Scaling AI Workflows with Kubeflow on Kubernetes

4 min readOct 16, 2024

Introduction

Artificial intelligence (AI) and machine learning (ML) have revolutionized industries, driving innovation and efficiency at a very fast pace. However, scaling and managing machine learning workflows in production can be challenging. That’s where Kubeflow comes in. Kubeflow is an open-source platform designed to make it easy to develop, deploy, and manage scalable machine learning (ML) workflows on Kubernetes. Its core purpose is to enable organizations to seamlessly orchestrate, automate, and deploy ML models on cloud-native infrastructure.

In this post, we will explore the architecture of Kubeflow, dive into an easy-to-understand example of how to deploy AI workflows on Kubernetes using Kubeflow, and show you how this powerful platform can transform how your team builds and manages AI models.

Architecture

At the heart of Kubeflow’s architecture is its tight integration with Kubernetes, which serves as a scalable foundation for running containerized applications. Kubeflow provides a set of microservices, each with its own specific function, enabling the creation of end-to-end machine learning pipelines.

Key Components of Kubeflow:

Kubeflow Pipelines: This is the core of Kubeflow, enabling the orchestration of ML workflows, from data preprocessing and training to deployment and monitoring.
Katib: Kubeflow’s hyperparameter tuning framework, helping you automate the process of finding the optimal configuration for your model.
KFServing: Designed for deploying, scaling, and managing machine learning models in production.
TFJob and PyTorchJob: Native Kubernetes jobs for running TensorFlow and PyTorch workloads, streamlining the training and deployment of models in a Kubernetes-native way.
Kubeflow Notebooks: A Jupyter notebook integration that allows data scientists to create and run their experiments interactively.

Each of these components is containerized, running as pods in a Kubernetes cluster. Kubernetes’ underlying infrastructure allows for easy scaling, resource management, and robust orchestration of these services, making it ideal for AI workflows.

Kubeflow builds on Kubernetes as a system for deploying, scaling, and managing AI/ML infrastructure.

How Kubeflow Works

Kubeflow is built on the principle of Composability, Portability and Scalability. It allows you to create reusable, scalable workflows that can be fine-tuned to meet the specific needs of your team. Whether you’re training a deep learning model or deploying an already-trained model to serve predictions, Kubeflow ensures that each step of the ML pipeline is modular and scalable.

A Simple Example to Get Started

Let’s break down how to create a machine learning workflow using Kubeflow with a simple example: training and deploying a TensorFlow model.

Prerequisites:

Installation of Kubernetes
Installation of Kubeflow

Step 1: Setting up a Training Pipeline with Kubeflow Pipelines Kubeflow Pipelines allow you to create a directed acyclic graph (DAG) representing the steps of your machine learning workflow. Below is a Python example that creates a basic pipeline:

import kfp
from kfp import dsl
@dsl.pipeline(
   name='TensorFlow Training Pipeline',
   description='An example pipeline to train a TensorFlow model'
)
def tensorflow_train_pipeline():
    # Define steps in the pipeline
    train_step = dsl.ContainerOp(
        name='Train Model',
        image='gcr.io/my-project/tensorflow:2.3.0',
        command=['python', 'train.py'],
        arguments=[]
    )
    
    deploy_step = dsl.ContainerOp(
        name='Deploy Model',
        image='gcr.io/my-project/tensorflow:2.3.0',
        command=['python', 'serve.py'],
        arguments=[],
        # Wait for training to finish before deploying
        after=train_step
    )

This pipeline defines two steps: the training step (train_step) and the deployment step (deploy_step). Kubeflow Pipelines will manage these steps, ensuring that training happens before deployment.

Step 2: Deploying the Model with KFServing Once the model is trained, we can deploy it using KFServing, a Kubeflow component that simplifies the process of serving machine learning models on Kubernetes.

Here’s a simple manifest for deploying a TensorFlow model with KFServing:

apiVersion: serving.kubeflow.org/v1beta1
kind: InferenceService
metadata:
  name: tensorflow-inference
spec:
  predictor:
    tensorflow:
      storageUri: "gs://my-model-bucket/model"
      resources:
        requests:
          memory: 2Gi
          cpu: 1

With this configuration, KFServing will automatically handle the serving of the TensorFlow model, ensuring it is easily accessible for predictions. It can also scale up or down based on incoming traffic, ensuring you only use the necessary compute resources.

Conclusion

Kubeflow provides an incredibly powerful platform for orchestrating and managing machine learning workflows on Kubernetes. It simplifies the process of scaling AI/ML pipelines from research and development to production, with components like Kubeflow Pipelines, Katib, and KFServing streamlining each phase of the machine learning lifecycle.

By utilizing the flexibility and scalability of Kubernetes, Kubeflow empowers teams to build more robust, scalable, and efficient AI workflows, making it a go-to solution for organizations looking to advance their AI initiatives.