Cloud platforms are not new — they have been around for a few years. And containers have been around even longer. Together, they have changed the way we think about software. Since the creation of these technologies, we have focused on platforms and apps. And who could blame anyone? Containers and Kubernetes let us do things that were unheard of only a few years ago.
Epinio, the application development engine for Kubernetes, is meant to take you from app to URL in one step. It does that by either applying buildpacks to your app or using a pre-built docker image. Epinio installs into any Kubernetes cluster to bring your application from source code to deployment and allow for developers and operators to work better together.
The rise of containerization has been a revolutionary development for many organizations. Being able to deploy applications of any kind on a standardized platform with robust tooling and low overhead is a clear advantage over many of the alternatives. Viewing container images as a packaging format also allows users to take advantage of pre-built images, shared and audited publicly, to reduce development time and rapidly deploy new software.
Containers, along with containerization technology like Docker and Kubernetes, have become increasingly common components in many developers’ toolkits. The goal of containerization, at its core, is to offer a better way to create, package, and deploy software across different environments in a predictable and easy-to-manage way.
Virtual machines (VMs) have transformed infrastructure deployment and management. VMs are so ubiquitous that I can’t think of a single instance where I deployed production code to a bare metal server in my many years as a professional software engineer. VMs provide secure, isolated environments hosting your choice of operating system while sharing the resources of the underlying server. This allows resources to be allocated more efficiently, reducing the cost of over-provisioned hardware.
In Episode 1 of the OCTOpod, Alan Clark talks with Thierry Carrez about open source communities: what they are, how they work and how you can get involved.
Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.
We are happy to share the v0.3.0 release of Rancher Desktop. This release brings more stability, some new features, and a fresh look to the UI. This screenshot shows the preferences screen while in dark mode on Mac. In the lower left corner the status when performing an action is now displayed. This status will tell you when Rancher Desktop needs to download something, such as a version of Kubernetes you have now used before, or when another step is happening.
For those of us who need to get applications running in Kubernetes, having Kubernetes on the desktop is incredibly useful. When we want to focus on our applications, it’s especially useful when Kubernetes is easy to use. This is where Rancher Desktop comes in. Rancher Desktop provides easy-to-use Kubernetes and container management (something we’ll look at in a moment) for Mac and Windows. Having Kubernetes isn’t enough.
It has been five months since we announced project Harvester, open source hyperconverged infrastructure (HCI) software built using Kubernetes. Since then, we’ve received a lot of feedback from the early adopters. This feedback has encouraged us and helped in shaping Harvester’s roadmap. Today, I am excited to announce the Harvester v0.2.0 release, along with the Beta availability of the project!
