Hey @<1649221394904387584:profile|RattySparrow90> - You can try configuring CLEARML__logging__root__level as an extraEnvs for the apiserver and fileserver 🙂

Hi @<1523701907598610432:profile|ReassuredArcticwolf33> - Are you referring to the clearml helm chart or to the clearml-agent one?
Either case, the respective values.yaml file is self-documented and contains example. Here I am reporting an example for adding additional volumes and volume mounts to the apiserver component of the clearml chart:

apiserver:
  # -- # Defines extra Kubernetes volumes to be attached to the pod.
  additionalVolumes:
    - name: ramdisk
      empty...

Wonderful - We do not have such feature planned for now, feel free to contribute 🙂

Hey @<1726047624538099712:profile|WorriedSwan6> - I am sorry, I forgot that the multi-queue feature with templateOverrides is only for the enterprise version.
What you can do, though, is to deploy two different agents in k8s using the helm chart. Simply try installing two different releases, then modify only one of them to have basePodTemplate use the nvidia.com/gpu : "4"
Let me know if this solves your issue 🙂

Hey @<1726047624538099712:profile|WorriedSwan6> , the basePodTemplate sections configures the default base template for all pods spawned by the Agent.
If you don't want every Task (or Pod) to use the same requests/limits, one thing you could try is to set up multiple queues in the Agent.
Each queue can then have an override of the Pod template.
So, you can try removing the nvidia.com/gpu : "4" from the root basePodTemplate and add a section like this in ...

@<1710827340621156352:profile|HungryFrog27> have you installed the Nvidia gpu-operator to advertise GPUs to Kubernetes?

Hey @<1743079861380976640:profile|HighKitten20> - Try to configure this section in the values override file for the Agent helm chart:

# -- Private image registry configuration
imageCredentials:
  # -- Use private authentication mode
  enabled: false
  # -- If this is set, chart will not generate a secret but will use what is defined here
  existingSecret: ""
  # -- Registry name
  registry: docker.io
  # -- Registry username
  username: someone
  # -- Registry password
  password: pwd...

Hey @<1734020156465614848:profile|ClearKitten90> - You can try with the following in your ClearML Agent override helm values. Make sure to replace mygitusername and git-password

agentk8sglue:
  basePodTemplate:
    env:
      # to setup access to private repo, setup secret with git credentials
      - name: CLEARML_AGENT_GIT_USER
        value: mygitusername
      - name: CLEARML_AGENT_GIT_PASS
        valueFrom:
          secretKeyRef:
            name: git-password
         ...

Sure! I'll talk to the guys to update the documentation 🙂

Hi @<1523708147405950976:profile|AntsyElk37> - Yes, having the runtimeClass makes sense. I am handling your PR soon 🙂

value can be DEBUG, INFO, WARNING, ERROR, CRITICAL

@<1734020208089108480:profile|WickedHare16> - please try configuring the cookieDomain

clearml:
  cookieDomain: ""

You should set it as your base domain, example pixis.internal , without any api or files in front of it

Hey @<1736194540286513152:profile|DeliciousSeaturtle82> , yes please try changing the health check to /debug.conf or /debug.ping 🙂

Hi @<1798162812862730240:profile|PreciousCentipede43> 🙂
When you say

one of the api backend is UNHEALTHY

do you mean you have multiple replicas of the apiserver component (i.e. you set the values apiserver.replicaCount > 1) and one of them is not ready?
Could you please share the output of the kubectl describe command for the ClearML apiserver Deployment?

Hi @<1798887585121046528:profile|WobblyFrog79> - Please try setting the environment variable CLEARML_K8S_GLUE_DEBUG=1 on the Agent

agentk8sglue:
  extraEnvs:
    - name: CLEARML_K8S_GLUE_DEBUG
      value: "1"

This will make the Agent Pod print the rendered Task Pod template in the logs, so you can see it 🙂

I think Mongo does not like for its db folder to be replaced like this in the running Pod.
You can try by turning off Mongo for a moment (scale it down to 0 replicas from the deployment), then create a one-time Pod (non-mongo, you can use an ubuntu image for example) mounting the same volume that Mongo was mounting, and try using this Pod to copy the db folder in the right place. When it's done, delete this Pod and scale back to 1 the Mongo deployment.

@<1736194540286513152:profile|DeliciousSeaturtle82> when you copy the folder on the new pod, it crashes almost instantly?

@<1736194540286513152:profile|DeliciousSeaturtle82> the data folder for mongo4 and mongo5 might be slightly different. What is the target path where you're moving data in mongo5? And how is that mounted?
And when you say "broken", could you elaborate on that? Does the target Mongo Pod crash when trying to move the data? Or you succeed in copying the data but can't see the result in the UI?

Hello @<1523708147405950976:profile|AntsyElk37> 🙂
You are right, the spec.runtimeClassName field is not supported in the Agent at the moment, I'll work on your Pull Request ASAP.
Could you elaborate a bit about why you need Tasks Pods to specify the runtimeclass to use GPUs?
Usually, you'd need to specify a Pod's container with, for example, resources.limits.nvidia.com/gpu : 1 , and the Nvidia Device Plugin would itself assign the correct device to the container. Will that work?

Hey @<1523701304709353472:profile|OddShrimp85> - You can tweak the following section in the clearml-agent override values:

# -- Global parameters section
global:
  # -- Images registry
  imageRegistry: "docker.io"

# -- Private image registry configuration
imageCredentials:
  # -- Use private authentication mode
  enabled: true # <-- Set this to true
  # -- Registry name
  registry: docker.io
  # -- Registry username
  username: someone
  # -- Registry password
  password: pwd
  # -- ...

@<1752864322440138752:profile|GiddyDragonfly90> - MongoDB is used as a dependency Helm Chart from the Bitnami repo. We are using version 12.1.31 of the chart. See this tag None

In the clearml override values, under the mongodb section you can specify any value that is usable in the original chart 🙂

Hey @<1734020208089108480:profile|WickedHare16> - Not 100% sure this is the issue, but I noticed a wrong configuration in your values.
You configured both these:

elasticsearch:
  enabled: true


externalServices:
  # -- Existing ElasticSearch connectionstring if elasticsearch.enabled is false (example in values.yaml)
  elasticsearchConnectionString: "[{\"host\":\"es_hostname1\",\"port\":9200},{\"host\":\"es_hostname2\",\"port\":9200},{\"host\":\"es_hostname3\",\"port\":9200}]"

Pl...

@<1669152726245707776:profile|ManiacalParrot65> could you please send your values file override for the Agent helm chart?

For this to work you might also have to add "secure": true in the connection string object:

externalServices:
    elasticsearchConnectionString: "[...,"secure":true,...]"

If that doesn't work, try removing the auth from the connection string and instead define two extraEnvs for the apiserver :

apiserver:
  extraEnvs:
    - name: CLEARML_ELASTIC_SERVICE_USERNAME
      value: "elastic"
    - name: CLEARML_ELASTIC_SERVICE_PASSWORD
      value: "toto"

I see, in the example you provided you used a comma , to separate username and password, I suggest trying to use a column :

Hi @<1752864322440138752:profile|GiddyDragonfly90> - Can you try with the last value you proposed, but use : to separate user and password in the string, like this:

externalServices:
    elasticsearchConnectionString: '[{"scheme":"http","host":"elastic:toto@elasticsearch-es-http","port":9200}]'

@<1752864322440138752:profile|GiddyDragonfly90> - I think you can also add verify_certs: false in the same elasticsearchConnectionString object, have you tried?

🙂 let me know if that works for you

Hey @<1734020208089108480:profile|WickedHare16> , could you please share your override values file for the clearml helm chart?