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158 Repos

Awesome GitHub RepositoriesCloud Infrastructure Deployment

Tools for deploying applications to managed cloud infrastructure.

Distinguishing note: Focuses on containerization and orchestration for cloud scaling.

Explore 158 awesome GitHub repositories matching devops & infrastructure · Cloud Infrastructure Deployment. Refine with filters or upvote what's useful.

Awesome Cloud Infrastructure Deployment GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • joaomdmoura/crewaiAvatar von joaomdmoura

    joaomdmoura/crewai

    53,752Auf GitHub ansehen↗

    CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

    Supports deployment across cloud environments and local data centers to meet security requirements.

    Python
    Auf GitHub ansehen↗53,752
  • streamlit/streamlitAvatar von streamlit

    streamlit/streamlit

    44,982Auf GitHub ansehen↗

    Streamlit is a Python framework designed to transform data scripts into interactive web applications. It utilizes a reactive execution engine that automatically reruns scripts from top to bottom whenever a user interaction triggers a state change, ensuring the interface remains synchronized with the underlying data. By providing a declarative interface, it allows developers to build functional applications without requiring extensive knowledge of frontend web technologies. The framework distinguishes itself through an identity-based widget reconciliation system that persists user input across

    Publishes applications to managed hosting services using containerization and orchestration tools.

    Pythondata-analysisdata-sciencedata-visualization
    Auf GitHub ansehen↗44,982
  • lightning-ai/lightningAvatar von lightning-AI

    lightning-AI/lightning

    31,189Auf GitHub ansehen↗

    Lightning is a PyTorch training framework and distributed AI training orchestrator designed to decouple core research logic from the engineering boilerplate required for model training. It functions as a deep learning workflow manager that automates the process of pretraining and finetuning models across diverse compute environments. The project distinguishes itself by providing a hardware-agnostic training wrapper, allowing the same model code to execute on CPUs, GPUs, or TPUs without modification. It further manages the scaling of workloads from single devices to multi-node clusters and ser

    Runs training jobs on cloud GPUs with integrated autoscaling and monitoring.

    Python
    Auf GitHub ansehen↗31,189
  • langfuse/langfuseAvatar von langfuse

    langfuse/langfuse

    29,190Auf GitHub ansehen↗

    Langfuse is an open-source observability and evaluation platform designed for language model applications. It provides a centralized system for tracking execution traces, monitoring performance metrics, and managing prompt templates. By capturing hierarchical units of work and telemetry data, the platform enables developers to debug complex application lifecycles and analyze token usage, latency, and model interactions in production environments. The platform distinguishes itself through an integrated evaluation framework that allows for systematic benchmarking and automated scoring of model

    Automates the provisioning of compute and storage resources on major cloud platforms.

    TypeScriptanalyticsautogenevaluation
    Auf GitHub ansehen↗29,190
  • heygen-com/hyperframesAvatar von heygen-com

    heygen-com/hyperframes

    28,209Auf GitHub ansehen↗

    Hyperframes is an HTML-to-video rendering engine and composition tool that transforms web layouts and CSS into encoded video files. It functions as a headless browser video pipeline and a distributed video rendering framework, allowing users to create seekable animations and programmatic motion designs using HTML, CSS, and JavaScript. The project differentiates itself as an AI agent video orchestrator, enabling the automation of video scripts and compositions through natural language prompts. It supports distributed video encoding by splitting rendering tasks across multiple serverless functi

    Provides dedicated constructs to set up the cloud topology and provision resources for render handlers.

    TypeScript
    Auf GitHub ansehen↗28,209
  • heartexlabs/label-studioAvatar von heartexlabs

    heartexlabs/label-studio

    27,626Auf GitHub ansehen↗

    Label Studio ist ein Tool für die Annotation verschiedener Datentypen und ein Arbeitsbereich für Datenannotation, der entwickelt wurde, um Datensätze für das Training von maschinellem Lernen vorzubereiten. Es fungiert als cloud-integrierte Daten-Pipeline, die Rohdaten aus Speichern importiert, den Annotationsprozess verwaltet und Labels in standardisierte Formate exportiert. Die Plattform verfügt über ein Framework zur Integration von Modellen für maschinelles Lernen, das eine Verbindung zu externen Modellservern herstellt. Dies ermöglicht modellgestützte Annotation und aktives Lernen, wodurch das System Vor-Labeling durchführen und Vorhersagen basierend auf menschlichem Feedback verfeinern kann. Die Software bietet Projektmanagement-Tools zur Organisation von Datensätzen und zur Zuweisung von Aufgaben an Benutzer über rollenbasierte Zugriffe. Sie unterstützt verschiedene Datentypen und nutzt speicherunabhängige Speicheradapter, um eine Verbindung zu lokalen Dateisystemen oder Cloud-Speicheranbietern herzustellen. Die Anwendung kann durch manuelle Einrichtung oder One-Click-Deployments auf Cloud-Infrastruktur installiert werden.

    Supports installation onto cloud providers via one-click deployments or manual setup for scalable accessibility.

    TypeScript
    Auf GitHub ansehen↗27,626
  • locustio/locustAvatar von locustio

    locustio/locust

    27,516Auf GitHub ansehen↗

    Locust is a distributed performance testing framework that allows users to define complex system stress scenarios using standard Python code. By modeling concurrent users as classes with weighted tasks and lifecycle hooks, it enables the simulation of realistic user behavior across large-scale environments. The tool functions as a scalable load generator capable of orchestrating traffic across multiple worker nodes to measure system stability and responsiveness under heavy, real-world conditions. The framework is distinguished by its protocol-agnostic architecture, which supports diverse comm

    Enables execution of load testing scenarios on managed cloud infrastructure to simulate large-scale traffic.

    Pythonbenchmarkinghttpload-generator
    Auf GitHub ansehen↗27,516
  • forem/foremAvatar von forem

    forem/forem

    22,726Auf GitHub ansehen↗

    Forem is an open-source platform designed for building and managing technical communities. It functions as a social publishing engine that enables members to share long-form content, participate in threaded discussions, and engage through social interactions. The platform provides tools for organizations to maintain branded profiles, host community hackathons, and facilitate collaborative learning through structured educational tracks. Beyond its social features, Forem integrates advanced capabilities for AI agent workflow orchestration and codebase knowledge graphing. It allows developers to

    Automates the provisioning and deployment of applications to cloud infrastructure using container orchestration.

    Rubycommunitydiscussionfeedback
    Auf GitHub ansehen↗22,726
  • prefecthq/prefectAvatar von PrefectHQ

    PrefectHQ/prefect

    21,640Auf GitHub ansehen↗

    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

    Provisions compute resources for workflow execution.

    Pythonautomationdatadata-engineering
    Auf GitHub ansehen↗21,640
  • cube-js/cubeAvatar von cube-js

    cube-js/cube

    20,251Auf GitHub ansehen↗

    Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orches

    Provisions and manages analytics components within private cloud environments while maintaining centralized control.

    Rustagentic-analyticsagentsai
    Auf GitHub ansehen↗20,251
  • microsoft/onnxruntimeAvatar von microsoft

    microsoft/onnxruntime

    19,347Auf GitHub ansehen↗

    This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

    Executes machine learning models within managed cloud environments and containerized services to scale inference across distributed systems.

    C++ai-frameworkdeep-learninghardware-acceleration
    Auf GitHub ansehen↗19,347
  • kubernetes/kopsAvatar von kubernetes

    kubernetes/kops

    16,631Auf GitHub ansehen↗

    kops is a Kubernetes cluster provisioner and lifecycle manager designed to automate the creation, maintenance, and destruction of production-grade clusters on cloud infrastructure. It functions as a declarative infrastructure manager, synchronizing the live state of a cluster with versioned manifests stored in remote object storage to ensure idempotent operations. The project distinguishes itself by offering comprehensive automation for the entire cluster lifecycle, including high-availability control plane deployment, incremental rolling updates, and automated version upgrades. It also serve

    Generates Terraform configurations from the current cluster state to allow management via external code.

    Gocncfcontainersgo
    Auf GitHub ansehen↗16,631
  • googlecloudplatform/terraformerAvatar von GoogleCloudPlatform

    GoogleCloudPlatform/terraformer

    14,551Auf GitHub ansehen↗

    Terraformer is a reverse engineering tool and infrastructure-to-code generator that transforms existing live cloud resources into declarative configuration files and state manifests. It functions as a cloud infrastructure exporter, allowing users to extract resource metadata from cloud providers to create reproducible infrastructure deployments. The tool specializes in reverse engineering by querying cloud provider APIs to map active resource configurations and translate them into Terraform resource blocks. It supports infrastructure state recovery by reconstructing state files from live envi

    Provides a utility for selecting and importing specific cloud objects into Terraform state and configuration files.

    Goawscloudgcp
    Auf GitHub ansehen↗14,551
  • lemmynet/lemmyAvatar von LemmyNet

    LemmyNet/lemmy

    14,454Auf GitHub ansehen↗

    Lemmy is a self-hosted, federated discussion platform that enables the operation of independent, decentralized social networking servers. By implementing the ActivityPub protocol, it allows autonomous instances to exchange content, synchronize user interactions, and participate in a global, distributed network without centralized control. The platform distinguishes itself through a decoupled architecture that separates the backend API from the frontend, facilitating the development of custom interfaces while maintaining unified user handles and cross-platform communication. It provides granul

    Automates the provisioning of server and database resources to simplify the hosting of independent platforms.

    Rustactivitypubchatfediverse
    Auf GitHub ansehen↗14,454
  • unstructured-io/unstructuredAvatar von Unstructured-IO

    Unstructured-IO/unstructured

    14,019Auf GitHub ansehen↗

    Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

    Provisions private, single-tenant infrastructure within cloud environments to ensure data isolation, regulatory compliance, and restricted network access.

    HTMLdata-pipelinesdeep-learningdocument-image-analysis
    Auf GitHub ansehen↗14,019
  • dask/daskAvatar von dask

    dask/dask

    13,746Auf GitHub ansehen↗

    Dask ist ein Framework für paralleles Rechnen und ein verteilter Task-Scheduler, der darauf ausgelegt ist, Python-Data-Science-Workflows von einzelnen Maschinen auf große Cluster zu skalieren. Es fungiert als Cluster-Ressourcenmanager, der die Berechnungslogik orchestriert, indem Aufgaben und deren Abhängigkeiten als gerichtete azyklische Graphen dargestellt werden. Diese Architektur ermöglicht es dem System, die Verteilung von Workloads auf verfügbare Hardware zu automatisieren und gleichzeitig komplexe Ausführungsanforderungen zu verwalten. Das Projekt zeichnet sich durch eine Lazy-Evaluation-Engine aus, die Datenoperationen verzögert, bis sie explizit angefordert werden, was eine globale Graphoptimierung und effiziente Ressourcenzuweisung ermöglicht. Es integriert speicherbewusstes Data-Spilling, um Systemabstürze bei der Verarbeitung von Datensätzen zu verhindern, die den verfügbaren Speicher überschreiten, und nutzt Task-Graph-Fusion, um Sequenzen von Operationen in einzelne Ausführungsschritte zu kombinieren, wodurch Scheduling-Overhead und Inter-Node-Kommunikation minimiert werden. Die Plattform bietet eine umfassende Oberfläche für die Datenanalyse im großen Maßstab, einschließlich Unterstützung für verteiltes maschinelles Lernen, Integration in das Hochleistungsrechnen und parallele Datenverarbeitung. Sie bietet umfangreiche Werkzeuge für das Cluster-Lebenszyklusmanagement, Performance-Profiling und die Echtzeitüberwachung der Aufgabenausführung. Benutzer können diese Umgebungen über verschiedene Infrastrukturen hinweg bereitstellen, einschließlich lokaler Hardware, Cloud-Anbietern, containerisierten Systemen und Hochleistungsrechner-Clustern.

    Provisions worker nodes across commercial cloud providers to scale data analysis workflows dynamically.

    Pythondasknumpypandas
    Auf GitHub ansehen↗13,746
  • triggerdotdev/trigger.devAvatar von triggerdotdev

    triggerdotdev/trigger.dev

    13,696Auf GitHub ansehen↗

    Trigger.dev is a platform for building durable, event-driven background workflows. It functions as a workflow engine that allows developers to define complex, long-running processes using standard code rather than proprietary configuration languages. By utilizing a durable execution model, the system checkpoints progress, ensuring that tasks can automatically resume from the exact point of failure after a crash or interruption. The platform distinguishes itself through its focus on stateful, multi-step automation and real-time feedback. It supports the orchestration of AI agents and external

    Supports deployment to managed compute platforms using containerized solutions and automated scaling.

    TypeScriptaiai-agent-frameworkai-agents
    Auf GitHub ansehen↗13,696
  • powerlevel9k/powerlevel9kAvatar von Powerlevel9k

    Powerlevel9k/powerlevel9k

    13,428Auf GitHub ansehen↗

    Powerlevel9k is a customizable visual theme and plugin framework for the Zsh shell. It functions as a command line interface enhancer and environment dashboard, providing a configurable layout system for adding informational segments to the left and right sides of the shell prompt. The system tracks development context and version control status, displaying active branches and repository states. It also monitors cloud infrastructure, showing active profiles and cluster contexts, alongside programming language versions and environment data. The prompt includes real-time system status indicato

    Shows active cloud profiles and cluster contexts to help users identify the current target infrastructure.

    Shelleye-candypowerline-fontsterminal
    Auf GitHub ansehen↗13,428
  • aws/aws-cdkAvatar von aws

    aws/aws-cdk

    12,817Auf GitHub ansehen↗

    The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

    Provides a framework for synthesizing code into declarative templates to automate the provisioning and updating of cloud infrastructure.

    TypeScriptawscloud-infrastructurehacktoberfest
    Auf GitHub ansehen↗12,817
  • redpanda-data/redpandaAvatar von redpanda-data

    redpanda-data/redpanda

    12,248Auf GitHub ansehen↗

    Redpanda is a distributed event streaming engine designed to serve as a high-performance, drop-in replacement for existing event-driven architectures. It provides a foundation for building and scaling applications that require reliable data movement, analytical querying, and strict operational compliance across both cloud and self-managed environments. The platform distinguishes itself through a shared-nothing architecture that utilizes thread-per-core execution and a non-blocking asynchronous input/output engine to maximize throughput. It maintains data consistency through a consensus-based

    Provisions high-performance clusters that support existing event-driven applications with Kafka compatibility.

    C++containerscppevent-driven
    Auf GitHub ansehen↗12,248
Vorherige123456…8Nächste
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Unter-Tags erkunden

  • Azure Cloud Resources2 Sub-TagsProvisioning and management of Azure-specific compute and networking resources. **Distinct from Cloud Infrastructure Deployment:** Focuses specifically on Azure's VM and VNet ecosystem rather than general cloud deployment tools.
  • Cloud Infrastructure Provisioners1 Sub-TagTools for managing analytics components within specific cloud provider environments. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on the provisioning of specific cloud-native analytics components.
  • Cross-Infrastructure ProvisioningCapabilities for deploying clusters consistently across diverse environments such as public clouds, private clouds, and bare metal. **Distinct from Cloud Infrastructure Deployment:** Covers the consistency of deployment across heterogeneous substrates, whereas cloud infrastructure deployment focuses on managed cloud services.
  • Durable Function Cloud DeployersIntegrates durable function runtimes into cloud platforms to execute background tasks without managing infrastructure. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on the integration of durable runtimes rather than general container orchestration.
  • Infrastructure Context DisplaysVisual indicators of active cloud profiles and cluster contexts. **Distinct from Cloud Infrastructure Deployment:** Focuses on visualizing the current target infrastructure rather than deploying to it.
  • Infrastructure Resource TransformationsModifies cloud resource definitions programmatically during the deployment process. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on the programmatic modification of resource definitions rather than the deployment process itself.
  • Infrastructure SynthesisAutomatic generation of cloud resource configurations based on source code analysis. **Distinct from Cloud Infrastructure Deployment:** Distinct from general deployment as it specifically synthesizes resource definitions from code analysis
  • Isolation ConfigurationsSettings for enforcing single-tenant resource boundaries and blast-radius protection in cloud environments. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on security-oriented isolation policies rather than general deployment mechanics.
  • Managed Infrastructure Deployment6 Sub-TagsProvisions and configures cloud resources within private accounts to host data processing pipelines. **Distinct from Cloud Infrastructure Deployment:** Focuses on the automated provisioning of private cloud infrastructure for data pipelines, distinct from general container orchestration.
  • Model Deployments4 Sub-TagsDeploying and scaling AI inference models on cloud infrastructure using Kubernetes and orchestration tools. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: specifically targets AI model inference workloads, not general application deployment.
  • Multi-Cluster Cloud Cleanup ToolsTools for removing multi-cluster networking-related cloud resources on AWS, GCP, or OpenStack after uninstallation. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on cleanup of multi-cluster networking resources, not general application deployment.
  • Multi-Cluster Cloud Preparers1 Sub-TagConfiguration of underlying cloud platforms for multi-cluster networking installation. **Distinct from Cloud Infrastructure Deployment:** Distinct from Cloud Infrastructure Deployment: focuses on preparing cloud infrastructure for multi-cluster networking, not general application deployment.