what was the old version?

ContemplativeGoat37 and new

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.

ConvolutedFox44 any updates?

AgitatedDove14 sorry, no, in fact my configuration looks like:

` ...

agent.git_user=""
agent.git_pass=""
agent.git_host=""

agent.package_manager.extra_index_url= [

]

agent {
worker_id: ""
worker_name: ""
force_git_ssh_protocol: true

... `

I’ve found the answer:
https://github.com/allegroai/clearml-agent/issues/42#issuecomment-887331420

AgitatedDove14

Specifically

/tmp/clearml_agent.ssh.rbw8o0t7

is the copy of the .ssh that the agent created, and now it is mounting it into the container

but why is it mounted only once? second and following containers do not mount the folder

upd: the status is changed to “Aborted”

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

AgitatedDove14 , do you know the answer?

RoundMosquito25 hi, any updates?

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 :(

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...

CostlyOstrich36 thank you! appreciate the quick response!

the same result

AgitatedDove14 no, it’s not a request.

I have custom python class, that uses a lot of models from frameworks that supported by ClearML already. I want to enable auto reporting for all models by using command clearml_task.connect(my_custom_class_instance) , but it doesn’t work the way I need it to — there is the only one loss curve, because because this graph is redrawn every time a new instance starts training.
Is there any way to reporting all instances inside my custom class without ...

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

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

python:3.7.13-bullseye

sorry, just found it)

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

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...

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,...

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?

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

@<1523701181375844352:profile|ExasperatedCrocodile76> hi, try to pass “--network=host” to --docker_args
example:

clearml-task --project project --name name --script run.py --queue queue --requirements requirements.txt --docker python:3.7.13-bullseye --docker_args "--cpus=8 --memory=16g --network=host"

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