#Kubernetes

Kong API Gateway is a cloud-native solution supporting high-performance API traffic management. Key features include proxying, routing, and authentication, with Kubernetes support via its Ingress Controller. It offers AI capabilities with multi-LLM support and can be deployed in various environments. A wide range of plugins allows customization of traffic management, security, and monitoring.

KubeSphere is a distributed operating system tailored for managing cloud-native applications using Kubernetes. It offers seamless third-party integrations and multi-tenant capabilities across data center, multi-cloud, and edge environments. The user-friendly web UI facilitates full-stack operations and streamlined DevOps workflows, providing essential features for enterprise-level Kubernetes management. With multi-cluster management, DevOps solutions, service mesh, observability, an app store, and edge computing, KubeSphere is perfect for deploying Kubernetes clusters across diverse infrastructures, offering a comprehensive and accessible ecosystem.

AIKit is an adaptable platform for hosting, deploying, and fine-tuning large language models (LLMs). It offers OpenAI API-compatible tools, supports LocalAI for inference, and provides a flexible fine-tuning interface through Unsloth. Its minimal image size enhances security, and it supports multi-modal models and OpenAI API clients. AIKit is suitable for air-gapped environments and allows multiple model hosting with one image. It can be deployed on Kubernetes and supports AMD64, ARM64, and NVIDIA GPUs for faster inferencing.

This project integrates Dapr to enhance distributed applications with microservices, leveraging .NET 7 for improved service discovery, communication, and scalability. It addresses eCommerce challenges through a modular architecture with key features like a Blazor frontend, an Envoy API gateway, and independent backend microservices for managing eCommerce functionalities. Asynchronous messaging facilitated by the Dapr-supported event bus enhances performance. Explore various deployment options including Docker and Kubernetes for effective application management and scalability, ideal for developers pursuing robust, scalable microservices integration.

Data on EKS provides effective solutions for scaling AI and data workloads on Amazon EKS. With a diverse set of Terraform Blueprints and best practices, it aids in deploying data and AI/ML platforms. The project utilizes tools such as Apache Spark, Apache Flink, and Apache Kafka for data processing, along with NVIDIA Triton Server, Ray, and AWS Trainium for AI functions, enhancing both scalability and performance. It offers advanced serving solutions, supports high-performance NVIDIA GPUs, and integrates Kubernetes with big data using Amazon EMR on EKS. Data on EKS also streamlines workflow automation with Apache Airflow, offering robust analytics capabilities to build resilient, production-ready clusters.

The k8sgpt-operator integrates K8sGPT into Kubernetes environments, providing customizable resources for AI-driven workload management. It enhances monitoring capabilities by configuring analysis outputs and utilizing various AI backends. Additionally, it offers flexible cache settings with Azure or S3 and supports easy installation through Helm, making it suitable for advanced cluster management across multiple infrastructures.

Explore a robust Kubernetes management tool developed in C# Blazor, integrating ChatGPT models to ease Kubernetes administration. Suitable for beginners, it features an intuitive interface with YAML analysis, dynamic resource display, and multilingual support including Chinese and English. The tool facilitates semantic command operations, detailed resource explanations, and visual workload mapping through topology diagrams, enabling efficient YAML resource editing and integration of commands like 'kubectl describe' and 'kubectl explain'.

OpenFaaS simplifies the deployment of event-driven functions and microservices on Kubernetes, utilizing OCI-compatible images for scalability and auto-scaling. It features a UI portal and one-click installation, supports multiple languages using a Template Store or Dockerfile, and is portable across various environments. The Golang-based CLI allows for templating and function definition via YAML, enhancing integration with Kubernetes or OpenShift. OpenFaaS is available in a commercially supported Pro distribution, suitable for scalable serverless architecture.

This implementation outlines top practices for deploying microservices on Azure with Kubernetes, featuring Fabrikam's drone delivery service. It showcases various microservices like ingestion and scheduling, simulating real-world designs. Tested to handle up to 2000 messages/sec, it ensures reliable performance. Visit Azure Architecture Center for more insights.

Explore a configuration language with rich validation and tools for modern development. Access detailed documentation, including installation steps, language guides, and tools. Participate in community discussions on GitHub and track issues. Discover examples and implementations in diverse environments such as Go, JVM, and Kubernetes, with support for editors like VS Code and Neovim. Stay informed about updates and contribute to the language's evolution.

Sematic offers an open-source ML platform enabling the creation of complex, type-safe pipelines with simplicity using Python. It supports local runs and scalable cloud executions via Kubernetes. Key features include seamless local-to-cloud integration, comprehensive traceability, and versatile compute resource management. It is particularly suitable for integrating data processing with model training to ensure reproducibility and easy visualization in a web interface. Boasting major compatibility with frameworks like Apache Spark, Ray, PyTorch, and TensorFlow, it requires no pre-deployment setup, simplifying onboarding for engineers and data scientists.

Argo CD, a GitOps tool for Kubernetes, offers automated, auditable, and version-controlled application deployment. Its robust community and comprehensive documentation support seamless application lifecycle management, making it integral for organizations aiming to optimize Kubernetes operations.

Kubie provides efficient management of Kubernetes contexts and namespaces with standalone shell support and compatibility with multiple configuration files. Features include 'kubie exec' for executing commands within specific contexts and namespaces, and 'kubie lint' for validating config files. It allows seamless environment management with intuitive commands and customizable settings, and is easily installed across multiple platforms like Linux, OS X, and Homebrew. Suitable for developers looking for improved control and flexibility in Kubernetes operations.

Jina-Serve offers a dynamic framework for AI services, supporting gRPC, HTTP, and WebSockets. It integrates major ML frameworks, dynamic batching, and containerization for efficient deployment. Enhance scalability with Kubernetes, Docker Compose, and cloud capabilities while focusing on core logic and service design. Benefit from LLM streaming and orchestrate with executors and flows for seamless integration.

Explore Kubernetes-native AutoML that seamlessly automates processes like hyperparameter tuning, early stopping, and neural architecture search across multiple frameworks such as TensorFlow, PyTorch, and MXNet. This open-source project integrates efficiently with Kubernetes resources and tools such as Kubeflow Training Operator and Argo Workflows, supporting algorithms including Random Search and Bayesian Optimization. Discover framework compatibility with Goptuna, Hyperopt, and Optuna, and initiate efficient model tuning with the Python SDK.

Helix provides a secure platform for hosting open source AI models directly in data centers or VPCs, featuring RAG, API-calling, and model fine-tuning with a user-friendly drag-and-drop interface. This solution optimizes GPU usage and reduces latency for scalable applications. Install effortlessly with Docker and Kubernetes, guided by complete documentation. Helix is designed for personal, educational, and small business applications, promoting innovation while safeguarding data security and control. Engage with the Helix community to explore and contribute to cutting-edge AI solutions.

Kubeflow offers a comprehensive ecosystem of Kubernetes-native components that streamline, scale, and port AI/ML operations. It supports top open-source tools throughout every phase of the AI/ML lifecycle. With modules like KServe, Katib, Model Registry, and Pipelines, each having accessible repositories, Kubeflow is a community-governed platform that evolves with ongoing contributions from diverse groups. Comprehensive documentation is available for in-depth exploration.

Explore WSO2 APK, a cutting-edge open-source API management platform integrated with Kubernetes. It features a flexible microservices architecture, supports CI/CD workflows, and uses an Envoy-based gateway for precise API use cases, underscoring its role in efficient Kubernetes deployment.

ialacol provides a lightweight alternative to the OpenAI API, focusing on seamless deployment on Kubernetes. It supports multiple LLMs, including LLaMa 2 and StarCoder, and enhances user experience with streaming and optional CUDA acceleration. This tool is perfect for developers looking for a flexible, cloud-native deployment solution, easily integrating with platforms like Github Copilot.

Kubernetes Copilot utilizes OpenAI technology for automating Kubernetes cluster operations, providing efficient management and security enhancements. It enables task automation using ChatGPT, system diagnostics, and manifest generation from prompts. With integrated kubectl and trivy tools, it offers strong security and easy access to cluster resources. Additionally, it supports web and Google searches from the terminal, optimizing developer workflows. Suitable for enhancing efficiency and security, it is available as an open-source project in both Go and Python.

Explore an operator that simplifies AI/ML model deployment and tuning within Kubernetes clusters. Using container images to manage large model files and enabling automatic GPU node provisioning, this tool provides preset configurations for easier workload parameter adjustments across various hardware setups. Models like falcon and phi-3 can be deployed effectively, and the operator is hosted in Microsoft’s Container Registry when licenses permit. Employing a workspace custom resource and Kubernetes CRD/controller pattern, it automates the deployment process to align with GPU and tuning specifications, including model fine-tuning capabilities. Supported by Azure CLI or Terraform for easy model addition, this operator delivers an efficient approach for scaling and customizing AI applications.

This repository offers resources and configuration scripts to build custom Amazon EKS AMIs using HashiCorp Packer, as used by Amazon EKS for official AMIs. It includes a Makefile for easy integration with specific Kubernetes versions and OS distributions. New users are guided through AWS documentation to efficiently begin with Amazon EKS and launch node groups. The repository emphasizes security by directing issue reports to AWS Security and is available under the MIT-0 license with specific provisions for NVIDIA and Neuron accelerated AMIs.

Kubeshark is an API traffic analyzer built for Kubernetes, providing real-time protocol-level visibility. It captures and monitors traffic and payloads across containers, pods, nodes, and clusters. Deployment is possible via binary, Homebrew, or Helm. The web UI streams cluster traffic, offering insights akin to TCPDump and Wireshark, tailored for Kubernetes. Further details and contribution guidelines are available in the documentation.

Grafana Alloy serves as a flexible OpenTelemetry Collector distribution, integrating Prometheus for enhanced observability. Key features include powerful programmable pipelines, broad compatibility with diverse telemetry ecosystems, and seamless Kubernetes-native integration. The platform facilitates centralized configuration management and intuitive pipeline debugging through a user-friendly UI. Alloy also supports automatic workload distribution with cluster formation and allows the sharing of pipelines via modules. Regular updates ensure alignment with upstream OpenTelemetry Collector advancements, bolstered by active community support through Grafana's Slack and forums.

The MPI Operator facilitates distributed training on Kubernetes by simplifying configuration and deployment. It allows for efficient resource management and scalability in machine learning tasks, supporting diverse MPI implementations such as Intel MPI and MPICH. Key features include job monitoring and logging, enhancing manageability in high-performance computing applications. This setup is optimized for environments demanding efficient orchestration and resource usage.

Longhorn provides efficient block storage for Kubernetes with a robust distributed system that avoids single points of failure. It supports incremental snapshots and backups to NFSv4 or S3-compatible storage. Other features include recurring backups, non-disruptive upgrades, and a user-friendly dashboard. The installation process is straightforward via kubectl or Helm, enhancing Kubernetes with persistent volume capabilities.

The tool enhances Kubernetes cluster management by providing detailed issue diagnosis through integrated AI capabilities. It leverages SRE expertise to identify and explain issues clearly, independent of platform specifics. The tool integrates with AI solutions including OpenAI and Amazon Bedrock, offering critical insights to improve cluster performance. It supports various installations, compatible with platforms such as Linux, Mac, Windows, and Kubernetes clusters. Integration with monitoring solutions like Prometheus facilitates continuous monitoring and real-time issue diagnosis.

Kubeflow Training Operator offers a Kubernetes-based system for scalable, distributed training of machine learning models. Compatible with frameworks like PyTorch, TensorFlow, and XGBoost, it also supports HPC tasks through MPI. It simplifies model training via Kubernetes Custom Resources API and a Python SDK, aiding in efficient resource management. Explore integration and performance enhancement with comprehensive guides and community resources.

Flyte is a Kubernetes-based framework that simplifies managing scalable data and ML pipelines. It supports Python and other languages, offering strong data validation, dynamic workflow capabilities, and efficient resource management, making it suitable for cloud and on-premise environments. Used by companies like LinkedIn and Spotify, Flyte's extensive SDKs and documentation streamline deployment and scaling for complex workflows.

This Helm chart is suitable for Kubernetes stateless applications needing only namespace-scoped resources. It enables deployment, job, or cronjob configurations without privileged containers or Kubernetes API access. Features include flexible service and security settings, detailed resource configuration, and support for Kubernetes integrations, including Ingress and RBAC. It facilitates efficient application management with options for persistence, scaling, and monitoring.

Discover kubectl, a command line interface for Kubernetes offering essential client packages and seamless integration. It follows comprehensive contribution guidelines for creating high-quality, Go tool-compliant code. Engage with the Kubernetes community for contributions and support while adhering to established conduct codes. Enhance management and optimize performance in diverse Kubernetes environments with kubectl's innovative client interfaces.

Utilizing AI technology, Kopilot assists in diagnosing and troubleshooting Kubernetes cluster workloads while auditing resource configurations for security improvements. It helps in identifying the root causes of issues and misconfigurations to ensure efficient performance and security. Kopilot supports various languages and is easily installable across multiple platforms.

This guide outlines the recommended baseline infrastructure for AKS clusters, assisting teams in deploying key components such as identity, network topology, and secret management. It supports multi-regional growth with secure traffic and serves as a foundation for production stages, integrating Azure services for enhanced lifecycle and deployment management.

Crane provides a FinOps platform specifically for Kubernetes clusters, aiming to simplify cloud resource analytics and cost management. This FinOps Certified Solution includes features like cost visualization and optimization assessments. It also offers a recommendation framework to enhance resource management efficiency. With capabilities like prediction-driven autoscaling and load-aware scheduling, Crane optimizes resource utilization, further supported by enhanced QoS for operational stability. Discover its capabilities via Crane's live demo and documentation to start optimizing cloud resource costs.

Discover KubeInvaders, a chaos engineering tool designed for Kubernetes clusters with a gamified twist. This tool allows you to conduct stress tests interactively, enhancing system resilience. Featuring automatic chaos experiments, namespace switching, pod monitoring, and container customization, it integrates smoothly with Kubernetes through Docker, Podman, and Helm installation options. Suitable for engineers who require detailed metrics, security support, and thorough installation instructions.

This guide provides a structured approach to manually setting up a Kubernetes cluster to understand its core components and functionalities. It focuses on educational purposes, demonstrating tasks from node configuration to control plane deployment rather than using automated tools. Intended for learners exploring Kubernetes integration, the tutorial utilizes four ARM64 machines and aligns with versions like Kubernetes v1.28.x and containerd v1.7.x. As an educational tool, it does not cater to production use or offer community support.

Explore Loggie, a cloud-native solution for efficient log collection and management. Built on Golang, this lightweight, high-performance agent is perfect for Kubernetes environments, supporting multiple pipeline configurations through CRDs for straightforward operation and management. Loggie enhances observability, reliability, and automation, making it suitable for scalable log data platforms. Its features include real-time data transformation, adaptive concurrency, and comprehensive monitoring, providing reliable log aggregation and analysis across different architectures.

The Go client library allows users to interact with Istio resources within a Kubernetes cluster efficiently. Installation is straightforward with support for Go's module system, and it offers compatibility with both the latest updates and specific versions of Istio from release 1.4 onwards, facilitating the reliable management of Istio services.

This platform uses a robust architecture to enable the efficient sharing of resources like GPUs and FPGAs. It supports on-premises, hybrid, and cloud deployments, integrates widely-used AI frameworks, and offers a comprehensive solution for deep learning. The platform is compatible with Kubernetes and simplifies distributed training and IT operations. Its modular design allows for easy customization and scalability to meet developing AI demands.

RAGapp enables seamless Agentic RAG deployment in enterprise cloud environments using Docker and LlamaIndex. It supports AI models from OpenAI and Gemini, offers intuitive interfaces like Admin and Chat UIs, and plans enhanced security with access token authorization. Deploy smoothly with Docker Compose or Kubernetes and ensure effective traffic management via API Gateway integration. Reach out for support at any time.

The guide presents selected resources and insights for mastering Kubernetes and related ecosystems. It aids in understanding for KCNA exam candidates and cloud-native technology enthusiasts, providing exam highlights, practice questions, and key topic overviews like Kubernetes fundamentals and container orchestration. Appropriate for those exploring cloud infrastructures, it supports effective learning and preparation, complemented with cost-effective solutions and free materials.

Kubectl-ai plugin streamlines the creation of Kubernetes manifests with OpenAI GPT technology. It simplifies the development process by avoiding the need to source random manifests. Installation methods include Homebrew, Krew, and direct download from GitHub releases. Required setup involves a Kubernetes configuration and access to OpenAI API or a compatible service. Features encompass confirmation prompts and customization options via 'temperature' settings, and it supports Kubernetes OpenAPI Spec. The plugin seamlessly integrates with other tools, supporting pipe input/output and manifest editing in external editors.

ElasticDL utilizes Kubernetes features to boost frameworks like TensorFlow and PyTorch with fault tolerance and dynamic scheduling. It facilitates distributed training, ensuring continuity amid process failures, and optimizes GPU usage via Kubernetes preemption. With support for TensorFlow Estimator, Keras, and PyTorch, it offers a user-friendly interface for seamless execution. Extensive documentation and tutorials guide setup on platforms from local machines to cloud services like Google Kubernetes Engine.

LangStream provides a comprehensive platform for efficient application development and deployment. Its versatile CLI enables installation and management of applications in both local and cloud environments. Integration with Kubernetes supports various distributions for production deployments, leveraging external components like Apache Kafka and S3-compatible storage. Developer tools and sample applications further expand LangStream's adaptability, facilitating custom application creation. Access to resources and community support via Slack enhances user experience. Manage development cycles effectively with LangStream's robust features.

K9s is an open-source project that provides a terminal UI for navigating, observing, and managing Kubernetes clusters with ease. It operates across platforms including Linux, macOS, and Windows and supports Kubernetes versions 1.21.3 and above. K9s can be installed via Homebrew, Chocolatey, Docker, and other methods. Its features include real-time resource monitoring, context switching, and node shell access, without corporate backing and remaining community-driven and free.

This Golang conference provides insights into advances in cloud-native systems, focusing on multi-cloud management and Kubernetes innovation using Golang. It presents best practices from industry leaders such as ByteDance and Didi, and covers new approaches to observability with eBPF as well as high-performance server architecture design. Key topics include GitOps adoption and the development of enterprise-level microservices, emphasizing integration with technologies such as eBPF and Kubernetes. This event is a valuable resource for developers and enterprises aiming to improve their Golang infrastructure solutions in line with industry standards and emerging tech trends.

Astra is a cloud-native platform optimized for log, trace, and audit data management with advanced search and analytics functions. It focuses on recent data ingestion, offers robust support for Kubernetes, auto-scaling, and Grafana, and guarantees zero data loss. Astra supports multi-tenancy, operates across various cloud providers, and serves as an OpenSearch log alternative utilizing Apache Lucene technologies. Licensed under MIT, Astra prioritizes append-only records and excludes general-purpose search capabilities.

Garden is a DevOps automation tool developed to improve Kubernetes applications' development and testing by creating production-like environments on demand. Its Stack Graph feature helps streamline CI/CD pipelines by identifying dependencies and minimizing redundant tasks. Garden's configuration with `garden.yml` files suits complex applications across multiple repositories. The tool supports plugins for Kubernetes, Terraform, and Pulumi, offering adaptability across development environments. The interactive console and live sync mode provide real-time updates, enhancing the development experience. Engage with the Garden community on Discord for collaboration and support.

Kubestack is an open-source Terraform framework designed for Kubernetes platform engineering teams to define and manage their cloud-native infrastructure using a unified code base. With an emphasis on 'Convention over Configuration', it simplifies the engineering process, allowing team members to safely iterate through a GitOps workflow. Even complex Kubernetes platforms can benefit from its low-maintenance and extendable infrastructure. Users have access to detailed tutorials and community support to aid in efficient platform development and iteration, promoting scalability and effectiveness in platform engineering.

Enables efficient ML workflow execution and microservice orchestration, supporting multiple languages and cloud scalability.