ml/docs/latest/docs/configs/clearml_conf#configuration-vault
CloudySwallow27 okay essentially this defs file is kind of a user "secret vault" for access credentials, is that correct?
gotit. any solution where the credentials wouldnt be visible to everyone?
Right, so this "vault" design is built into the paid tiers of ClearML to achieve exactly that. Long story short, users can put their credentials/configs on the clearml-server and the agent (or the clients) will pull and merge them into the execution.
It's very cool and works really nice, but not part of the open source (or the SaaS tier).
What you could do is store these configurations on the Task itself (one way o r another). Maybe for example have an empty definitions.py file part of the git repository, and then the local changes (i.e. the users credentials) are stored as part pf the uncommitted changes section. This would mean that the agent will replicate the same user's definitions/py into the remote machine, and pass the credentials.
wdyt?
UpsetTurkey67 my apologies I just notices the message
AgitatedDove14 do you have two sets of documentations: for the free tier and paid? (our team is on the pro plan, but I might have been using the documentation of the open sourced version, so want to double check)
ok, i suppose that will have to do for now. thank you!
you could also use:
https://github.com/allegroai/clearml/blob/ce7e77a00e869a2690f31cbc578636ce88bc4613/docs/clearml.conf#L188
and setup the clearml.conf on the users machine to automatically log the environment variables at run time (stored under the Configuration tab).
Then the agent will pull these same variables at execution time and set them
Hmm I cannot think of something that will provide something a per user basis.
Wouldn't a global set of credentials that the agent is using be enough ?
(on the local machine, user can keep using the "definitions.py")