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 ?

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...

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

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

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

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...

Can we have one worker running for multiple agents ?

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 👍

Thank you for your answer, it worked 😃

Yes, that's right

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" ? 🤔

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

Hi @<1523701435869433856:profile|SmugDolphin23> ! I enqueued my task and I got an error sadly 😞 . I put the logs here

Okay, thank you for your answer and your concern 👍

Here we go @<1523701070390366208:profile|CostlyOstrich36>

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 @<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...

With my team we found a solution: to execute tasks with agent, we use clearml-task in CLI. We add the argument --output-uri : ***:1234 where *** is the link to our self-hosted server. Then models in pickle are automatically exported to the server, and not the path of the agent

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

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

yeah

Hi @<1523701070390366208:profile|CostlyOstrich36> , sorry for the delay
I just found I could reveal the hidden projects in the setting, I think that was why I couldn't delete everything I wanted 😉

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

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

Thank you for your help 😄

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

No problem, I tried with this code :

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from joblib import dump
from clearml import Task, OutputModel

task = Task.init(project_name="serving examples", task_name="train sklearn model", output_uri=True)

# generate 2d classification dataset
X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
# fit final model
model = LogisticRegression()
model.fit(X, y)

#dump(model, filename="...

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.

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

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