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Arrikto

Getting Started with Kubeflow locally using MiniKF and Vagrant

Jimmy Guerrero
・2 min read

The Kubeflow project was announced back in December 2017 and has since become a very popular machine learning platform with both data scientists and MLOps engineers. If you are new to the Kubeflow ecosystem and community, here’s a quick rundown.

Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. In a nutshell, Kubeflow is the machine learning toolkit for Kubernetes. As such, anywhere you are running Kubernetes, you should also be able to run Kubeflow. Use cases for Kubeflow include:

  • Deploying and managing a complex machine learning system at scale
  • Experimentation with training a machine learning model
  • End to end hybrid and multi-cloud machine learning workloads
  • Tuning the model hyperparameters during training
  • Continuous integration and deployment (CI/CD) for machine learning

Good stuff, huh? Well, if you dig into Kubeflow a little bit, you will quickly discover that it is a collection of distinct projects like Katib, Kale, KFServing, Pipelines and more… with each component providing essential functionality in the workflow. So, now you may be asking yourself, “What’s the easiest way to get started with Kubeflow on my laptop, with the least amount of hassle?”

Kubeflow up and running in just minutes with MiniKF

At Arrikto, we built MiniKF to be hands down the simplest way to get started with Kubeflow. Check out the short video below to see an installation of MiniKF in action.

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In the video we cover the following topics:

  • System requirements
  • Supported operating systems
  • Prerequisites
  • Tips and Tricks
  • Installation

As you can see from the video, getting up and running with Kubeflow via MiniKF is a piece of cake. Try it for yourself.

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