
k8m
一款轻量级、跨平台的 Mini Kubernetes AI Dashboard,集成多集群管理、智能分析、实时异常检测和自然语言查询功能,支持多架构并可单文件部署,助力高效集群管理与运维优化。
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k8m is an AI-driven Mini Kubernetes AI Dashboard lightweight console tool designed to simplify cluster management. It is built on AMIS and uses 'kom' as the Kubernetes API client. k8m has built-in Qwen2.5-Coder-7B model interaction capabilities and supports integration with your own private large models. Its key features include miniaturized design for easy deployment, user-friendly interface for intuitive operation, efficient performance with backend in Golang and frontend based on Baidu AMIS, pod file management for browsing, editing, uploading, downloading, and deleting files, pod runtime management for real-time log viewing, log downloading, and executing shell commands within pods, CRD management for automatic discovery and management of CRD resources, and intelligent translation and diagnosis based on ChatGPT for YAML property translation, Describe information interpretation, AI log diagnosis, and command recommendations, providing intelligent support for managing k8s. It is cross-platform compatible with Linux, macOS, and Windows, supporting multiple architectures like x86 and ARM for seamless operation. k8m's design philosophy is 'AI-driven, lightweight and efficient, simplifying complexity,' helping developers and operators quickly get started and easily manage Kubernetes clusters.
README:
k8m 是一款AI驱动的 Mini Kubernetes AI Dashboard 轻量级控制台工具,专为简化集群管理设计。它基于 AMIS 构建,并通过
kom
作为 Kubernetes API 客户端,k8m 内置了
Qwen2.5-Coder-7B,支持deepseek-ai/DeepSeek-R1-Distill-Qwen-7B模型
模型交互能力,同时支持接入您自己的私有化大模型。
- 迷你化设计:所有功能整合在一个单一的可执行文件中,部署便捷,使用简单。
- 简便易用:友好的用户界面和直观的操作流程,让 Kubernetes 管理更加轻松。
- 高效性能:后端采用 Golang 构建,前端基于百度 AMIS,保证资源利用率高、响应速度快。
-
AI驱动融合:基于ChatGPT实现划词解释、资源指南、YAML属性自动翻译、Describe信息解读、日志AI问诊、运行命令推荐,并集成了
k8s-gpt
功能,实现中文展现,为管理k8s提供智能化支持。 - 多集群管理:自动识别集群内部使用InCluster模式,配置kubeconfig路径后自动扫描同级目录下的配置文件,同时注册管理多个集群。
- Pod 文件管理:支持 Pod 内文件的浏览、编辑、上传、下载、删除,简化日常操作。
- Pod 运行管理:支持实时查看 Pod 日志,下载日志,并在 Pod 内直接执行 Shell 命令。
- CRD 管理:可自动发现并管理 CRD 资源,提高工作效率。
- Helm 市场:支持Helm自由添加仓库,一键安装、卸载、升级 Helm 应用。
- 跨平台支持:兼容 Linux、macOS 和 Windows,并支持 x86、ARM 等多种架构,确保多平台无缝运行。
- 完全开源:开放所有源码,无任何限制,可自由定制和扩展,可商业使用。
k8m 的设计理念是“AI驱动,轻便高效,化繁为简”,它帮助开发者和运维人员快速上手,轻松管理 Kubernetes 集群。
- 下载:从 GitHub 下载最新版本。
-
运行:使用
./k8m
命令启动,访问http://127.0.0.1:3618。 - 参数:
Usage of ./k8m:
--admin-password string 管理员密码 (default "123456")
--admin-username string 管理员用户名 (default "admin")
-k, --chatgpt-key string 大模型的自定义API Key (default "sk-xxxxxxx")
-m, --chatgpt-model string 大模型的自定义模型名称 (default "Qwen/Qwen2.5-Coder-7B-Instruct")
-u, --chatgpt-url string 大模型的自定义API URL (default "https://api.siliconflow.cn/v1")
-d, --debug 调试模式
--in-cluster 是否自动注册纳管宿主集群,默认启用
--jwt-token-secret string 登录后生成JWT token 使用的Secret (default "your-secret-key")
-c, --kubeconfig string kubeconfig文件路径 (default "/root/.kube/config")
--kubectl-shell-image string Kubectl Shell 镜像。默认为 bitnami/kubectl:latest,必须包含kubectl命令 (default "bitnami/kubectl:latest")
--log-v int klog的日志级别klog.V(2) (default 2)
--login-type string 登录方式,password, oauth, token等,default is password (default "password")
--node-shell-image string NodeShell 镜像。 默认为 alpine:latest,必须包含`nsenter`命令 (default "alpine:latest")
-p, --port int 监听端口 (default 3618)
--sqlite-path string sqlite数据库文件路径, (default "./data/k8m.db")
-v, --v Level klog的日志级别 (default 2)
从v0.0.8版本开始,将内置GPT,无需配置。 如果您需要使用自己的GPT,请参考以下步骤。
需要设置环境变量,以启用ChatGPT。
export OPENAI_API_KEY="sk-XXXXX"
export OPENAI_API_URL="https://api.siliconflow.cn/v1"
export OPENAI_MODEL="Qwen/Qwen2.5-Coder-7B-Instruct"
如果设置参数后,依然没有效果,请尝试使用./k8m -v 6
获取更多的调试信息。
会输出以下信息,通过查看日志,确认是否启用ChatGPT。
ChatGPT 开启状态:true
ChatGPT 启用 key:sk-hl**********************************************, url:https: // api.siliconflow.cn/v1
ChatGPT 使用环境变量中设置的模型:Qwen/Qwen2.5-Coder-7B-Instruc
本项目集成了github.com/sashabaranov/go-openaiSDK。 国内访问推荐使用硅基流动的服务。 登录后,在https://cloud.siliconflow.cn/account/ak创建API_KEY
以下是k8m支持的环境变量设置参数及其作用的表格:
环境变量 | 默认值 | 说明 |
---|---|---|
PORT |
3618 |
监听的端口号 |
KUBECONFIG |
~/.kube/config |
kubeconfig 文件路径 |
OPENAI_API_KEY |
"" |
大模型的 API Key |
OPENAI_API_URL |
"" |
大模型的 API URL |
OPENAI_MODEL |
Qwen/Qwen2.5-Coder-7B-Instruct |
大模型的默认模型名称,如需DeepSeek,请设置为deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
LOGIN_TYPE |
"password" |
登录方式(如 password , oauth , token ) |
ADMIN_USERNAME |
"admin" |
管理员用户名 |
ADMIN_PASSWORD |
"123456" |
管理员密码 |
DEBUG |
"false" |
是否开启 debug 模式 |
LOG_V |
"2" |
log输出日志,同klog用法 |
JWT_TOKEN_SECRET |
"your-secret-key" |
用于 JWT Token 生成的密钥 |
KUBECTL_SHELL_IMAGE |
bitnami/kubectl:latest |
kubectl shell 镜像地址 |
NODE_SHELL_IMAGE |
alpine:latest |
Node shell 镜像地址 |
SQLITE_PATH |
/data/k8m.db |
持久化数据库地址,默认sqlite数据库,文件地址/data/k8m.db |
IN_CLUSTER |
"true" |
是否自动注册纳管宿主集群,默认启用 |
这些环境变量可以通过在运行应用程序时设置,例如:
export PORT=8080
export OPENAI_API_KEY="your-api-key"
export GIN_MODE="release"
./k8m
注意:环境变量会被启动参数覆盖。
- 创建 KinD Kubernetes 集群
brew install kind
- 创建新的 Kubernetes 集群:
kind create cluster --name k8sgpt-demo
kubectl apply -f https://raw.githubusercontent.com/weibaohui/k8m/refs/heads/main/deploy/k8m.yaml
- 访问: 默认使用了nodePort开放,请访问31999端口。或自行配置Ingress http://NodePortIP:31999
首选建议通过修改环境变量方式进行修改。 例如增加deploy.yaml中的env参数
build-all 目标支持以下操作系统和架构组合的交叉编译:
-
Linux:
amd64
arm64
ppc64le
s390x
mips64le
riscv64
-
Darwin(macOS):
amd64
arm64
-
Windows:
amd64
arm64
构建适用于当前操作系统和架构的 k8m
可执行文件:
make build
交叉编译 k8m
为所有指定的平台和架构:
make build-all
在 Unix 系统上构建并运行 k8m
:
make run
删除所有编译生成的可执行文件和 bin/
目录:
make clean
显示所有可用的 Makefile 目标及其描述:
make help
-
版本控制:你可以在构建时通过传递
VERSION
变量来指定自定义版本:make build VERSION=v2.0.0
-
可执行文件扩展名:对于 Windows 构建,Makefile 会自动为可执行文件添加
.exe
扩展名。 -
依赖性:确保 Git 已安装并且项目已初始化为 Git 仓库,以便正确获取
GIT_COMMIT
哈希值。
-
缺少依赖:如果遇到与缺少命令相关的错误(如
make
、go
等),请确保所有先决条件已安装并正确配置在系统的PATH
中。 -
权限问题:如果在运行
make run
时收到权限被拒绝的错误,请确保bin/
目录和编译后的二进制文件具有必要的执行权限:chmod +x bin/k8m
- 文件浏览权限问题:依赖容器内的ls命令,请在容器内安装shell、tar、cat等命令 。
- 无法启动:启动时卡住,请使用 k8m -v 6 命令启动,会输出更多日志,一般是由于部分版本的k8s集群的openAPI文档格式问题导致,请将日志贴到issue,或微信发我,我将优先处理 。
v0.0.60更新
- 增加helm 常用仓库
- Namespace增加LimitRange、ResourceQuota快捷菜单
- 增加InCluster模式开关 默认开启InCluster模式,如需关闭,可以注入环境变量,或修改配置文件,或修改命令行参数
v0.0.53更新
- 日志查看支持颜色,如果输出console的时候带有颜色,那么在pod 日志查看时就可以显示。
- Helm功能上线
2.1 新增helm仓库
2.2 安装helm chart 应用 应用列表
查看应用
支持对参数内容选中划词AI解释
2.3 查看已部署release
2.4 查看安装参数
2.5 更新、升级、降级部署版本
2.6 查看已部署release变更历史
v0.0.50更新
v0.0.49更新
- 新增标签搜索:支持精确搜索、模糊搜索。
精确搜索。可以搜索k,k=v两种方式精确搜索。默认列出所有标签。支持自定义新增搜索标签。
模糊搜索。可以搜索k,v中的任意满足。类似like %xx%的搜索方式。
- 多集群纳管支持自定义名称。
- 优化Pod状态显示
在列表页展示pod状态,不同颜色区分正常运行与未就绪运行。
v0.0.44更新
-
新增创建功能页面 执行过的yaml会保存下来,下次打开页面可以直接点击,收藏的yaml可以导入导出。导出的文件为yaml,可以复用
-
deploy、ds、sts等类型新增关联资源 4.1 容器组 直接显示其下受控的pod容器组,并提供快捷操作
4.2 关联事件 显示deploy、rs、pod等所有相关的事件,一个页面看全相关事件
4.3 日志 显示Pod列表,可选择某个pod、Container展示日志
4.4 历史版本 支持历史版本查看,并可diff
v0.0.21更新
- 新增问AI功能:
有什么问题,都可以直接询问AI,让AI解答你的疑惑
- 文档界面优化:
优化AI翻译效果,降低等待时间
- 文档字段级AI示例:
针对具体的字段,给出解释,给出使用Demo样例。
- 增加容忍度详情:
- 增加Pod关联资源
一个页面,展示相关的svc、endpoint、pvc、env、cm、secret,甚至集成了pod内的env列表,方便查看
- yaml创建增加导入功能:
增加导入功能,可以直接执行,也可导入到编辑器。导入编辑器后可以二次编辑后,再执行。
v0.0.19更新
- 多集群管理功能
按需选择多集群,可随时切换集群
- 节点资源用量功能
直观显示已分配资源情况,包括cpu、内存、pod数量、IP数量。
- Pod 资源用量
- Pod CPU内存设置
按范围方式显示CPU设置,内存设置,简洁明了
- AI页面功能升级为打字机效果
响应速度大大提升,实时输出AI返回内容,体验升级
v0.0.15更新
- 所有页面增加资源使用指南。启用AI信息聚合。包括资源说明、使用场景(举例说明)、最佳实践、典型示例(配合前面的场景举例,编写带有中文注释的yaml示例)、关键字段及其含义、常见问题、官方文档链接、引用文档链接等信息,帮助用户理解k8s
- 所有资源页面增加搜索功能。部分页面增高频过滤字段搜索。
- 改进LimitRange信息展示模式
- 改进状态显示样式
- 统一操作菜单
- Ingress页面增加域名转发规则信息
- 改进标签显示样式,鼠标悬停展示
- 优化资源状态样式更小更紧致
- 丰富Service展示信息
- 突出显示未就绪endpoints
- endpoints鼠标悬停展开未就绪IP列表
- endpointslice 突出显示未ready的IP及其对应的POD,
- 角色增加延展信息
- 角色与主体对应关系
- 界面全量中文化,k8s资源翻译为中文,方便广大用户使用。
如果你有任何进一步的问题或需要额外的帮助,请随时与我联系!
zhaomingcheng01:提出了诸多非常高质量的建议,为k8m的易用好用做出了卓越贡献~
微信(大罗马的太阳) 搜索ID:daluomadetaiyang,备注k8m。
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wcgw is a shell and coding agent designed for Claude and Chatgpt. It provides full shell access with no restrictions, desktop control on Claude for screen capture and control, interactive command handling, large file editing, and REPL support. Users can use wcgw to create, execute, and iterate on tasks, such as solving problems with Python, finding code instances, setting up projects, creating web apps, editing large files, and running server commands. Additionally, wcgw supports computer use on Docker containers for desktop control. The tool can be extended with a VS Code extension for pasting context on Claude app and integrates with Chatgpt for custom GPT interactions.

k8m
k8m is an AI-driven Mini Kubernetes AI Dashboard lightweight console tool designed to simplify cluster management. It is built on AMIS and uses 'kom' as the Kubernetes API client. k8m has built-in Qwen2.5-Coder-7B model interaction capabilities and supports integration with your own private large models. Its key features include miniaturized design for easy deployment, user-friendly interface for intuitive operation, efficient performance with backend in Golang and frontend based on Baidu AMIS, pod file management for browsing, editing, uploading, downloading, and deleting files, pod runtime management for real-time log viewing, log downloading, and executing shell commands within pods, CRD management for automatic discovery and management of CRD resources, and intelligent translation and diagnosis based on ChatGPT for YAML property translation, Describe information interpretation, AI log diagnosis, and command recommendations, providing intelligent support for managing k8s. It is cross-platform compatible with Linux, macOS, and Windows, supporting multiple architectures like x86 and ARM for seamless operation. k8m's design philosophy is 'AI-driven, lightweight and efficient, simplifying complexity,' helping developers and operators quickly get started and easily manage Kubernetes clusters.

gptme
Personal AI assistant/agent in your terminal, with tools for using the terminal, running code, editing files, browsing the web, using vision, and more. A great coding agent that is general-purpose to assist in all kinds of knowledge work, from a simple but powerful CLI. An unconstrained local alternative to ChatGPT with 'Code Interpreter', Cursor Agent, etc. Not limited by lack of software, internet access, timeouts, or privacy concerns if using local models.

aichat
Aichat is an AI-powered CLI chat and copilot tool that seamlessly integrates with over 10 leading AI platforms, providing a powerful combination of chat-based interaction, context-aware conversations, and AI-assisted shell capabilities, all within a customizable and user-friendly environment.

wingman-ai
Wingman AI allows you to use your voice to talk to various AI providers and LLMs, process your conversations, and ultimately trigger actions such as pressing buttons or reading answers. Our _Wingmen_ are like characters and your interface to this world, and you can easily control their behavior and characteristics, even if you're not a developer. AI is complex and it scares people. It's also **not just ChatGPT**. We want to make it as easy as possible for you to get started. That's what _Wingman AI_ is all about. It's a **framework** that allows you to build your own Wingmen and use them in your games and programs. The idea is simple, but the possibilities are endless. For example, you could: * **Role play** with an AI while playing for more immersion. Have air traffic control (ATC) in _Star Citizen_ or _Flight Simulator_. Talk to Shadowheart in Baldur's Gate 3 and have her respond in her own (cloned) voice. * Get live data such as trade information, build guides, or wiki content and have it read to you in-game by a _character_ and voice you control. * Execute keystrokes in games/applications and create complex macros. Trigger them in natural conversations with **no need for exact phrases.** The AI understands the context of your dialog and is quite _smart_ in recognizing your intent. Say _"It's raining! I can't see a thing!"_ and have it trigger a command you simply named _WipeVisors_. * Automate tasks on your computer * improve accessibility * ... and much more

letmedoit
LetMeDoIt AI is a virtual assistant designed to revolutionize the way you work. It goes beyond being a mere chatbot by offering a unique and powerful capability - the ability to execute commands and perform computing tasks on your behalf. With LetMeDoIt AI, you can access OpenAI ChatGPT-4, Google Gemini Pro, and Microsoft AutoGen, local LLMs, all in one place, to enhance your productivity.
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AirGo
AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

mosec
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

llm-code-interpreter
The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

pezzo
Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

learn-generative-ai
Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

gcloud-aio
This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

fluid
Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

aiges
AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.