Under today's multi-cluster business scene, we often encounter these typical requirements: distribute to multiple specific clusters, specific group distributions according to business need, and differentiated configurations for multi-clusters.
KubeVela v1.3 iterates based on the previous multi-cluster function. This article will reveal how to use it to do swift multiple clustered deployment and management to address all your anxieties.
The cloud platform development team of China Merchants Bank has been trying out KubeVela since 2021 internally and aims to using it for enhancing our primary application delivery and management capabilities. Due to the specific security concern for financial insurance industry, network control measurements are relatively strict, and our intranet cannot directly pull Docker Hub image, and there is no Helm image source available as well. Therefore, in order to landing KubeVela in the intranet, you must perform a complete offline installation.
This article will take the KubeVela V1.2.5 version as an example, introduce the offline installation practice to help other users easier to complete KubeVela's deployment in offline environment.
At the background of Machine learning goes viral, AI engineers not only need to train and debug their models, but also need to deploy them online to verify how it looks(of course sometimes, this part of the work is done by AI platform engineers. ). It is very tedious and draining AI engineers.
In the cloud-native era, our model training and model serving are also usually performed on the cloud. Doing so not only improves scalability, but also improves resource utility. This is very effective for machine learning scenarios that consume a lot of computing resources.
But it is often difficult for AI engineers to use cloud-native techniques. The concept of cloud native has become more complex over time. Even to deploy a simple model serving on cloud native architecture, AI engineers may need to learn several additional concepts: Deployment, Service, Ingress, etc.
As a simple, easy-to-use, and highly scalable cloud-native application management tool, KubeVela enables developers to quickly and easily define and deliver applications on Kubernetes without knowing any details about the underlying cloud-native infrastructure. KubeVela's rich extensibility extends to AI addons and provide functions such as model training, model serving, and A/B testing, covering the basic needs of AI engineers and helping AI engineers quickly conduct model training and model serving in a cloud-native environment.
This article mainly focus on how to use KubeVela's AI addon to help engineers complete model training and model serving more easily.
KubeVela is a simple, easy-to-use, and highly extensible cloud-native application platform. It can make developers deliver microservices applications easily, without knowing Kubernetes details.
KubeVela is based on OAM model, which naturally solves the orchestration problems of complex resources. It means that KubeVela can manage complex large-scale applications with GitOps. Convergence of team and system size after the system complexity problem.
KubeVela bridges the gap between applications and infrastructures, enabling easy delivery and management of development codes. Compared to Kubernetes objects, the Application in KubeVela better abstracts and simplifies the configurations which developers care about, and leave complex infrastruature capabilities and orchestration details to platform engineers. The KubeVela apiserver further exposes HTTP interfaces, which help developers to deploy applications even without Kubernetes cluster access.
This article will use Jenkins, a popular continuous integration tool, as basis and give a brief introduction to how to build GitOps-based application continuous delivery highway.
As an application management and integration platform, KubeVela needs to handle thousands of applications in production scenario. To evaluate the performance of KubeVela, develop team has conducted performance tests based on simultated environments and demonstrated the capability of managing a large number of applications concurrently.
7 Dec 2020 12:33pm, by Lei Zhang and Fei Guo
Last month at KubeCon+CloudNativeCon 2020, the Open Application Model (OAM) community launched KubeVela, an easy-to-use yet highly extensible application platform based on OAM and Kubernetes.
For developers, KubeVela is an easy-to-use tool that enables you to describe and ship applications to Kubernetes with minimal effort, yet for platform builders, KubeVela serves as a framework that empowers them to create developer-facing yet fully extensible platforms at ease.
The trend of cloud native technology is moving towards pursuing consistent application delivery across clouds and on-premises infrastructures using Kubernetes as the common abstraction layer. Kubernetes, although excellent in abstracting low-level infrastructure details, does introduce extra complexity to application developers, namely understanding the concepts of pods, port exposing, privilege escalation, resource claims, CRD, and so on. We’ve seen the nontrivial learning curve and the lack of developer-facing abstraction have impacted user experiences, slowed down productivity, led to unexpected errors or misconfigurations in production.
Abstracting Kubernetes to serve developers’ requirements is a highly opinionated process, and the resultant abstractions would only make sense had the decision-makers been the platform builders. Unfortunately, the platform builders today face the following dilemma: There is no tool or framework for them to easily extend the abstractions if any.
Thus, many platforms today introduce restricted abstractions and add-on mechanisms despite the extensibility of Kubernetes. This makes easily extending such platforms for developers’ requirements or to wider scenarios almost impossible.
In the end, developers complain those platforms are too rigid and slow in response to feature requests or improvements. The platform builders do want to help but the engineering effort is daunting: any simple API change in the platform could easily become a marathon negotiation around the opinionated abstraction design.