RoughTiger69 So basically (If I follow your example), the question is whether ClearML "knows" Task B" is a clone of "Task A"?
And if the loaded Dataset Y, is somehow registered on Task X?
Is that correct?

Do you want the task object or the task id?

So in which scenario do you want to keep those folders as artifacts and where would you like to store them?

Hi DeliciousBluewhale87 ,

Can you please elaborate a bit? The fileserver is a very simple component for storing and retrieving data. You can save any form of data on it. What exactly is your use case?

Can you add the errors you had?

DeliciousBluewhale87 , Hi!

I think you can have models/artifacts automatically copied to a location if the experiment is initialized with
output_uri
For example:
task = Task.init('examples', 'model test', output_uri=' ')What version of ClearML are you using? I'd suggest upgrading to the latest 🙂

DeliciousBluewhale87 , I believe so, yes 🙂

ClearML has a built in model repository so together I think they make a "feature store" again, it really depends on your definition

It looks like it since as far as I manage to understand a 'traditional' feature store is a model / data repository, correct?

Hi DeliciousBluewhale87 , yes I think it does. Although I think ClearML-Serving works as a control plane on top of your serving engine.

MagnificentSeaurchin79 , can you please describe how you created the dataset?

Hi PanickyMoth78 ,

What version of ClearML are you using?

RoughTiger69 thanks for the input 🙂

If you run an agent in docker mode ( --docker ) the agent will run a docker run command and the task will be executed inside a container. In that scenario, I think, if you kill the daemon then the docker will stay up and finish the job (i think, haven't tested)

It will stop running

Hi @<1658281112104865792:profile|ExasperatedDove89> , I would suggest going over this doc 🙂
None

Yes. Run all the pipelines examples and see how the parameters are added via code to the controller.

For example:
None

Hi, I think you can get that from support@clear.ml

Hi @<1643060818490691584:profile|MagnificentHedgehong41> , did you specify a project name? You can go into settings and enable showing hidden projects/experiments and then you will be able to see the pipeline steps in projects as well

Hi @<1661904968040321024:profile|SpotlessOwl43> , you can achieve this using the REST API of ClearML - None

UpsetTurkey67 , take a look here:
https://github.com/allegroai/clearml/tree/master/examples/services/monitoring

Hi @<1669152726245707776:profile|ManiacalParrot65> , is this a specific task or the controller?

@<1554638160548335616:profile|AverageSealion33> , what if you just run a very simple piece of code that includes Task.init() ? One of the examples in the repository, does this issue reproduce?

Hi @<1554638160548335616:profile|AverageSealion33> , so if you remove the Task.init() everything goes back to working fine?

This is strange. Can you take a look inside the apiserver and webserver docker logs to see if any errors pop up?

JitteryCoyote63 , you can also double click an experiment to get the context menu 🙂

Hi @<1679299596997627904:profile|OddOstrich66> , can you please add the full log? Also some code snippet would be helpful 🙂

Hi @<1547028031053238272:profile|MassiveGoldfish6> , you should set output_uri in Task.init to point towards your S3 bucket 🙂

I suggest taking a look at this example - None

Hi @<1664079296102141952:profile|DangerousStarfish38> , you can control it in the agent.default_docker.image section of the clearml.conf where the agent is running. You can also control it via the CLI when you use the --docker tag and finally, you can also control it via the webUI in the execution tab -> container -> image section