AgitatedDove14 done) btw, could you show me the place in the code where scalars are written? I want to make a hotfix

/opt/clearml/logs/apiserver.log:

@<1523701070390366208:profile|CostlyOstrich36> yes, WebApp: 1.12.1-397 • Server: 1.12.1-397 • API: 2.26.
Docker version 28.0.1, build 068a01e (updated to this version few weeks ago).

no, other

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

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

can’t find the desired method, I thought that tasks.get_configurations is exactly what I need, but it is not

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

the same result

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.

` 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...

ContemplativeGoat37 and new

ConvolutedFox44 any updates?

what was the old version?

I've tried to run something like clearml-task ... --docker_bash_setup_script "cd $(echo /root/.clearml/venvs-builds/${PYTHON_VERSION%.*}/task_repository/...) && pip install ." but it not working because the repo is fetching after docker_bash_setup_script, so I need to run this command later

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

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

task log
` task ca3ab0ce39aa436f9e656fff378a2c25 pulled from c39519fcfb3f4353808fd266d6100795 by worker v012-0:gpuGPU-0929fd0f-eff1-91f1-854e-9874599660c3
2022-12-12 16:32:21
Current configuration (clearml_agent v1.5.1, location: /tmp/.clearml_agent.guezjnez.cfg):

api.version = 1.5
api.verify_certificate = true
api.default_version = 1.5
api.http.max_req_size = 15728640
api.http.retries.total = 240
api.http.retries.connect = 240
api.http.retries.read = 240
api.http.retri...

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 thank you! appreciate the quick response!

@<1523701070390366208:profile|CostlyOstrich36> I need to compare aggregated values: I want to compare mean metric value of N experiments from project 1 vs mean metric value of N experiments from project 2

this is the case of developing AutoML: when I have a lot of datasets and the model should be nice at all datasets in average. So projects here are different versions of AutoML and I need to compare them

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

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

I want aggregate only final metrics from the model. For example, "Metric HO" (holdout) here:

Hi AgitatedDove14 , I’m using clearml clearml-task to queue a task in a remote agent. The git remote URL is “ ssh://git@0.0.0.0:1234/path/to/repo.git ”, clearml https://github.com/allegroai/clearml/blob/aad01056b548660bb271c4f98447b715b8ba4c7d/clearml/backend_interface/task/repo/scriptinfo.py#L909 username from it (to cover cases like https://username@github.com/username/repository.git ), so the final URL is ssh://0.0.0.0:1234/path/to/repo.git , not ssh://git@0.0.0.0:1234/path/to/repo.g...

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 :(