Troubleshoot Azure Machine Learning extension

Article
08/28/2024

In this article, you learn how to troubleshoot common problems you may encounter with Azure Machine Learning extension deployment in your AKS or Arc-enabled Kubernetes.

How is Azure Machine Learning extension installed

Azure Machine Learning extension is released as a helm chart and installed by Helm V3. All components of Azure Machine Learning extension are installed in azureml namespace. You can use the following commands to check the extension status.

# get the extension status
az k8s-extension show --name <extension-name>

# check status of all pods of Azure Machine Learning extension
kubectl get pod -n azureml

# get events of the extension
kubectl get events -n azureml --sort-by='.lastTimestamp'

Troubleshoot Azure Machine Learning extension deployment error

Error: can't reuse a name that is still in use

This error means the extension name you specified already exists. If the name is used by Azure Machine Learning extension, you need to wait for about an hour and try again. If the name is used by other helm charts, you need to use another name. Run helm list -Aa to list all helm charts in your cluster.

Error: earlier operation for the helm chart is still in progress

You need to wait for about an hour and try again after the unknown operation is completed.

Error: unable to create new content in namespace azureml because it's being terminated

This error happens when an uninstallation operation isn't finished and another installation operation is triggered. You can run az k8s-extension show command to check the provisioning status of the extension and make sure the extension has been uninstalled before taking other actions.

Error: failed in download the Chart path not found

This error happens when you specify a wrong extension version. You need to make sure the specified version exists. If you want to use the latest version, you don't need to specify --version .

Error: can't be imported into the current release: invalid ownership metadata

This error means there's a conflict between existing cluster resources and Azure Machine Learning extension. A full error message could be like the following text:

CustomResourceDefinition "jobs.batch.volcano.sh" in namespace "" exists and cannot be imported into the current release: invalid ownership metadata; label validation error: missing key "app.kubernetes.io/managed-by": must be set to "Helm"; annotation validation error: missing key "meta.helm.sh/release-name": must be set to "amlarc-extension"; annotation validation error: missing key "meta.helm.sh/release-namespace": must be set to "azureml"

Use the following steps to mitigate the issue.

Check who owns the problematic resources and if the resource can be deleted or modified.
If the resource is used only by Azure Machine Learning extension and can be deleted, you can manually add labels to mitigate the issue. Taking the previous error message as an example, you can run commands as follows,
```
kubectl label crd jobs.batch.volcano.sh "app.kubernetes.io/managed-by=Helm" 
kubectl annotate crd jobs.batch.volcano.sh "meta.helm.sh/release-namespace=azureml" "meta.helm.sh/release-name=<extension-name>"
```
By setting the labels and annotations to the resource, it means helm is managing the resource that is owned by Azure Machine Learning extension.
When the resource is also used by other components in your cluster and can't be modified. Refer to deploy Azure Machine Learning extension to see if there's a configuration setting to disable the conflict resource.

HealthCheck of extension

When the installation failed and didn't hit any of the above error messages, you can use the built-in health check job to make a comprehensive check on the extension. Azure machine learning extension contains a HealthCheck job to precheck your cluster readiness when you try to install, update or delete the extension. The HealthCheck job outputs a report, which is saved in a configmap named arcml-healthcheck in azureml namespace. The error codes and possible solutions for the report are listed in Error Code of HealthCheck.

Run this command to get the HealthCheck report,

kubectl describe configmap -n azureml arcml-healthcheck

The health check is triggered whenever you install, update or delete the extension. The health check report is structured with several parts pre-install, pre-rollback, pre-upgrade and pre-delete.

If the extension is installed failed, you should look into pre-install and pre-delete.
If the extension is updated failed, you should look into pre-upgrade and pre-rollback.
If the extension is deleted failed, you should look into pre-delete.

When you request support, we recommend that you run the following command and send thehealthcheck.logs file to us, as it can facilitate us to better locate the problem.

kubectl logs healthcheck -n azureml

Error Code of HealthCheck

This table shows how to troubleshoot the error codes returned by the HealthCheck report.

Error Code	Error Message	Description
E40001	LOAD_BALANCER_NOT_SUPPORT	Load balancer isn't supported in your cluster. You need to configure the load balancer in your cluster or consider setting `inferenceRouterServiceType` to `nodePort` or `clusterIP`.
E40002	INSUFFICIENT_NODE	You have enabled `inferenceRouterHA` that requires at least three nodes in your cluster. Disable the HA if you have fewer than three nodes.
E40003	INTERNAL_LOAD_BALANCER_NOT_SUPPORT	Currently, only AKS support the internal load balancer, and we only support the `azure` type. Don't set `internalLoadBalancerProvider` if you don't have an AKS cluster.
E40007	INVALID_SSL_SETTING	The SSL key or certificate isn't valid. The CNAME should be compatible with the certificate.
E45002	PROMETHEUS_CONFLICT	The Prometheus Operator installed is conflict with your existing Prometheus Operator. For more information, see Prometheus operator
E45003	BAD_NETWORK_CONNECTIVITY	You need to meet network-requirements.
E45004	AZUREML_FE_ROLE_CONFLICT	Azure Machine Learning extension isn't supported in the legacy AKS. To install Azure Machine Learning extension, you need to delete the legacy azureml-fe components.
E45005	AZUREML_FE_DEPLOYMENT_CONFLICT	Azure Machine Learning extension isn't supported in the legacy AKS. To install Azure Machine Learning extension, you need to run the command below this form to delete the legacy azureml-fe components, more detail you can referto here.

Commands to delete the legacy azureml-fe components in the AKS cluster:

kubectl delete sa azureml-fe
kubectl delete clusterrole azureml-fe-role
kubectl delete clusterrolebinding azureml-fe-binding
kubectl delete svc azureml-fe
kubectl delete svc azureml-fe-int-http
kubectl delete deploy azureml-fe
kubectl delete secret azuremlfessl
kubectl delete cm azuremlfeconfig

Open source components integration

Azure Machine Learning extension uses some open source components, including Prometheus Operator, Volcano Scheduler, and DCGM exporter. If the Kubernetes cluster already has some of them installed, you can read following sections to integrate your existing components with Azure Machine Learning extension.

Prometheus operator

Prometheus operator is an open source framework to help build metric monitoring system in kubernetes. Azure Machine Learning extension also utilizes Prometheus operator to help monitor resource utilization of jobs.

If the cluster has the Prometheus operator installed by other service, you can specify installPromOp=false to disable the Prometheus operator in Azure Machine Learning extension to avoid a conflict between two Prometheus operators. In this case, the existing prometheus operator manages all Prometheus instances. To make sure Prometheus works properly, the following things need to be paid attention to when you disable prometheus operator in Azure Machine Learning extension.

Check if prometheus in azureml namespace is managed by the Prometheus operator. In some scenarios, prometheus operator is set to only monitor some specific namespaces. If so, make sure azureml namespace is in the allowlist. For more information, see command flags.
Check if kubelet-service is enabled in prometheus operator. Kubelet-service contains all the endpoints of kubelet. For more information, see command flags. And also need to make sure that kubelet-service has a labelk8s-app=kubelet.

Create ServiceMonitor for kubelet-service. Run the following command with variables replaced:

cat << EOF | kubectl apply -f -
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: prom-kubelet
  namespace: azureml
  labels:
    release: "<extension-name>"     # Please replace to your Azure Machine Learning extension name
spec:
  endpoints:
  - port: https-metrics
    scheme: https
    path: /metrics/cadvisor
    honorLabels: true
    tlsConfig:
      caFile: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      insecureSkipVerify: true
    bearerTokenFile: /var/run/secrets/kubernetes.io/serviceaccount/token
    relabelings:
    - sourceLabels:
      - __metrics_path__
      targetLabel: metrics_path
  jobLabel: k8s-app
  namespaceSelector:
    matchNames:
    - "<namespace-of-your-kubelet-service>"  # Please change this to the same namespace of your kubelet-service
  selector:
    matchLabels:
      k8s-app: kubelet    # Please make sure your kubelet-service has a label named k8s-app and it's value is kubelet

EOF

DCGM exporter

Dcgm-exporter is the official tool recommended by NVIDIA for collecting GPU metrics. We've integrated it into Azure Machine Learning extension. But, by default, dcgm-exporter isn't enabled, and no GPU metrics are collected. You can specify installDcgmExporter flag to true to enable it. As it's NVIDIA's official tool, you may already have it installed in your GPU cluster. If so, you can set installDcgmExporter to false and follow the steps to integrate your dcgm-exporter into Azure Machine Learning extension. Another thing to note is that dcgm-exporter allows user to config which metrics to expose. For Azure Machine Learning extension, make sure DCGM_FI_DEV_GPU_UTIL, DCGM_FI_DEV_FB_FREE and DCGM_FI_DEV_FB_USED metrics are exposed.

Make sure you have Aureml extension and dcgm-exporter installed successfully. Dcgm-exporter can be installed by Dcgm-exporter helm chart or Gpu-operator helm chart

Check if there's a service for dcgm-exporter. If it doesn't exist or you don't know how to check, run the following command to create one.

cat << EOF | kubectl apply -f -
apiVersion: v1
kind: Service
metadata:
  name: dcgm-exporter-service
  namespace: "<namespace-of-your-dcgm-exporter>" # Please change this to the same namespace of your dcgm-exporter
  labels:
    app.kubernetes.io/name: dcgm-exporter
    app.kubernetes.io/instance: "<extension-name>" # Please replace to your Azure Machine Learning extension name
    app.kubernetes.io/component: "dcgm-exporter"
  annotations:
    prometheus.io/scrape: 'true'
spec:
  type: "ClusterIP"
  ports:
  - name: "metrics"
    port: 9400  # Please replace to the correct port of your dcgm-exporter. It's 9400 by default
    targetPort: 9400  # Please replace to the correct port of your dcgm-exporter. It's 9400 by default
    protocol: TCP
  selector:
    app.kubernetes.io/name: dcgm-exporter  # Those two labels are used to select dcgm-exporter pods. You can change them according to the actual label on the service
    app.kubernetes.io/instance: "<dcgm-exporter-helm-chart-name>" # Please replace to the helm chart name of dcgm-exporter
EOF

Check if the service in previous step is set correctly

kubectl -n <namespace-of-your-dcgm-exporter> port-forward service/dcgm-exporter-service 9400:9400
# run this command in a separate terminal. You will get a lot of dcgm metrics with this command.
curl http://127.0.0.1:9400/metrics

Set up ServiceMonitor to expose dcgm-exporter service to Azure Machine Learning extension. Run the following command and it takes effect in a few minutes.

cat << EOF | kubectl apply -f -
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: dcgm-exporter-monitor
  namespace: azureml
  labels:
    app.kubernetes.io/name: dcgm-exporter
    release: "<extension-name>"   # Please replace to your Azure Machine Learning extension name
    app.kubernetes.io/component: "dcgm-exporter"
spec:
  selector:
    matchLabels:
      app.kubernetes.io/name: dcgm-exporter
      app.kubernetes.io/instance: "<extension-name>"   # Please replace to your Azure Machine Learning extension name
      app.kubernetes.io/component: "dcgm-exporter"
  namespaceSelector:
    matchNames:
    - "<namespace-of-your-dcgm-exporter>"  # Please change this to the same namespace of your dcgm-exporter
  endpoints:
  - port: "metrics"
    path: "/metrics"
EOF

Volcano Scheduler

If your cluster already has the volcano suite installed, you can set installVolcano=false, so the extension won't install the volcano scheduler. Volcano scheduler and volcano controller are required for training job submission and scheduling.

The volcano scheduler config used by Azure Machine Learning extension is:

volcano-scheduler.conf: |
    actions: "enqueue, allocate, backfill"
    tiers:
    - plugins:
        - name: task-topology
        - name: priority
        - name: gang
        - name: conformance
    - plugins:
        - name: overcommit
        - name: drf
        - name: predicates
        - name: proportion
        - name: nodeorder
        - name: binpack

You need to use this same config settings, and you need to disable job/validate webhook in the volcano admission if your volcano version is lower than 1.6, so that Azure Machine Learning training workloads can perform properly.

Volcano scheduler integration supporting cluster autoscaler

As discussed in this thread , the gang plugin is not working well with the cluster autoscaler(CA) and also the node autoscaler in AKS.

If you use the volcano that comes with the Azure Machine Learning extension via setting installVolcano=true, the extension has a scheduler config by default, which configures the gang plugin to prevent job deadlock. Therefore, the cluster autoscaler(CA) in AKS cluster won't be supported with the volcano installed by extension.

For this case, if you prefer the AKS cluster autoscaler could work normally, you can configure this volcanoScheduler.schedulerConfigMap parameter through updating extension, and specify a custom config of no gang volcano scheduler to it, for example:

volcano-scheduler.conf: |
    actions: "enqueue, allocate, backfill"
    tiers:
    - plugins:
      - name: sla 
        arguments:
        sla-waiting-time: 1m
    - plugins:
      - name: conformance
    - plugins:
        - name: drf
        - name: predicates
        - name: proportion
        - name: nodeorder
        - name: binpack

To use this config in your AKS cluster, you need to follow the following steps:

Create a configmap file with the above config in the azureml namespace. This namespace will generally be created when you install the Azure Machine Learning extension.

Set volcanoScheduler.schedulerConfigMap=<configmap name> in the extension config to apply this configmap. And you need to skip the resource validation when installing the extension by configuring amloperator.skipResourceValidation=true. For example:

az k8s-extension update --name <extension-name> --config volcanoScheduler.schedulerConfigMap=<configmap name> amloperator.skipResourceValidation=true --cluster-type managedClusters --cluster-name <your-AKS-cluster-name> --resource-group <your-RG-name>

Note

Since the gang plugin is removed, there's potential that the deadlock happens when volcano schedules the job.

To avoid this situation, you can use same instance type across the jobs.

Using a scheduler configuration other than the default provided by the Azure Machine Learning extension may not be fully supported. Proceed with caution.

Note that you need to disable job/validate webhook in the volcano admission if your volcano version is lower than 1.6.

Ingress Nginx controller

The Azure Machine Learning extension installation comes with an ingress nginx controller class as k8s.io/ingress-nginx by default. If you already have an ingress nginx controller in your cluster, you need to use a different controller class to avoid installation failure.

You have two options:

Change your existing controller class to something other than k8s.io/ingress-nginx.
Create or update our Azure Machine Learning extension with a custom controller class that is different from yours by following the following examples.

For example, to create the extension with a custom controller class:

az ml extension create --config nginxIngress.controller="k8s.io/amlarc-ingress-nginx"

To update the extension with a custom controller class:

az ml extension update --config nginxIngress.controller="k8s.io/amlarc-ingress-nginx"

Nginx ingress controller installed with the Azure Machine Learning extension crashes due to out-of-memory (OOM) errors

Symptom

The nginx ingress controller installed with the Azure Machine Learning extension crashes due to out-of-memory (OOM) errors even when there is no workload. The controller logs do not show any useful information to diagnose the problem.

Possible Cause

This issue may occur if the nginx ingress controller runs on a node with many CPUs. By default, the nginx ingress controller spawns worker processes according to the number of CPUs, which may consume more resources and cause OOM errors on nodes with more CPUs. This is a known issue reported on GitHub

Resolution

To resolve this issue, you can:

Adjust the number of worker processes by installing the extension with the parameter nginxIngress.controllerConfig.worker-processes=8.
Increase the memory limit by using the parameter nginxIngress.resources.controller.limits.memory=<new limit>.

Ensure to adjust these two parameters according to your specific node specifications and workload requirements to optimize your workloads effectively.

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