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OpenDataHub Installation and Configuration

This directory contains the configuration files for installing OpenDataHub (ODH) with KServe support for the MaaS platform.

Key Features

  • RawDeployment Mode: Direct pod deployments without Knative/Serverless overhead
  • NVIDIA NIM Support: GPU-accelerated inference with NVIDIA Inference Microservices
  • Headless Services: Direct pod-to-pod communication for low latency
  • OpenShift Integration: Uses OpenShift's default ingress and certificates

Installation Methods

Method 1: Automated Installation (Recommended)

Use the provided installation script which handles all steps in the correct order:

# From the project root
./deployment/scripts/installers/install-odh.sh

Method 2: Manual Installation

  1. Install ODH Operator from OperatorHub:

    # Via OpenShift Console:
# 1. Navigate to Operators → OperatorHub
# 2. Search for "OpenDataHub"
# 3. Install with default settings

# Or via CLI:
oc create -f - <<EOF
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: opendatahub-operator
  namespace: openshift-operators
spec:
  channel: fast
  name: opendatahub-operator
  source: community-operators
  sourceNamespace: openshift-marketplace
EOF

  2. Create namespace:

    kubectl create namespace opendatahub

  3. Wait for CRDs to be registered:

    # Wait for the operator to create the CRDs
kubectl wait --for condition=established --timeout=300s \
  crd/dscinitializations.dscinitialization.opendatahub.io \
  crd/datascienceclusters.datasciencecluster.opendatahub.io

  4. Apply the configuration:

    # IMPORTANT: DSCInitialization MUST be created before DataScienceCluster
kubectl apply -f dscinitialisation.yaml

# Wait for DSCInitialization to be ready
kubectl wait --for=jsonpath='{.status.phase}'=Ready \
  dscinitializations.dscinitialization.opendatahub.io/default-dsci \
  -n opendatahub --timeout=300s

# Now create the DataScienceCluster
kubectl apply -f datasciencecluster.yaml

    Or use kustomize:

    kubectl apply -k deployment/components/odh/

Troubleshooting

Error: "dscinitializations.dscinitialization.opendatahub.io not found"

This is the most common error when creating a DataScienceCluster. It occurs when:

  1. The ODH operator is not installed
  2. The DSCInitialization resource hasn't been created yet
  3. The CRDs haven't been registered yet

Solution: Run the fix script:

./deployment/scripts/installers/fix-odh-dsci.sh

This script will:

  • Check if the ODH operator is installed
  • Wait for CRDs to be registered
  • Create the DSCInitialization if missing
  • Provide next steps for creating the DataScienceCluster

Manual Troubleshooting Steps

  1. Check operator status:

    kubectl get csv -n openshift-operators | grep opendatahub
kubectl logs -n openshift-operators deployment/opendatahub-operator-controller-manager

  2. Check CRDs:

    kubectl get crd | grep opendatahub

  3. Check existing resources:

    kubectl get dscinitializations -A
kubectl get datasciencecluster -A

  4. Check pod status:

    kubectl get pods -n opendatahub
kubectl get pods -n kserve

Configuration Details

DSCInitialization

  • Configures the foundational settings for ODH
  • Sets up Service Mesh integration
  • Configures monitoring and trusted CA bundles
  • MUST be created before DataScienceCluster

DataScienceCluster

  • Deploys the actual ODH components
  • Configured for KServe with:
    • RawDeployment mode: No Knative/Serverless overhead
    • NIM support: For NVIDIA GPU inference
    • Headless services: For direct pod communication
    • OpenShift ingress: Native OpenShift routing

Components Status

  • ✅ Enabled: Dashboard, Workbenches, KServe (with NIM)
  • ❌ Disabled: ModelMesh, Pipelines, Ray, Kueue, Model Registry, TrustyAI, Training Operator

Verification

After installation, verify the deployment:

# Check DSCInitialization status
kubectl get dscinitializations -n opendatahub

# Check DataScienceCluster status
kubectl get datasciencecluster -n opendatahub

# Check KServe components
kubectl get pods -n kserve

# Check if InferenceService CRD is available
kubectl get crd inferenceservices.serving.kserve.io

Integration with MaaS

Once ODH is installed with KServe, you can:

  1. Deploy models using KServe InferenceService
  2. Use the MaaS API for model management
  3. Apply rate limiting and authentication policies
  4. Monitor model performance through the ODH dashboard

Additional Resources