Install Kubeflow anywhere
Create and train machine learning models on your laptop, in your data centre, or in the cloud.
How to deploy Kubeflow
If you already have Ubuntu or another Linux, the following instructions are all you need. However, if you are on Windows or Mac, consider using Multipass to easily create an Ubuntu VM to work with.
For a more detailed guide, consider following the Deploy Kubeflow on Ubuntu, Windows and macOS tutorial, or follow the video tutorial below:
The following steps assume you want to install MicroK8s as your Kubernetes cluster.
In order to install kubeflow you will need an Ubuntu machine with 16Gb of RAM and 50Gb of free disk.
MicroK8s can be installed with one command.
You can enable features on your Kubernetes cluster with the following commands:
If you have a GPU, run (optional):
Run the following command to enable Kubeflow.
This will take a few minutes.
Access Kubeflow dashboard
After installing kubeflow, an IP address and credentials will be prompted on your terminal.
If you installed Microk8s on your local host, you simply need to access the IP address provided on your browser:
https://[kubeflow dashboard IP].
To get more information on this install process, including screenshots of the process, please visit the Deploy Kubeflow on Ubuntu, Windows and macOS.
More recommended reading:
- Kubeflow - the main Kubeflow website
- Kubeflow samples - several examples to help you get started with leveraging Kubeflow
- Kubeflow pipelines - use or create standard workflows for your models, automating tasks from training to production
- Kubeflow fairing - interact with Kubeflow through Python code
- TensorFlow - open source library to help you develop and train ML models
- TensorFlow: CNN benchmarks - high-performance benchmarks
For tailored Enterprise training and assistance, please consider our AI Consulting and Delivery services.
Learn more about AI/ML and Kubeflow
A detailed look into the AI and ML landscape, how to deploy your first model and more.
Articles from across the web on getting started with AI and Kubeflow in your workplace.
Examine the fundamentals of a successful AI project that helps your organisation achieve their AI ambitions.