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7 posts tagged with "use-case"

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· 8 min read
Jianbo Sun

Helm Charts are very popular that you can find almost 10K different software packaged in this way. While in today's multi-cluster/hybrid cloud business environment, we often encounter these typical requirements: distribute to multiple specific clusters, specific group distributions according to business need, and differentiated configurations for multi-clusters.

In this blog, we'll introduce how to use KubeVela to do multi cluster delivery for Helm Charts.

If you don't have multi clusters, don't worry, we'll introduce from scratch with only Docker or Linux System required. You can also refer to the basic helm chart delivery in single cluster.

· 7 min read
Wei Duan

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.

· 6 min read
Xiangbo Ma

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.

· 10 min read
Tianxin Dong and Yicai Yu

With the rapid development of cloud-native, how can we use cloud to empower business development? When launching applications, how can cloud developers easily develop and debug applications in a multi-cluster and hybrid cloud environment? In the deployment process, how to make the application deployment have sufficient verification and reliability?

These crucial issues are urgently needed to be resolved.

In this article, we will use KubeVela and Nocalhost to provide a solution for cloud debugging and multi-cluster hybrid cloud deployment.

When a new application needs to be developed and launched, we hope that the results of debugging in the local IDE can be consistent with the final deployment state in the cloud. Such a consistent posture gives us the greatest confidence in deployment and allows us to iteratively apply updates in a more efficient and agile way like GitOps. That is: when new code is pushed to the code repository, the applications in the environment are automatically updated in real time.

Based on KubeVela and Nocalhost, we can deploy application like below:


As shown in the figure: Use KubeVela to create an application, deploy the application to the test environment, and pause it. Use Nocalhost to debug the application in the cloud. After debugging, push the debugged code to the code repository, use GitOps to deploy with KubeVela, and then update it to the production environment after verification in the test environment.

In the following, we will introduce how to use KubeVela and Nocalhost for cloud debugging and multi-cluster hybrid cloud deployment.

· 10 min read
Tianxin Dong

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.

· 13 min read
Tianxin Dong

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.

· 12 min read
Da Yin, Yang Song

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.