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The Graphical Processor Unit can now be configured to allocate resources for mainly machine learning engineers creating AI models with the KubeSphere update.

The graphic processing unit (GPU) in your rig is used for complicated processes and assists the central processing unit (CPU) in executing tasks that would be difficult to do individually.

KubeSphere is an open-source framework that is used to manage Kubernetes clusters. Its maintainers have released a recent update of their framework that makes it possible for you to manage and monitor the consumption of your graphical processor unit.

KubeSphere Version 3.2

The new update of KubeSphere, version 3.2, allows IT teams to seamlessly build AI workloads by efficiently consuming the graphical processor unit. 

The resource supplied from the GPU helps machine learning engineers train their artificial intelligence models more effectively, offering better results.

In addition, this new update allows simplified management of GPU resources, creating workloads and scheduling resources for specific tasks.

The update also offers other capabilities such as custom dashboard monitoring, converting Grafana into Kubesphere monitoring dashboards, and cross-cluster or multi-cluster scenarios.

The future of KubeSphere

KubeSphere is widely used by DevOps teams that are looking to manage and scale Kubernetes environments. With the release of KubeSphere, average administrators can now access the framework through a web interface.

The clusters in Kubernetes have increased drastically, and it is encouraging IT administrators to take up more responsibility on top of their existing ones.

KubeSphere aims to simplify this process of managing clusters in Kubernetes by creating a simplified version that can be accessed and used by anyone.

Although the new update has made it easier for machine learning engineers to allocate resources from their computers to undergo specific tasks, the rise in artificial intelligence will soon show how viable this new update will be. As for KubeSphere, with clusters increasing, they are trying to simplify their framework, making it accessible to everyone.