Configure inferenceService to Access AWS Services from KServe

Configuration for accessing AWS services for inference services such as pulling images from private ECR and downloading models from S3 bucket.

Access AWS Service from Kserve with IAM Roles for ServiceAccount(IRSA)

  1. Export env values:

    export CLUSTER_NAME="<>"
    export CLUSTER_REGION="<>"
    export PROFILE_NAMESPACE=kubeflow-user-example-com
    export SERVICE_ACCOUNT_NAME=aws-sa
    export ECR_IMAGE_URL="<>"
    # s3://your-s3-bucket/model
    export S3_BUCKET_URL="<>"
  2. Create Service Account with IAM Role using IRSA. The following command attaches both AmazonEC2ContainerRegistryReadOnly and AmazonS3ReadOnlyAccess IAM policies:

    eksctl create iamserviceaccount --name ${SERVICE_ACCOUNT_NAME} --namespace ${PROFILE_NAMESPACE} --cluster ${CLUSTER_NAME} --region ${CLUSTER_REGION} --attach-policy-arn=arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly --attach-policy-arn=arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess  --override-existing-serviceaccounts --approve

    NOTE: You can use ECR (AmazonEC2ContainerRegistryReadOnly) and S3 (AmazonS3ReadOnlyAccess) ReadOnly managed policies. We recommend creating fine grained policy for production usecase.

Deploy models from S3 Bucket

  1. Create Secret:
cat <<EOF > secret.yaml
apiVersion: v1
kind: Secret
  name: aws-secret
  namespace: ${PROFILE_NAMESPACE}
  annotations: "1" ${CLUSTER_REGION}
type: Opaque

kubectl apply -f secret.yaml
  1. Attach secret to IRSA in your profile namespace:
    kubectl patch serviceaccount ${SERVICE_ACCOUNT_NAME} -n ${PROFILE_NAMESPACE} -p '{"secrets": [{"name": "aws-secret"}]}'

Create an InferenceService

  1. Specify the service account in the model server spec :

NOTE: make sure you have workable image in ${ECR_IMAGE_URL}and model in ${S3_BUCKET_URL} for the inferenceService to work. Versioning of model and image must be consistent: eg. you can not use a v1 model then a v2 image.

cat <<EOF > inferenceService.yaml
kind: InferenceService
  name: "sklearn-iris"
  namespace: ${PROFILE_NAMESPACE}
  annotations: "false"
    serviceAccountName: ${SERVICE_ACCOUNT_NAME}
        name: sklearn
      image: ${ECR_IMAGE_URL}
      storageUri: ${S3_BUCKET_URL}

kubectl apply -f inferenceService.yaml
  1. Check the InferenceService status:
kubectl get inferenceservices sklearn-iris -n ${PROFILE_NAMESPACE}
NAME           URL                                                        READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                    AGE
sklearn-iris   True           100                              sklearn-iris-predictor-default-00001   105s