Leveraging Tekton for AI Workloads on OpenShift

Synopsis #

In this post, we delve into the application of Tekton, a powerful Kubernetes-native continuous integration and continuous delivery (CI/CD) solution, for managing AI workloads on an OpenShift cluster. We’ll discuss the advantages of Tekton for AI/ML workflows and provide detailed examples and configurations.

Introduction #

Tekton introduces a cloud-native mechanism to define and run CI/CD pipelines, making it a fitting tool for managing AI/ML workloads. From automating model training to deploying models into production, Tekton can streamline your AI/ML workflows on OpenShift.

Tekton Overview #

Tekton provides Kubernetes custom resources to define pipelines, tasks, and runs. A Task is a collection of steps that perform a specific job, like training a model or deploying an application. A Pipeline groups multiple tasks together, and a PipelineRun is an execution of a pipeline.

Tekton for AI/ML Workflows #

Let’s examine how Tekton can streamline AI/ML workflows on OpenShift, with specific examples.

1. Automating Model Training #

You can define a Tekton task to automate the model training process. This task might include steps to pull the training data from a data source, run the training job, and save the trained model to a model registry. Here’s an example configuration:

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: train-model
spec:
  steps:
  - name: pull-data
    image: <your-data-image>
    script: |
            # Pull data from your data source
  - name: train
    image: <your-training-image>
    script: |
            # Run training job
  - name: save-model
    image: <your-model-image>
    script: |
            # Save model to model registry

2. Deploying Models #

A Tekton task can also handle model deployment. This task might include steps to pull the trained model from the model registry, create a SeldonDeployment or a KFServing InferenceService to serve the model, and expose a service to access the model.

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: deploy-model
spec:
  steps:
  - name: pull-model
    image: <your-model-image>
    script: |
            # Pull model from model registry
  - name: deploy
    image: <your-deployment-image>
    script: |
            # Deploy model using Seldon or KFServing
  - name: expose-service
    image: <your-service-image>
    script: |
            # Expose service to access the model

3. CI/CD Pipeline for AI/ML #

You can orchestrate these tasks into a CI/CD pipeline to automate the entire AI/ML workflow. The pipeline can be triggered by events such as a new data arrival or a code update, enabling continuous integration and deployment of your AI/ML workloads.

apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
  name: ai-pipeline
spec:
  tasks:
  - name: train-model
    taskRef:
      name: train-model
  - name:

 deploy-model
    taskRef:
      name: deploy-model
    runAfter:
    - train-model

Conclusion #

Tekton presents a powerful tool for managing AI workloads in an OpenShift cluster. By automating and streamlining AI/ML workflows, Tekton ensures faster, more reliable, and repeatable processes. This ultimately leads to a more efficient and agile machine learning lifecycle.

References #

1. OpenShift Documentation
2. Tekton Documentation
3. Getting started with Tasks | Tekton
4. Kubeflow Pipelines with Tekton
5. Tekton Pipeline Examples
6. Open Data Hub and Tekton