Configure GKE for ML Diagnostics

If you are using Google Kubernetes Engine (GKE) for your ML workload, use this guide to configure your GKE cluster and install the required GKE artifacts. The GKE requirements are the same regardless of whether you use the ML Diagnostics gcloud CLI and trigger on-demand profiling from the CLI, or use the ML Diagnostics SDK with your workload and deploy on GKE.

If you are using a version of GKE that is later than 1.35.0-gke.3065000, you can set up GKE cluster for ML Diagnostics with a single gcloud CLI command. For more information, see Set up with gcloud CLI.

For GKE versions prior to 1.35.0-gke.3065000, you need to manually configure the GKE cluster to install the cert-manager, injection-webhook, and connection-operator artifacts. For more information, see Manual installation.

Set up with gcloud CLI

For GKE versions later than 1.35.0-gke.3065000, use one of the following gcloud CLI commands to deploy the required ML Diagnostics components into your GKE cluster.

For new GKE clusters:

gcloud container clusters create CLUSTER_NAME --enable-managed-mldiagnostics

For existing GKE clusters:

gcloud container clusters update CLUSTER_NAME --enable-managed-mldiagnostics

To disable ML Diagnostics, use the following:

gcloud container clusters update CLUSTER_NAME --no-enable-managed-mldiagnostics

You can also enable the gcloud CLI commands through the GKE Google Cloud console:

• For new GKE clusters, go to Feature Manager > Managed Machine Learning Diagnostics.

  Go to GKE Managed Machine Learning Diagnostics

• For existing GKE clusters, go to Clusters, select the name of your cluster, go to Edit, and edit Managed Machine Learning Diagnostics under Features.

For more information on gcloud CLI commands to set up a GKE cluster for ML Diagnostics, refer to the enable-managed-mldiagnostics flag in the following API reference pages:

• container clusters update
• container clusters create

Manual installation

For GKE versions prior to 1.35.0-gke.3065000, you need to manually configure the GKE cluster to install the following:

• cert-manager: A prerequisite for the injection-webhook.
• injection-webhook: Provides the SDK with the required metadata. It supports common ML Kubernetes workloads, like JobSet,RayJob, and LeaderWorkerSet.
• connection-operator: For on-demand profiling on GKE. Deploying connection-operator along with injection-webhook into the GKE cluster will initialize profiling requests to target pods with profiling servers running when you trigger on-demand capture.

For more information on setting up for Google Kubernetes Engine, see Configure Google Kubernetes Engine cluster.

Cert-manager

cert-manager acts as the certificate controller for your cluster, ensuring that your applications are secure and that your certificates never unintentionally expire.

Use Helm to install the following:

helm repo add jetstack https://charts.jetstack.io
helm repo update

helm install \
  cert-manager jetstack/cert-manager \
  --namespace cert-manager \
  --create-namespace \
  --version v1.13.0 \
  --set installCRDs=true \
  --set global.leaderElection.namespace=cert-manager \
  --timeout 10m

Injection-webhook

injection-webhook passes metadata into the SDK. Use helm upgrade to update to the latest version of injection-webhook to ensure that there is no downtime.

Use Helm to install the following:

helm install mldiagnostics-injection-webhook \
   --namespace=gke-mldiagnostics \
   --create-namespace \
oci://us-docker.pkg.dev/ai-on-gke/mldiagnostics-webhook-and-operator-helm/mldiagnostics-injection-webhook

Connection-operator

connection-operator enables on-demand profiling on GKE. Use the following table to find the correct mldiagnostics-connection-operator version:

Use Helm to install the required version:

helm install mldiagnostics-connection-operator \
   --namespace=gke-mldiagnostics \
   --create-namespace \
   --version VERSION \
oci://us-docker.pkg.dev/ai-on-gke/mldiagnostics-webhook-and-operator-helm/mldiagnostics-connection-operator

Label workload

To trigger the injection-webhook to inject metadata into pods, you need to label either the workload or its namespace with managed-mldiagnostics-gke=true before deploying the workload:

• Label a workload. Label a Jobset, LWS, or RayJob workload, which will enable the webhook for that specific workload. The following is an example for a JobSet workload:

  apiVersion: jobset.x-k8s.io/v1alpha2
  kind: JobSet
  metadata:
    name: single-host-tpu-v3-jobset2
    namespace: default
    labels:
      managed-mldiagnostics-gke: "true"
  

• Label a namespace. This will enable the webhook for all Jobset, LWS, and RayJob workloads within that namespace.

  kubectl create namespace ai-workloads
  kubectl label namespace ai-workloads managed-mldiagnostics-gke=true