How to build your own Ubuntu Core image and other documentation add-on

David Callé

on 21 November 2016

This article was last updated 9 years ago.



2 weeks since the launch of Ubuntu Core 16! Many of you have been asking for help porting Ubuntu Core to new boards, chips or simply building your own images for supported boards like the Raspberry Pi. Wait no more!! Here is the first piece of documentation to help you build an Ubuntu Core image for your preferred board.

New documentation

The new Board enablement documentation gives a set of instructions for advanced users to help them enable new boards and build images, including kernel building, gadget snap composition, signature generation and model assertion creation.

The latest new interfaces have been added to the core interfaces reference:

  • `raw-usb` allowing access to connected USB devices
  • `lxd`, allowing usage of the LXD API through the LXD snap

Updates

The Security and sandboxing overview has been augmented with debugging guidance to investigate which authorizations your apps need to request to work within security confinement.

Improved looks

The doc interface also got a few enhancements, with an in-page navigation menu on the right hand side which will help navigate through long pages (and yes there are a few long pages 🙂 .

Talk to us today

Interested in running Ubuntu in your organisation?

Newsletter signup

Get the latest Ubuntu news and updates in your inbox.

By submitting this form, I confirm that I have read and agree to Canonical's Privacy Policy.

Related posts

Canonical releases Ubuntu 26.04 LTS Resolute Raccoon

The 11th long-term supported release of Ubuntu delivers deep silicon optimization and state-of-the-art security for enterprise workloads.

From Jammy to Resolute: how Ubuntu’s toolchains have evolved

We cover new toolchain versions, devpacks and workflows that improve the developer experience. The evolution of Ubuntu’s toolchains story goes beyond just...

Hybrid search and reranking: a deeper look at RAG

Many of us are familiar with the retrieval augmented generative AI (RAG) pattern for building agentic AI applications – like digital concierges, frontline...