Can I run multiple isolated environments simultaneously?

Yes, by using the --instance flag, you can deploy multiple independent clusters and application stacks with isolated configurations and unique network ports.

Do I need to install Kubernetes manually before using this skill?

No, the skill utilizes k3d to run Kubernetes inside your container engine (Podman/Docker) and will automatically create a cluster if one is not already present.

Does this skill support GPU acceleration for AI models?

Yes, the KubeAI application support specifically targets GPU-accelerated LLM inference, providing an OpenAI-compatible API on your local cluster.

How do I access my deployed applications?

Applications are accessible via Traefik ingress (e.g., http://jupyterhub.localhost:8080) or via standard kubectl port-forwarding commands provided in the status output.

What are the prerequisites for using the deploy skill?

You must have Helm installed on your system. Within the Overthink environment, you can quickly set this up using the 'ujust install helm' command.

Kubernetes Application Deployment

Name: Kubernetes Application Deployment
Author: atrawog

byatrawog

部署与 DevOps

Automates the deployment and lifecycle management of JupyterHub and KubeAI on local k3d Kubernetes clusters using Helm.

关于

The deploy skill streamlines the complex process of setting up local Kubernetes environments specifically optimized for AI and machine learning workloads. It handles the automated creation of k3d clusters and utilizes Helm to deploy sophisticated applications like multi-user JupyterHub servers and GPU-accelerated KubeAI inference engines. This skill is ideal for developers needing to bridge the gap between local development and production-grade orchestration, providing standardized commands for configuration, status monitoring, and scaling across multiple instances without manual infrastructure plumbing.

主要功能

Pre-configured Helm deployments for JupyterHub and KubeAI
GPU-accelerated LLM inference support with OpenAI-compatible APIs
Multi-instance support with automated port mapping and namespacing
0 GitHub stars
Automated k3d cluster provisioning and lifecycle management
Integrated status reporting for clusters, Helm releases, and pod health

使用场景

Deploying local LLM inference servers to test AI applications with GPU acceleration
Prototyping Kubernetes-native application stacks before cloud deployment
Spinning up a local multi-user Jupyter environment for collaborative data science

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add atrawog/overthink-plugins deploy

For use in Claude.ai and ChatGPT

Download Skill

GitHub

关于