Version: 1.6.4

Well it seems that we have similar https://github.com/allegroai/clearml-agent/issues/86
we are not able to reference this orphan worker (it does not show up with ps -ef | grep clearml-agent -
but still appears with clearml-agent list
and not able to stop with clearml-agent daemon --stop clearml-server-agent-group-cpu-agent-5df4476cfc-j54gh:0
getting
` Could not find a running clearml-agent instance with worker_name=clearml-server-agent-group-cpu-agent-5df4476cfc-j54gh:0 wo...

not sure i understand
we are running the daemon in a detached mode

clearml-agent daemon --queue <execution_queue_to_pull_from> --detached

Sorry - I'm a Helm newbee
when running
helm search repo clearml --versionsI can't see version 3.6.2 - the highest is 3.5.0
This is the repo that we used to get the helm chart
helm repo add allegroaiWhat I'm I missing?

Still trying to understand what is this default worker.
I've removed clearml.conf and reinstall clearml-agent
then running the
clearml-agent listgets the following error
` Using built-in ClearML default key/secret

clearml_agent: ERROR: Could not find host server definition (missing ~/clearml.conf or Environment CLEARML_API_HOST)
To get started with ClearML: setup your own clearml-server, or create a free account at and run clearml-agent init Then returning the...

is this running from the same linux user on which you checked the git ssh clone on that machine?

yes

The only thing that could account for this issue is somehow the agent is not getting the right info from the ~/.ssh folder

maybe -
Question - if we change the clearml.conf do we need to stop and start the daemon?

agree -
we understand now that the worker is the default worker that is installed after running
pip install clearml-agentis it possible to remove it ? since all tasks that use the worker don't have the correct credentials.

so running the command clearml-agent -d list returns the https://clearml.slack.com/archives/CTK20V944/p1657174280006479?thread_ts=1657117193.653579&cid=CTK20V944

Thx for investigating - What is the use case for such behavior ?
How would you use the user properties as part of an experiment?

opened an https://github.com/allegroai/clearml/issues/668

TimelyPenguin76 - do you have any suggestions?

ClearML key/secret provided to the agent

When is this provided? Is this during the build ?

What key is used for this? I can only see git_user

BTW - is the CLEARML_HOST_IP relevant for the clearml-agent ?
i can see that we can create a worker with this environment variable . e.g.
CLEARML_WORKER_NAME=MY-WORKDER CLEARML_WORKER_ID=MY-WORKER:0 CLEARML_HOST_IP=X.X.X.X clearml-agent daemon --detachedmy mistake doesn't use it to create a dedicated IP

i've doubled checked the Path- and it is correct

Btw -after updating clearml.conf do I need to restart the agent?
I can't see the additional tab under https://clearml.slack.com/archives/CTK20V944/p1658199530781499?thread_ts=1658166689.168039&cid=CTK20V944 , and I reran the task and got the same error

How do you do so?
can't find it under
clearml-agent daemon -h

yes - the agent is running with --docker
Great - where do I define the volume mount?
Should I build a base image that runs on the server and then use it as the base image in the container?

upgrading to 1.12.1 didn't help
I think the issue is that when I create the dataset - i used

use_current_task=True,

If I change it to

use_current_task=False,

then it finalizes

This also may help with the configuration for GCS
https://clearml.slack.com/archives/CTK20V944/p1635957916292500?thread_ts=1635781244.237800&cid=CTK20V944

Feeling that we are nearly there ....
One more question -
Is there a way to configure Clearml to store all  the  artifacts    and the  Plots   etc. in a bucket instead of manually uploading/downloading the artifacts from within the client's code?
Specifying the output_uri in Task.init saves the the checkpoints, what about the rest of the outputs?
https://clear.ml/docs/latest/docs/faq#git-and-storage

Hi,
Thx for you response,
Yes - we are using the above repo.
We would like to have easy/cheaper access to the artifacts etc. that will be output from the experiments

Hi SuccessfulKoala55
Thx again for your help

in case of the google colab, the values can be provided as environment variables

We still need to run the code in a colab environment (or remote client)
do you have any example for setting the environment variables?
For a general environment variable there is an example
! export MPLBACKEND=TkAgBut what would be for the clearml.conf ?
retrieving we can use

config_obj.get('sdk.google')

but how would the setting work? we did ...

But this is not on the pods, isn't it? We're talking about the python code running from COLAB or locally...?

correct - but where is the clearml.conf file?

Just for the record - I guess there is an option to use os.environ
https://github.com/allegroai/clearml/blob/ca7909f0349b255f7edca0500878a8e08f3b1c99/clearml/automation/auto_scaler.py#L152-L157

Hi

you will have to configure the credentials there (in a local 

clearml.conf

 or using environment variables

This is the part that confuses me - is there a way to configure clearml.conf from the values.yaml ? I would like the GKE to load the cluster with the correct credentials without logging into the pods and manually updating the claerml.conf file

Are we suppose to use the "Extra Configurations" from the https://clear.ml/docs/latest/assets/images/ClearML_Server_Diagram-7ea19db8e22a7737f062cce207befe38.png ?
https://docs.google.com/drawings/d/11f-AWVmIq7P0e8bP5OnMUz0hguXm2T_Xqq7iNMA-ANA/edit?usp=sharing

Great - Thx for the clarifications!

Thx CostlyOstrich36 for your input -
So I guess that if we try to always work with https://clear.ml/docs/latest/docs/fundamentals/hyperparameters (even if there is only have 1 parameter), we will consistently log our parameters.
Do you have a suggested different workflow?

Strange
I ran
clearml-agent daemon --stopand after 10 min I ran
clearml-agent listand I still see a worker