Can you try with these values? For instance the changes are: not using clearmlConfig, not overriding the image and use default, not defining resources

agentk8sglue:
  apiServerUrlReference: 

  clearmlcheckCertificate: false
  createQueueIfNotExists: true
  fileServerUrlReference: 

  queue: default
  webServerUrlReference: 

clearml:
  agentk8sglueKey: 8888TMDLWYY7ZQJJ0I7R2X2RSP8XFT
  agentk8sglueSecret: oNODbBkDGhcDscTENQyr-GM0cE8IO7xmpaPdqyfsfaWearo1S8EQ8eBOxu-opW8dVUU
sessions:
  externalIP: 192.168.70.211
  maxServices: 5
  startingPort: 30100
  svcType: NodePort

I will try to create them on the UI and only run the Agent task on argo or so to see if it helps

If i run helm get values clearml-agent -n clearml-prod
the output is the following:
USER-SUPPLIED VALUES:
agentk8sglue:
apiServerUrlReference: None
clearmlcheckCertificate: false
createQueueIfNotExists: true
fileServerUrlReference: None
image:
pullPolicy: Always
repository: allegroai/clearml-agent-k8s-base
tag: 1.25-1
queue: default
resources:
limits:
cpu: 500m
memory: 1Gi
requests:
cpu: 100m
memory: 256Mi
webServerUrlReference: None
clearml:
agentk8sglueKey: CLEARML8AGENT9KEY1234567890ABCD
agentk8sglueSecret: CLEARML-AGENT-SECRET-1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ123456
clearmlConfig: |-
api {
web_server: None
api_server: None
files_server: None
credentials {
"access_key" = "CLEARML8AGENT9KEY1234567890ABCD"
"secret_key" = "CLEARML-AGENT-SECRET-1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ123456"
}
}
sessions:
externalIP: 192.168.70.211
maxServices: 5
startingPort: 30100
svcType: NodePort

Python regex error in k8s glue agent :

sre_constants.error: bad inline flags: cannot turn on global flag at position 92

• Issue is in clearml-agent k8s glue codebase (Python 3.6 compatibility)
• Not configuration-related - persists across different HOCON formats
• Affects image tags: 1.24-21 , 1.24-23 , latest

I will try :
1- update the agent with these values
2- run argo with those changes

I understand, I'd just like to make sure if that's the root issue and there's no other bug, and if so then you can think of how to automate it via API

As far as i can test, the server is going ok, i had some isses with resources not loading but solved those. The bigger issue for now is agent and prob could propagate to the serving. Later on i plan on adding also gpu resouces to both so im not entirely sure on that part

clearml-apiserver-866ccf75f7-zr5wx 1/1 Running 0 37m
clearml-apiserver-asyncdelete-8dfb574b8-8gbcv 1/1 Running 0 37m
clearml-elastic-master-0 1/1 Running 0 37m
clearml-fileserver-86b8ddf6f6-4xnqd 1/1 Running 0 37m
clearml-mongodb-5f995fbb5-xmdpb 1/1 Running 0 37m
clearml-redis-master-0 1/1 Running 0 37m
clearml-webserver-c487cfcb-vv5z5 1/1 Running 0 37m

I will get back at you in 15mn if thats ok

Ok will try it

Oh no worries, I understand 😄
Sure, if you could share the whole values and configs you're using to run both the server and agent that would be useful.
Also what about other Pods from the ClearML server, are there any other crash or similar error referring to a read-only filesystem? Are the server and agent installed on the same K8s node?

Hi, im trying to add the agent to a running server and facing the same issue.

Defaulted container "k8s-glue" out of: k8s-glue, init-k8s-glue (init)
p = sre_compile.compile(pattern, flags)
File "/usr/lib/python3.6/sre_compile.py", line 562, in compile
p = sre_parse.parse(p, flags)
File "/usr/lib/python3.6/sre_parse.py", line 855, in parse
p = _parse_sub(source, pattern, flags & SRE_FLAG_VERBOSE, 0)
File "/usr/lib/python3.6/sre_parse.py", line 416, in _parse_sub
not nested and not items))
File "/usr/lib/python3.6/sre_parse.py", line 765, in _parse
p = _parse_sub(source, state, sub_verbose, nested + 1)
File "/usr/lib/python3.6/sre_parse.py", line 416, in _parse_sub
not nested and not items))
File "/usr/lib/python3.6/sre_parse.py", line 765, in _parse
p = _parse_sub(source, state, sub_verbose, nested + 1)
File "/usr/lib/python3.6/sre_parse.py", line 416, in _parse_sub
not nested and not items))
File "/usr/lib/python3.6/sre_parse.py", line 734, in _parse
flags = _parse_flags(source, state, char)
File "/usr/lib/python3.6/sre_parse.py", line 803, in _parse_flags
raise source.error("bad inline flags: cannot turn on global flag", 1)
sre_constants.error: bad inline flags: cannot turn on global flag at position 92 (line 4, column 20)

just to check is this the intended image: docker.io/allegroai/clearml-agent-k8s-base:1.24-2

kubectl describe pod -n clearml-prod -l app.kubernetes.io/name=clearml-agent
kubectl logs -n clearml-prod -l app.kubernetes.io/name=clearml-agent --previous 2>/dev/null || true
Name:             clearml-agent-848875fbdc-x8x6s
Namespace:        clearml-prod
Priority:         0
Service Account:  clearml-agent-sa
Node:             kharrinhao/192.168.70.211
Start Time:       Mon, 21 Jul 2025 15:23:02 +0000
Labels:           app.kubernetes.io/instance=clearml-agent
                  app.kubernetes.io/managed-by=Helm
                  app.kubernetes.io/name=clearml-agent
                  app.kubernetes.io/version=1.24
                  helm.sh/chart=clearml-agent-5.3.3
                  pod-template-hash=848875fbdc
Annotations:      checksum/config: 5c1b50a353fea7ffd1fa5e62f968edc92e2610e0f0fd7783900a44f899ebe9ca
                  cni.projectcalico.org/containerID: 6964e25aa0cf54fa1dc91e36648d97e6deeae3366a924579be1e72742a25365a
                  cni.projectcalico.org/podIP: 192.168.31.162/32
                  cni.projectcalico.org/podIPs: 192.168.31.162/32
Status:           Running
IP:               192.168.31.162
IPs:
  IP:           192.168.31.162
Controlled By:  ReplicaSet/clearml-agent-848875fbdc
Init Containers:
  init-k8s-glue:
    Container ID:  

5
    Image:         docker.io/allegroai/clearml-agent-k8s-base:1.24-21
    Image ID:      docker.io/allegroai/clearml-agent-k8s-base@sha256:772827a01bb5a4fff5941980634c8afa55d1d6bbf3ad805ccd4edafef6090f28
    Port:          <none>
    Host Port:     <none>
    Command:
      /bin/sh
      -c
      set -x; while [ $(curl --insecure -sw '%{http_code}' "

" -o /dev/null) -ne 200 ] ; do
        echo "waiting for apiserver" ;
        sleep 5 ;
      done; while [[ $(curl --insecure -sw '%{http_code}' "

" -o /dev/null) =~ 403|405 ]] ; do
        echo "waiting for fileserver" ;
        sleep 5 ;
      done; while [ $(curl --insecure -sw '%{http_code}' "

" -o /dev/null) -ne 200 ] ; do
        echo "waiting for webserver" ;
        sleep 5 ;
      done

    State:          Terminated
      Reason:       Completed
      Exit Code:    0
      Started:      Mon, 21 Jul 2025 15:23:03 +0000
      Finished:     Mon, 21 Jul 2025 15:23:03 +0000
    Ready:          True
    Restart Count:  0
    Environment:    <none>
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-7f2zt (ro)
Containers:
  k8s-glue:
    Container ID:  

6
    Image:         docker.io/allegroai/clearml-agent-k8s-base:1.24-21
    Image ID:      docker.io/allegroai/clearml-agent-k8s-base@sha256:772827a01bb5a4fff5941980634c8afa55d1d6bbf3ad805ccd4edafef6090f28
    Port:          <none>
    Host Port:     <none>
    Command:
      /bin/bash
      -c
      export PATH=$PATH:$HOME/bin; source /root/.bashrc && /root/entrypoint.sh

    State:          Waiting
      Reason:       CrashLoopBackOff
    Last State:     Terminated
      Reason:       Error
      Exit Code:    1
      Started:      Mon, 21 Jul 2025 15:23:58 +0000
      Finished:     Mon, 21 Jul 2025 15:24:02 +0000
    Ready:          False
    Restart Count:  3
    Environment:
      CLEARML_API_HOST:              

      CLEARML_WEB_HOST:              

      CLEARML_FILES_HOST:            

      CLEARML_API_HOST_VERIFY_CERT:  false
      K8S_GLUE_EXTRA_ARGS:           --namespace clearml-prod --template-yaml /root/template/template.yaml  --create-queue
      CLEARML_CONFIG_FILE:           /root/clearml.conf
      K8S_DEFAULT_NAMESPACE:         clearml-prod
      CLEARML_API_ACCESS_KEY:        <set to the key 'agentk8sglue_key' in secret 'clearml-agent-ac'>     Optional: false
      CLEARML_API_SECRET_KEY:        <set to the key 'agentk8sglue_secret' in secret 'clearml-agent-ac'>  Optional: false
      CLEARML_WORKER_ID:             clearml-agent
      CLEARML_AGENT_UPDATE_REPO:
      FORCE_CLEARML_AGENT_REPO:
      CLEARML_DOCKER_IMAGE:          ubuntu:18.04
      K8S_GLUE_QUEUE:                default
    Mounts:
      /root/clearml.conf from k8sagent-clearml-conf-volume (ro,path="clearml.conf")
      /root/template from clearml-agent-pt (rw)
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-7f2zt (ro)
Conditions:
  Type                        Status
  PodReadyToStartContainers   True
  Initialized                 True
  Ready                       False
  ContainersReady             False
  PodScheduled                True
Volumes:
  clearml-agent-pt:
    Type:      ConfigMap (a volume populated by a ConfigMap)
    Name:      clearml-agent-pt
    Optional:  false
  k8sagent-clearml-conf-volume:
    Type:        Secret (a volume populated by a Secret)
    SecretName:  clearml-agent-ac
    Optional:    false
  kube-api-access-7f2zt:
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
QoS Class:                   BestEffort
Node-Selectors:              <none>
Tolerations:                 node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                             node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
  Type     Reason     Age                From               Message
  ----     ------     ----               ----               -------
  Normal   Scheduled  96s                default-scheduler  Successfully assigned clearml-prod/clearml-agent-848875fbdc-x8x6s to kharrinhao
  Normal   Pulled     95s                kubelet            Container image "docker.io/allegroai/clearml-agent-k8s-base:1.24-21" already present on machine
  Normal   Created    95s                kubelet            Created container: init-k8s-glue
  Normal   Started    95s                kubelet            Started container init-k8s-glue
  Normal   Pulled     40s (x4 over 94s)  kubelet            Container image "docker.io/allegroai/clearml-agent-k8s-base:1.24-21" already present on machine
  Normal   Created    40s (x4 over 94s)  kubelet            Created container: k8s-glue
  Normal   Started    40s (x4 over 93s)  kubelet            Started container k8s-glue
  Warning  BackOff    10s (x6 over 84s)  kubelet            Back-off restarting failed container k8s-glue in pod clearml-agent-848875fbdc-x8x6s_clearml-prod(42a51ff8-6423-485a-89e3-6109b3c0583a)
    not nested and not items))
  File "/usr/lib/python3.6/sre_parse.py", line 765, in _parse
    p = _parse_sub(source, state, sub_verbose, nested + 1)
  File "/usr/lib/python3.6/sre_parse.py", line 416, in _parse_sub
    not nested and not items))
  File "/usr/lib/python3.6/sre_parse.py", line 734, in _parse
    flags = _parse_flags(source, state, char)
  File "/usr/lib/python3.6/sre_parse.py", line 803, in _parse_flags
    raise source.error("bad inline flags: cannot turn on global flag", 1)
sre_constants.error: bad inline flags: cannot turn on global flag at position 92 (line 4, column 20)

yes

Also, in order to simplify the installation, can you use a simpler version of your values for now, something like this should work:

agentk8sglue:
  apiServerUrlReference: 

  clearmlcheckCertificate: false
  createQueueIfNotExists: true
  fileServerUrlReference: 

  queue: default
  resources:
    limits:
      cpu: 500m
      memory: 1Gi
    requests:
      cpu: 100m
      memory: 256Mi
  webServerUrlReference: 

clearml:
  agentk8sglueKey: <NEW_KEY>
  agentk8sglueSecret: <NEW_SECRET>
sessions:
  externalIP: 192.168.70.211
  maxServices: 5
  startingPort: 30100
  svcType: NodePort

Oh, okay, not sure this will be the only issue but you'll need these credentials to be valid, since they are used by the ClearML Agent to connect to the ClearML Server 🙂
The easiest way to generate credentials is to open the ClearML UI in the browser, login with an Admin user, then navigate to the Settings located on the top right corner when clicking on the user icon. From there go to "Workspace" and click "Create new credentials" and use the value provided

resulting in the same issue

for now:

• name: clearml-access-key
  value: CLEARML8AGENT9KEY1234567890ABCD
  - name: clearml-secret-key
  value: CLEARML-AGENT-SECRET-1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ123456
  - name: admin-password
  value: clearml123!

Since with argo i can pass them as params

I also see these logs:
bash

/root/entrypoint.sh: line 28: /root/clearml.conf: Read-only file system

This indicates that the container's filesystem is mounted as read-only , preventing the agent from writing its configuration file.

From

podSecurityContext:
  readOnlyRootFilesystem: true  # This causes the issue

PodSecurityPolicies
Security Context Constraints (OpenShift)
Admission controllers enforcing read-only filesystems

parameters:
      - name: namespace
        value: clearml-prod
      - name: node-ip
        value: "192.168.70.211"
      - name: force-cleanup
        value: "false"
      - name: install-server
        value: "true"
      - name: install-agent
        value: "true"
      - name: install-serving
        value: "true"
      - name: diagnose-only
        value: "false"
      - name: storage-class
        value: openebs-hostpath
      - name: helm-timeout
        value: 900s
      - name: clearml-access-key
        value: CLEARML8AGENT9KEY1234567890ABCD
      - name: clearml-secret-key
        value: CLEARML-AGENT-SECRET-1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ123456
      - name: admin-password
        value: clearml123!

Hi @<1811208768843681792:profile|BraveGrasshopper38> , following up on your last message, are you running in an OpenShift k8s cluster?

So CLEARML8AGENT9KEY1234567890ABCD is the actual real value you are using?

Hi! Im using just a plain Kubernetes cluster (kubeadm) running on Proxmox VM, and im using Argo to deploy the helm, in order to standarize it Let me know if you need any more details!

Yeah i know.. thats what i did for the github implementation, but for this i need them to be generated on the fly or via CLI that i can use argo to create if thats possible

The value field is a default argo falls back into if i dont provide any

I have separed the most crutial part. Its a container that runs the standard helm commands

example:
....
cat > /tmp/server-values.yaml <<EOF
global:
defaultStorageClass: $STORAGE_CLASS

        apiserver:

...
helm install clearml clearml/clearml
--namespace "$NS"
--values /tmp/server-values.yaml
--wait
--timeout "$TMO"

...

helm install clearml-agent clearml/clearml-agent
--namespace "$NS"
--values /tmp/simple-agent-values.yaml
--wait
--timeout 300s

these are the values :

clearml:
agentk8sglueKey: $ACCESS_KEY
agentk8sglueSecret: $SECRET_KEY
clearmlConfig: |-
api {
web_server: http://$NODE_IP:30080
api_server: http://$NODE_IP:30008
files_server: http://$NODE_IP:30081
credentials {
"access_key" = "$ACCESS_KEY"
"secret_key" = "$SECRET_KEY"
}
}

        agentk8sglue:
          # Try newer image version to fix Python 3.6 regex issue
          image:
            repository: allegroai/clearml-agent-k8s-base
            tag: "1.25-1"
            pullPolicy: Always

          apiServerUrlReference: "http://$NODE_IP:30008"
          fileServerUrlReference: "http://$NODE_IP:30081"
          webServerUrlReference: "http://$NODE_IP:30080"
          clearmlcheckCertificate: false
          queue: default
          createQueueIfNotExists: true

          # Keep resources minimal for testing
          resources:
            limits:
              cpu: 500m
              memory: 1Gi
            requests:
              cpu: 100m
              memory: 256Mi

        sessions:
          svcType: NodePort
          externalIP: $NODE_IP
          startingPort: 30100
          maxServices: 5

It's a bit hard for me to provide support here with the additional layer of Argo.
I assume the server is working fine and you can open the clearml UI and log in, right? If yes, would it be possible to extract the Agent part only, out of Argo, and proceed installing it through standard helm?

Please replace those credentials on the Agent and try upgrading the helm release

Hey! @<1729671499981262848:profile|CooperativeKitten94> Is there any tips you can give me on this?

It seems like the most recent version supported for kubernetes is clearml-agent==1.9.2?

thanks again!