Task has to be in draft mode, hover over the section and you will see an edit button or just double click the field you want to edit

After you store the model in ClearML server accessing it later becomes almost trivial 🙂

Hi GrittyCormorant73 , can you please add the error you're getting? What version of ClearML are you using?

Hi @<1529995795791613952:profile|NervousRabbit2> , if you're running in docker mode you can easily pass it in the docker_args parameter for example so you can set env variables with -e docker arg

Can you try running it via agent without the docker?

You're getting
Syncing scheduler Failed deserializing configuration: the JSON object must be str, bytes or bytearray, not NoneTypeLike before? Are all the symptoms the same as above?

Hi @<1523701062857396224:profile|AttractiveShrimp45> , I think this is currently by design. How would you suggest doing multiple metric optimization - priority between metrics after certain threshold is met?

UpsetTurkey67 Hi,
You can get the user id that created the task through this way:
from clearml import Task task=Task.init() #Fetching the Task object would also work user_id = task.data.user

but without -d

Hi @<1790190274475986944:profile|UpsetPanda50> , none AWS s3 solutions are also supported. Please see docs - None

Hi @<1715900788393381888:profile|BitingSpider17> , I think this is what you're looking for - None

Hi @<1593051292383580160:profile|SoreSparrow36> , can I assume you're running a self hosted server? Is there any chance you were either using a very old SDK or old backend?

The default behavior now is to create pipeline tasks as hidden and only show them as part of the pipelines UI section.

Does it go back to working if you revert the changes?

The assumption is that the server and serving don't run on the same machine. The ClearML server is just a control plane whereas the serving solution actually does computation.

Hi @<1523701295830011904:profile|CluelessFlamingo93> , are you self hosting or using the community server?

Hi @<1577106236631945216:profile|SucculentCat11> , I think the file must be a .py file

My guess other agents are sitting on different machines, did you verify that the credentials are the same between the different clearml.conf files? Maybe @<1523701087100473344:profile|SuccessfulKoala55> might have an idea

Hi @<1523717803952050176:profile|SmoothArcticwolf58> , can you describe a bit about the network between the agent and the new server?

Hi @<1585078752969232384:profile|FantasticDuck7> , I think you can pass this in bash setup script when the docker spins up

Hi @<1523701260895653888:profile|QuaintJellyfish58> , yes it is. You can simply specify a branch 🙂

Hi @<1574931891478335488:profile|DizzyButterfly4> , can you provide a stand alone code snippet that reproduces this behaviour?

Oh you're right. My bad

Happy to help 🙂

Hi @<1570220844972511232:profile|ObnoxiousBluewhale25> , you can click on the model in the artifacts tab and that should take you to the model repository. What is logged in the url of the model?

Hi UnevenDolphin73 ,

If I look at a specific experiment (say, the Artifacts tab), and then click on another experiment in the experiment list, it used to automatically show the newly selected experiment's Artifacts tab. It still does this, but it now shows a blank page. I have to choose a different tab and switch back.I think they fixed it in the next version that should be released soon.

(Not sure if by design) When selecting an experiment in a (new) project, it used to automatically swit...

Might make life easier 🙂

https://github.com/allegroai/clearml/blob/master/examples/services/aws-autoscaler/aws_autoscaler.py#L39

And where are these login/pass env vars are used?

It looks like there might be a firewall or something of the sort, please try the curl command from the machine itself to verify

It's the ~/clearml.conf on the side of the agent/clearml that runs the script 🙂