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

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

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 😉

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

Here we go @<1523701070390366208:profile|CostlyOstrich36>

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

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> , 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.

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 👍

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 👍

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

Can we have one worker running for multiple agents ?

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

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

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

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

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

Thank you for your answer, it worked 😃

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...

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 👍

Yes, that's right

Oh okay, it could explain a lot of stuff. Thank you for your answer 👍 My server isn't on 0.0.0.0, so would I need to setup a new one to solve this problem, or is there an alternative ?
I checked the logs as you suggested, I didn't find any error of this type (maybe I didn't put an important parameter). My agent is setup as a docker. Here are the logs.

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

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="...

yeah