@<1523701087100473344:profile|SuccessfulKoala55> yes, elastic is failed. don’t understand why

sure
print(APIClient().tasks.get_all(["95db561a08304a1faac3aabcb117412e"]))
{‘id’: ‘95db561a08304a1faac3aabcb117412e’, ‘name’: ‘task’}

sorry, just found it)

ContemplativeGoat37 and new

AgitatedDove14 , do you know the answer?

the same result

no, other

what was the old version?

CostlyOstrich36 thank you! appreciate the quick response!

RoundMosquito25 hi, any updates?

AgitatedDove14 for example let’s add to https://github.com/allegroai/clearml/blob/master/examples/frameworks/catboost/catboost_example.py second catboost model training:
` ...
catboost_model = CatBoostRegressor(iterations=iterations, verbose=False)
catboost_model2 = CatBoostRegressor(iterations=iterations+200, verbose=False)
...
catboost_model.fit(train_pool, eval_set=test_pool, verbose=True, plot=False, save_snapshot=True)
catboost_model2.fit(train_pool, eval_set=test_pool, verbose=True,...

CostlyOstrich36 I’m running the task that doesn’t need GPU by this command
clearml-task … --docker python:3.7.13-bullseye

AgitatedDove14 hm, I don’t know what is the right expected behaviour, I’ve expected 2 plots. If my assumption looks right, should I make an issue on github?

` import numpy as np
import pandas as pd

from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from lightautoml.tasks import Task
from lightautoml.automl.presets.tabular_presets import TabularAutoML

import clearml

cml_task = clearml.Task.get_task(clearml.config.get_remote_task_id())
logger = cml_task.get_logger()

data = pd.read_csv("./examples/data/sampled_app_train.csv")
....

automl = TabularAutoML(task=Task('binary'))
cml_task.connect(automl)
o...

/opt/clearml/logs/apiserver.log:

AgitatedDove14 the best option would be custom charts in Web UI, like in wandb: https://docs.wandb.ai/ref/app/features/custom-charts
But pdf is acceptable too.

so we may use more specific lib/tool or just add a if-statement for case “ssh:git@”

I think docker mode is what you need to use if you want to pre-install packages in an environment

In order to use newest version I have to install the library at every run. I don’t think that building a docker image at every run is a good solution here. So the only solution is add it pythonically.

CostlyOstrich36 it is ok if I use agent in docker mode, but what should I use in other cases?

docker will Not actually limit the “vioew of the memory” it will just kill the container if you pass the memory limit, this is a limitation of docker runtime

it will only if oom killer is enabled

When I updated the URL of the remote repository in my git client

SuperiorPanda77 did you just replace “remote” for the client?

My remote in git client is ok:
ssh://git @<address>:5109<repo_path>.git
so I don’t understand why and where it changes :(

ContemplativeGoat37 hi, any updates? I have a similar issue due executing clearml-data create command, also the status is stuck in “uploading”

And when I’m trying to add a file to dataset, this happens:
` Retrying (Retry(total=2, connect=2, read=5, redirect=5, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f20d7231430>: Failed to establish a new connection: [Errno 111] Connection refused')': /
Retrying (Retry(total=1, conn...

Hi CostlyOstrich36 , I can’t find any options for specifying multiple workers for one GPU. Do you mean just run this command twice?
clearml-agent daemon --queue myqueue --gpus 0

SuccessfulKoala55 yes, I have /usr/bin/python3.8, but it doesn’t help if I set it in agent.python_binary. python3.8 set as alternative #1 for python. but conda for some reason creating env with python3.6...

Executing Conda: /home/user/conda/bin/conda env remove -p /home/jovyan/.clearml/venvs-builds/3.6 --quiet --json

CostlyOstrich36 self hosted,
clearml ==1.6.4
clearml -agent==1.4.0rc0

python:3.7.13-bullseye

CostlyOstrich36 no, there is only task_id and name in response

oh, should I use --cpu-only flag?