Docker/setup-qemu-action

Just tried again, and still getting the same issue, docker/setup-qemu-action.

But how will you ensure every environment has the right version of Ruby installed and every user knows how to install all of its dependencies? I think there is a simpler method to this madness. This is a tutorial to automatically build and push container images for your project, which will save you time and minimize tedious tasks. In my opinion, this tutorial is the most basic application of the term. I am applying automation to the code since I am familiar with both deployment strategies and application code.

Docker/setup-qemu-action

In this post, we will focus on building multi-platform Docker images, as well as Arm images, in GitHub Actions. Depot launches cloud builders for both Intel and Arm — allowing you to build multi-platform Docker images with native CPUs and instant caching across builds. By default, Docker images are built for the architecture of the machine running the build. If you build an image on an Intel machine, the image will be built for Intel. If you build an image on an Arm machine, the image will be built for Arm. If you want to build an image for a different architecture than the machine you are building on, you can specify the --platform flag during a Docker build. A Docker image can also be built for multiple architectures simultaneously. This produces what is often referred to as a multi-platform or multi-architecture image. If you want to build a multi-platform Docker image for both Intel and Arm, you can specify multiple platforms in the --platform flag. A multi-platform Docker image build triggers two builds, one for each architecture, and produces a single image that supports both platforms. But, to build that image, one of the architectures must be emulated using qemu emulation. Alternatively, there is the option to configure docker buildx build to use multiple builders, one for each platform. This method removes the need to emulate the non-host machine architecture. But, in exchange, you have to run your own native builders.

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The Bioconductor Build System BBS now includes routine package testing on Linux ARM64, but the relatively low frequency of testing means this, if a problem occurs with your package, it can take a while to identify and fix the issue using the build system alone. Ideally such testing would happen automatically whenever you make changes to a package, but providing more rapid feedback than the BBS provides. In this article we build on these previously presented ideas to describe one approach for testing package on Linux ARM64 using a continuous integration environment on GitHub Actions. The first thing to remember when using architecture emulation is that everything works much slower than when running natively - typically by at least an order of magnitude. This influences some of the decisions made during this workflow regarding which containers to use and what we want to cache between workflow steps.

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Docker/setup-qemu-action

Using buildx we can build multi architecture containers , we can use a github action to automatically build it. We are going to need to get the latest tag to be able to properly tag the docker image and to check out the correct version:. Then we will have to setup qemu and buildx.

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Using them in a continuous integration environment allows one to detect and highlight unforeseen issues introduced by changes to a package or the wider R environment. Take a nice thing and then automate it. Could you give it another go? That change to specifying the buildkit version worked great for me. Deployments failing using docker hub image. Thus it will often give a green tick, even if something went wrong, and it is easy to miss an error if just glancing at the step summaries. Red Hat Summit. Last updated: December 13, Note: You could probably achieve a similar result by using the standard Bioconductor container and running R CMD check with the arguments --no-manual and --no-build-vignettes , however I would rather run the complete testing process in case there is problematic code in either the manual page examples or vignette. The combination of faster machines, native CPUs, and instant caching across builds results in faster builds. Deploying to Fly via GitHub Action failing. Iterate and improve.

In today's rapidly evolving software development landscape, the ability to efficiently build and deploy applications across diverse architectures is crucial. Our focus will be on leveraging the capabilities of GitHub Actions, including setup of QEMU for emulation and Docker Buildx for building images, along with caching strategies and security practices for Docker Hub integration.

We use the run option to provide steps similar to the Bioconductor Build System. Here's an example of a GitHub Actions workflow that builds multi-architecture container images for both amd64 and arm64 architectures when a pull request is created or when changes are pushed to the main branch:. I can confirm this workaround worked for me. Depot launches cloud builders for both Intel and Arm — allowing you to build multi-platform Docker images with native CPUs and instant caching across builds. The layer cache can be used from your CI build in GitHub Actions and your local machine when you use depot build. To do this we can use the upload-artifact action. Graphical tool for container and kubernetes development. The arguments and setting used here are representative of the BBS, but one can change them if other testing mechanism are required. This is a tutorial to automatically build and push container images for your project, which will save you time and minimize tedious tasks. If this job executes successfully we should have a cached library containing all the packages required to test the package. This allows us to compile the package manual pages and and PDF vignettes during testing. It looks like sometime earlier today GitHub updated the ubuntu runner which bumped the version of BuildKit from 0. So this entire process only runs if the tests pass. We will create a file. There is one more step to go before we call it done.