heres is the log when executing with --foreground. but is there any difference?

I run clearml-agent manually in gpu available pod using command clearml-agent daemon --queue shelley
and this doesn’t show gpu usage same with when i run task remotely

and here is the log

agent.worker_id =
agent.worker_name = shelley-gpu-pod
agent.force_git_ssh_protocol = false
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version.0 = <20.2 ; python_version < ‘3.10’
agent.package_manager.pip_version.1 = <22.3 ; python_ver...

it is working on on-premise machine(i can see gpu usage on WORKERS & QUEUES Dashboard). but it is not working on cloud pod

nope. just running “clearml-agent daemon --queue shelley”

sure! this one?

yes!

can i hide some of them without fixing and rebuilding docker image?

clearml==1.9.2
clearml-session==0.4.0

@<1523701070390366208:profile|CostlyOstrich36> Hello. Oh, sorry for the lack of explanation.when i execute the command “clearml-session ~“, jupyter url format is ‘ None :{local_jupyter_port}/?token={jupyter_token}’ and vs code url format is just ‘ None :{local_vscode_port}’ like the pic i attached here. I wonder why vs code url doesn’t have token.

i am having same issue: None

@<1523701087100473344:profile|SuccessfulKoala55> Okay..but how can i specify agent’s verison in helm chart?

i fount the solution!! i added configuration to helm’s values.yaml below.

additionalConfigs:
# services.conf: |
# tasks {
# non_responsive_tasks_watchdog {
# # In-progress tasks that haven’t been updated for at least ‘value’ seconds will be stopped by the watchdog
# threshold_sec: 21000
# # Watchdog will sleep for this number of seconds after each cycle
# watch_interval_sec: 900
# }
# }
apiserver.co...

Are there other people experiencing the same issue as me?

This is clearml-agent helm chart values.yaml file i used to install

alright. thanks 🙂 i hope that too.

@<1523701205467926528:profile|AgitatedDove14> @<1529271085315395584:profile|AmusedCat74> Hi guys 🙌

• I think that by default it uses the host network so it can take care of that, are you saying you added k8s integration ?-> Yes, i modified clearml-agent helm chart.
• “SSH allows access with password” it is a very long random password, not sure I see a risk here, wdyt?-> Currently, when enqueueing a task, clearml-session generates a long random password for SSH and VS Code and...

it’s been working well until i removed virtualenv and recreated, then i reinstall only clearml and clearml-session

wow ! thank you for quick answer! it worked like a charm. have a nice day:)

The clearml server I installed is a self-hosted server, and developers log in using a fixed ID and password for authentication. That’s it!

Futhermore, to access ssh/vscode/jupyterlab directly without ssh tunneling, I modified the clearml-session script, and once I upload this script to the DevOps project in draft status, developers clone it to their own project. Then, they enqueue and wait for the command and URL to access ssh/vscode/jupyterlab, which will be displayed.