yeah

Hi @<1523701087100473344:profile|SuccessfulKoala55> , here is an example :
On the picture of the Dataset 'DS_Master', the versions 1.0.1,1.0.2,1.0.3 and 1.0.4 are all children of the version 1.0.0. When I go on one specific version, I can see that the version 1.0.0 is the parent of the version I'm looking at. But when I go on the version 1.0.4 for example, I dont' know that the versions 1.0.1,1.0.2,1.0.3 are also children of the version 1.0.0. And I would like to see that on a graph, like t...

Hey @<1537605940121964544:profile|EnthusiasticShrimp49> , yes I can download it and open it with pickle, here is how I do it :
pickle_data_url = ' None '
local_iris_pkl = StorageManager.get_local_copy(remote_url=pickle_data_url)
with open(local_iris_pkl, 'rb') as f:
iris = pickle.load(f)

I set up my agent from my machine but my open-source server is not running on my machine. I can share my agent conf...

Hi @<1523701070390366208:profile|CostlyOstrich36> , you can reproduce it with the pipeline of the iris dataset from the github None
I have a gitlab repo, I run this command to run this pipeline :
clearml-task --project test-iris --name pipeline-iris --repo ***.git --script pipeline/pipeline_from tasks.py --queue services --requirements requirements.txt --task-type controller --branch main
My agent is setup as a docke...

Hi @<1523701087100473344:profile|SuccessfulKoala55> , I see. With my team we are wondering what should be the best practice to train and make predictions with machine learning models: do we get models from artifacts to make predictions or is it a better approach to get models from "models" ? 🤔

Thank you for your help 😄

Hi @<1523701070390366208:profile|CostlyOstrich36> , I'm using pipeline from tasks, am I able to do the same as pipeline from decorator ?

Yeah I mean disk space usage (we have our own clearml server)

Hi @<1523701205467926528:profile|AgitatedDove14> , I added pipeline._task.add_tags(tags) and it works, thank you very much 👍

Hi @<1523701070390366208:profile|CostlyOstrich36> , thank you for your answer, sadly it "only" adds tags to the steps of the pipeline, not the pipeline itself. And that's the last part I'm looking for.

Okay, I found it : I put an if statement with the parameter and continue_on_fail=True in the steps, and it worked. But I had to specify the continue_on_fail in add_step to make it work.

Hi @<1523701087100473344:profile|SuccessfulKoala55> , Sorry for the delay, thank you for your answer 😉

I just checked, and there was no filter (sadly)

Hi @<1523701070390366208:profile|CostlyOstrich36> @<1537605940121964544:profile|EnthusiasticShrimp49> , thank you for your interest, I was wondering if you had time to quickly check my issue

Yes, that's right

Hi @<1523701205467926528:profile|AgitatedDove14> , I understand what you mean.
In the case when I want to change my bucket values, it will not update them on Grafana and I will have to add a new endpoint. Is there a way to update the bucket values in Grafana, or deleting the variable/metric in Grafana ?

Thank you for your answer, it worked 😃

Here we go @<1523701070390366208:profile|CostlyOstrich36>

Hi John, I'm waiting for the approval of my superior before I can share it

Hi @<1523701205467926528:profile|AgitatedDove14> , sorry for the delay, I have a better understanding oh workers and agents now, thank you 😁

Wow I didn't send my answer sorry about it.
Yeah you're right keeping old data is always a good way.
I'm new to Grafana so I get questions about it. Anyway thank you for your answer 👍

We got the server version : 1.12.1-397
We tried to delete a task "print hello world" from the web UI this morning, and we still find it on the disk space of our server

I'm trying to delete projects, datasets and pipeline from the web UI at my local server adress. For example if I want to delete a dataset, I put it in archive > delete the file only by clicking on the web UI (not with python).
When my team leader look at the disk space usage of the server (in docker), he can still access to this file with the dataset, even if I deleted it from the web UI.

Hi @<1523701118159294464:profile|ExasperatedCrab78> !
Here there are (left: locally, right: remotely)

Hi @<1523701070390366208:profile|CostlyOstrich36> , sure, here there are 😉

Hi @<1523701070390366208:profile|CostlyOstrich36> , I have version 1.9.2. When I use the command clearml-task like this one : clearml-task --project test_tag_git --name sklearn --repo http://***.git --script sklearn.py --requirements requirements.txt --branch test_tag --output-uri http://*** using the script from here : None . 'test_tag' is the name of my git tag. When executi...

Can we have one worker running for multiple agents ?

Hi @<1523701087100473344:profile|SuccessfulKoala55> , thank you for your answer, I will look into it 👍

Hi @<1523701205467926528:profile|AgitatedDove14> , yes the pipeline is created via the clearml-task CLI. I find it less constraining to launch a pipeline via the CLI. I'm opening a GitHub issue right now, hoping it will be fixed soon. Thank you for your answer 😁

Hi @<1523701435869433856:profile|SmugDolphin23> ! Thank you for your answer, I will try both your suggestions 😉