Skip to content

Latest commit

 

History

History
179 lines (135 loc) · 7.16 KB

README.md

File metadata and controls

179 lines (135 loc) · 7.16 KB
Raw

TrustyAI Kubernetes Operator

Controller Tests YAML lint Go Report Card

Overview

The TrustyAI Kubernetes Operator aims at simplifying the deployment and management of the TrustyAI service on Kubernetes and OpenShift clusters by watching for custom resources of kind TrustyAIService in the trustyai.opendatahub.io API group and manages deployments, services, and optionally, routes and ServiceMonitors corresponding to these resources.

The operator ensures the service is properly configured, is discoverable by Prometheus for metrics scraping (on both Kubernetes and OpenShift), and is accessible via a Route on OpenShift.

Prerequisites

  • Kubernetes cluster v1.19+ or OpenShift cluster v4.6+
  • kubectl v1.19+ or oc client v4.6+

Installation using pre-built Operator image

This operator is available as an image on Quay.io. To deploy it on your cluster:

  1. Install the Custom Resource Definition (CRD):

    Apply the CRD to your cluster (replace the URL with the relevant one, if using another repository):

    kubectl apply -f https://raw.githubusercontent.com/trustyai-explainability/trustyai-service-operator/main/config/crd/bases/trustyai.opendatahub.io_trustyaiservices.yaml

  2. Deploy the Operator:

    Apply the following Kubernetes manifest to deploy the operator:

    apiVersion: apps/v1
kind: Deployment
metadata:
  name: trustyai-operator
  namespace: trustyai-operator-system
spec:
  replicas: 1
  selector:
    matchLabels:
      control-plane: trustyai-operator
  template:
    metadata:
      labels:
        control-plane: trustyai-operator
    spec:
      containers:
        - name: trustyai-operator
          image: quay.io/trustyai/trustyai-service-operator:latest
          command:
            - /manager
          resources:
            limits:
              cpu: 100m
              memory: 30Mi
            requests:
              cpu: 100m
              memory: 20Mi

    or run

    kubectl apply -f https://raw.githubusercontent.com/trustyai-explainability/trustyai-service-operator/main/artifacts/examples/deploy-operator.yaml

Usage

Once the operator is installed, you can create TrustyAIService resources, and the operator will create corresponding TrustyAI deployments, services, and (on OpenShift) routes.

Here's an example TrustyAIService manifest:

apiVersion: trustyai.opendatahub.io/v1alpha1
kind: TrustyAIService
metadata:
  name: trustyai-service-example
spec:
  storage:
    format: "PVC"
    folder: "/inputs"
    size: "1Gi"
  data:
    filename: "data.csv"
    format: "CSV"
  metrics:
    schedule: "5s"
    batchSize: 5000 # Optional, defaults to 5000

You can apply this manifest with

kubectl apply -f <file-name.yaml> -n $NAMESPACE

to create a service, where $NAMESPACE is the namespace where you want to deploy it.

Additionally, in that namespace:

  • a ServiceMonitor will be created to allow Prometheus to scrape metrics from the service.
  • (if on OpenShift) a Route will be created to allow external access to the service.

Custom Image Configuration using ConfigMap

You can specify a custom TrustyAI-service image via adding parameters to the TrustyAI-Operator KFDef, for example:

apiVersion: kfdef.apps.kubeflow.org/v1
kind: KfDef
metadata:
  name: trustyai-service-operator
  namespace: opendatahub
spec:
  applications:
  - kustomizeConfig:
      repoRef:
        name: manifests
        path: config
      parameters:
         - name: trustyaiServiceImage
           value: NEW_IMAGE_NAME
    name: trustyai-service-operator
  repos:
  - name: manifests
    uri: https://github.com/trustyai-explainability/trustyai-service-operator/tarball/main
  version: v1.0.0

If these parameters are unspecified, the default image and tag will be used.

If you'd like to change the service image/tag after deploying the operator, simply change the parameters in the KFDef. Any TrustyAI service deployed subsequently will use the new image and tag.

TrustyAIService Status Updates

The TrustyAIService custom resource tracks the availability of InferenceServices and PersistentVolumeClaims (PVCs) through its status field. Below are the status types and reasons that are available:

InferenceService Status

Status Type Status Reason Description
InferenceServicesPresent InferenceServicesNotFound InferenceServices were not found.
InferenceServicesPresent InferenceServicesFound InferenceServices were found.

PersistentVolumeClaim (PVCs) Status

Status Type Status Reason Description
PVCAvailable PVCNotFound PersistentVolumeClaim not found.
PVCAvailable PVCFound PersistentVolumeClaim found.

Database Status

Status Type Status Reason Description
DBAvailable DBCredentialsNotFound Database credentials secret not found
DBAvailable DBCredentialsError Database credentials malformed (e.g. missing key)
DBAvailable DBConnectionError Service error connecting to the database
DBAvailable DBAvailable Successfully connected to the database

Status Behavior

  • If a PVC is not available, the Ready status of TrustyAIService will be set to False.
  • If on database mode, any DBAvailable reason other than DBAvailable will set the TrustyAIService to Not Ready
  • However, if InferenceServices are not found, the Ready status of TrustyAIService will not be affected, i.e., it is Ready by all other conditions, it will remain so.

Contributing

Please see the CONTRIBUTING.md file for more details on how to contribute to this project.

License

This project is licensed under the Apache License Version 2.0 - see the LICENSE file for details.