12 Repos
Configurations for running containerized applications on managed cloud orchestration services.
Distinguishing note: Specific to managed cloud container services rather than generic container orchestration.
Explore 12 awesome GitHub repositories matching devops & infrastructure · Cloud Container Deployments. Refine with filters or upvote what's useful.
Infisical is a centralized secrets management platform designed to store, synchronize, and control access to sensitive credentials and configuration data across distributed development, staging, and production environments. It employs client-side encryption to ensure that secrets remain unreadable to the underlying storage infrastructure, while providing a hierarchical permission model to govern both user and machine access. The platform distinguishes itself through dynamic credential provisioning, which generates short-lived access tokens that are automatically revoked after use. It supports
Runs the application on managed container services with production-ready traffic routing.
This project is a Docker educational resource and a collection of practical examples designed for learning containerization technologies. It serves as a guide for understanding container fundamentals, including the creation and management of custom images and the use of registries. The repository provides specialized references for container security hardening, such as managing kernel privileges and implementing supply chain security. It also includes tutorials for multi-container orchestration and a DevOps guide focused on CI/CD automation and image optimization. The material covers a broad
Provides configurations for running containerized workloads on managed cloud orchestration services.
This project is a recommendation system framework designed for building, evaluating, and operationalizing personalized item suggestion engines. It provides a comprehensive toolkit for implementing collaborative filtering and content-based algorithms, supported by an end-to-end machine learning pipeline for preparing datasets and deploying predictive models. The framework distinguishes itself through the integration of knowledge graphs to provide richer context for recommendations and the use of industry-specific patterns to accelerate system deployment. It also includes a specialized model ev
Supports the deployment of recommendation models to production using container orchestration and cloud databases.
Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep
Executes tasks within isolated Azure container instances.
Datasette is a tool for publishing and sharing SQLite databases as public websites. It functions as a data publishing system that provides searchable interfaces and JSON APIs to expose the contents of SQLite files. The project enables both server-side and client-side execution. It can operate as an API server or as a database browser that runs entirely within a web browser using WebAssembly, allowing for serverless database access. The system supports a variety of deployment strategies, including containerized images for cloud hosting and a local development server for testing. It includes c
Packages databases and the application into container images optimized for cloud platform deployment.
SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing images and videos using diffusion models. It functions as a comprehensive tool for diffusion model management and an automated image processing pipeline for bulk operations. The project is distinguished by its hardware-backend abstraction layer, which provides automatic detection and acceleration for NVIDIA CUDA, AMD ROCm, Intel OpenVINO, and DirectML. It features a headless generative API and a programmatic command interface, allowing users to trigger tasks via REST API or CLI wi
Supports running the application in containers on managed cloud orchestration services with public hostname exposure.
This is a step-by-step tutorial that teaches Docker from the ground up, covering how to build images from Dockerfiles, run and manage containers, and connect them on user-defined networks. The guide walks through packaging applications into portable containers and using Docker Compose to define and orchestrate multi-service applications with a single YAML configuration. The curriculum extends into cloud deployment, explaining how to push images to registries like Docker Hub and deploy single-container applications to AWS Elastic Beanstalk as well as multi-container setups to AWS ECS. It also
Explains deploying containerized applications to AWS Elastic Beanstalk and ECS.
Dieses Projekt ist eine Referenzbibliothek und eine Sammlung von Beispiel-Code-Mustern für die Bereitstellung von Cloud-Infrastruktur mithilfe des AWS CDK. Es bietet eine Reihe von Beispielprojekten, die demonstrieren, wie Rechen-, Speicher- und Netzwerkressourcen mithilfe allgemeiner Programmiersprachen definiert werden. Die Bibliothek enthält Referenzimplementierungen für verschiedene Architekturmuster, einschließlich serverloser Backends mit GraphQL- und WebSocket-APIs, Container-Orchestrierung mit Load Balancern und Auto-Scaling sowie globales Hosting statischer Websites via Content Delivery Networks. Sie bietet zudem Entwürfe für isolierte Netzwerktopologien und ereignisgesteuerte Workflow-Automatisierung mithilfe von Zustandsmaschinen. Die abgedeckten Funktionen erstrecken sich auf die Verwaltung relationaler Datenbanken, die Konfiguration sicherer Dateiübertragungsserver und die Implementierung fein abgestufter Autorisierung. Zusätzlich demonstrieren die Beispiele Techniken zur Infrastrukturanpassung, wie das Überschreiben von Ressourceneigenschaften und die Integration benutzerdefinierter Ressourcen.
Demonstrates the deployment of containerized applications on managed cloud orchestration services.
This project provides a collection of official base images for building and running .NET applications across various operating systems and hardware architectures. It includes standardized runtime environments, containerized development kits, and specialized images designed for isolated application execution. The collection is distinguished by its focus on image optimization and security hardening. It offers distroless images that remove shells and package managers to reduce the attack surface, as well as composite layering and ahead-of-time compilation to improve startup performance and lower
Enables launching optimized container images into managed cloud-hosting environments for scalable application hosting.
Die Google Cloud Go Client Library ist eine Reihe von Go-Paketen und ein Software Development Kit zur Interaktion mit Google Cloud Platform-Diensten und APIs. Sie bietet das primäre Mittel für Go-Anwendungen, sich über vereinfachte Sprachmuster und automatisierte API-Request-Handhabung in Cloud-Service-Endpunkte zu integrieren. Das Projekt bietet spezialisierte Bibliotheken zur Verwaltung von Identität und Zugriff via Service-Accounts, API-Keys und umgebungsbasierter Credential-Auflösung. Es enthält dedizierte SDKs für die programmatische Ressourcenorchestrierung sowie die Bereitstellung und Verwaltung von serverlosen Funktionen, containerisierten Jobs und skalierbaren Webdiensten. Die Funktionsfläche der Bibliothek deckt Cloud-Datenmanagement über relationale, NoSQL- und Objektspeicher hinweg sowie Infrastrukturorchestrierung für virtuelle Maschinen und Container ab. Sie bietet zudem Tools für die Entwicklung generativer KI, groß angelegte Datenanalysen und Cloud-Anwendungsüberwachung durch Log-Erfassung und Fehlerverfolgung.
Facilitates the deployment and scaling of containerized applications on managed cloud orchestration services.
Practicalnode is a comprehensive educational resource and backend development framework for mastering server-side programming with Node.js. It provides a structured approach to building scalable network services, REST APIs, and real-time applications using asynchronous JavaScript. The project serves as a detailed implementation guide for several core backend patterns, including MongoDB data modeling and the construction of REST API development kits. It emphasizes a specific workflow for Docker containerization and offers a variety of strategies for managing user identity through stateless tok
Provisions and scales cloud-based compute clusters to host and monitor containerized services in production.
This project is a comprehensive library of reference implementations and patterns for building web applications using the Go Fiber framework. It provides curated templates and implementation guides for creating REST APIs, web servers, and structured backend services. The repository serves as a practical resource for applying architectural patterns, including Clean and Hexagonal architectures, as well as port-and-adapter decoupling. It offers detailed examples for integrating common web features such as OAuth2 authentication, JWT verification, WebSockets for real-time communication, and server
Provides configurations and manifests for deploying containerized applications to managed cloud orchestration services.