If you’ve read our earlier post you’ll understand how to create an AWS ParallelCluster and adapt the head and compute nodes with custom scripts that you place in an AWS S3 bucket.

Here we discus using the AWS ParallelCluster [ImageBuilder] utility to create a custom AMI image that you can use to create your cluster, useful if you find you’re installing a lot of custom packages that slow down the formation of new compute nodes.

Rather than installing packages and tools every time a cluster node is created (costing valuable minutes) the [ImageBuilder] lets you construct a customised AMI.

Here we’re using the AWS ParallelCluster, an AWS supported open source cluster management tool that helps you to deploy and manage high performance computing clusters in the AWS Cloud. For a background refer to the AWS introduction.

In our earlier post we demonstrated the use of AWS ParallelCluster (v2) for Nextflow. Here we briefly discuss switching from ParallelCluster v2 to v3.

Try using popeye, a Kubernetes Cluster Sanitizer, to help lint objects deployed to Kubernetes to detect misconfigurations, and give you some feedback on compliance with community best practices.

[Actions] are a welcome addition to the GitHub service. They add a free, built-in CI/CD capability similar to [GitLab-CI], [Travis] and others.

What we’ll see in this blog post is a simple pattern to build a container image and push it to [Docker Hub]. What happens is based on whether we’re on a branch, responding to a pull-request, or responding to changes on main.

Here we’re going to explore repetitive project content and one method you can employ to automate its generation.

After creating a few Ansible-based Kubernetes projects the boilerplate begins to emerge on two fronts - a number of mandatory Ansible files and the Kubernetes object definitions. What’s most frustrating is that, for the most part, Kubernetes objects are often detailed (verbose) yet irritatingly repetitive and predictable.

In this article we’ll see how simple it is to install Kubernetes onto some Ubuntu hosts using [Pharos].

Pharos is a Certified Kubernetes with all batteries included. It is powered by the latest upstream version of Kubernetes kernel and include tools for cluster lifecycle management.

You’ve probably created a machine image at some point. A base image for AWS that builds upon someone’s work by adding a particular version of Java or Python or a new utility. Did you create the image on AWS using an EC2 instance, login, run some yum or apt-get and then save it? Great, and if someone wants the source code for that image or you want to build a similar image on a different region or provider? Well, [Packer] is an IaC tool for automating the construction of machine images.

The Python Jenkins module is a convenient wrapper for the Jenkins REST API that gives you control of a Jenkins server in a pythonic way. Here we’ll see how to grab all the jobs from a Jenkins server and also how these jobs can be re-created from the captured material.

Here we’re going to be looking at the the idea of applying automation tools to the wider product development process. Tools that help you do this are part of a collection known as “Infrastructure as Code”, which refers to the the provisioning of compute instances (physical machines and their operating systems) and software applications using revision-controlled machine-readable text files.