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6 dépôts

Awesome GitHub RepositoriesGPU Workload Virtualization and Containerization

Manages hardware resources with a hypervisor and runs guest OSes or Docker containers for isolation and scalability.

Distinct from GPU Resource Virtualization: Distinct from GPU Resource Virtualization: combines virtualization with containerization for GPU workloads.

Explore 6 awesome GitHub repositories matching operating systems & systems programming · GPU Workload Virtualization and Containerization. Refine with filters or upvote what's useful.

Awesome GPU Workload Virtualization and Containerization GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • loft-sh/vclusterAvatar de loft-sh

    loft-sh/vcluster

    11,186Voir sur GitHub↗

    vcluster is a Kubernetes virtual cluster platform that creates fully isolated Kubernetes environments with dedicated control planes, API servers, and RBAC on shared physical infrastructure. It virtualizes Kubernetes control planes by running them as pods inside a host cluster, as standalone binaries on bare metal or virtual machines, or within Docker containers, providing each tenant their own isolated Kubernetes environment without the overhead of managing separate physical clusters. The platform enables multi-tenant Kubernetes isolation through multiple tenancy models, from shared node pool

    Provides secure kernel-level isolation for workloads using seccomp, cgroups, and namespaces.

    Gocloud-nativehelmk3s
    Voir sur GitHub↗11,186
  • wasmedge/wasmedgeAvatar de WasmEdge

    WasmEdge/WasmEdge

    10,665Voir sur GitHub↗

    WasmEdge is an extensible WebAssembly runtime that executes WebAssembly bytecode in a secure sandbox for cloud, edge, and embedded applications. It functions as a multi-language compiler, compiling applications written in Rust, JavaScript, Go, and Python into WebAssembly bytecode for sandboxed execution, and as a server-side JavaScript runtime that runs JavaScript programs with ES6 modules, NPM packages, and Node.js-compatible APIs. The runtime also serves as an AI inference runtime, executing AI models from JavaScript using WASI-NN plug-ins for inference tasks on personal devices and edge har

    Orchestrates large language model inference tasks on GPU hardware within a Kubernetes cluster.

    C++artificial-intelligencecloudcloud-native
    Voir sur GitHub↗10,665
  • nvidia/isaac-gr00tAvatar de NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Voir sur GitHub↗

    Provides virtualization and containerization for managing GPU workloads.

    Jupyter Notebook
    Voir sur GitHub↗6,222
  • gpustack/gpustackAvatar de gpustack

    gpustack/gpustack

    5,173Voir sur GitHub↗

    gpustack est une plateforme de gestion de cluster GPU et un orchestrateur d'inférence LLM. Il fonctionne comme un système centralisé pour mettre en commun et orchestrer les unités de traitement graphique à travers les serveurs locaux et les environnements cloud, servant de gestionnaire de calcul hétérogène pour diverses configurations matérielles et logicielles. Le système fournit une passerelle de déploiement de modèle IA sécurisée qui sert les modèles en tant que services évolutifs en utilisant une authentification basée sur des clés. Il inclut un planificateur de ressources GPU qui équilibre les charges de travail à travers les accélérateurs et coordonne plusieurs moteurs d'inférence pour mapper des modèles IA spécifiques à un matériel compatible. La plateforme couvre une orchestration de cluster complète, y compris la récupération automatique en cas de défaillance, la surveillance des ressources en temps réel et la mise à l'échelle de l'inférence distribuée. Elle intègre l'optimisation des performances par la quantification et le décodage spéculatif pour maximiser le débit et réduire la latence. Les configurations système et l'état du cluster sont maintenus via la persistance de l'état de la base de données relationnelle externe.

    Coordinates and deploys inference engines like vLLM and SGLang to serve AI models.

    Python
    Voir sur GitHub↗5,173
  • jamesturland/jimsgarageAvatar de JamesTurland

    JamesTurland/JimsGarage

    4,439Voir sur GitHub↗

    JimsGarage is a collection of shell scripts and automation tools designed to help individuals deploy and manage a wide range of self-hosted services on their own hardware. It provides a structured approach to setting up containerized applications, from media servers and document management systems to VPNs and monitoring stacks, all through automated Docker-based configurations. The project distinguishes itself by offering a comprehensive library of deployment recipes that cover the full lifecycle of a home server environment. This includes not just the services themselves, but also the suppor

    Provides scripts to create toolbox containers with GPU access for running compute workloads.

    Shell
    Voir sur GitHub↗4,439
  • checkpoint-restore/criuAvatar de checkpoint-restore

    checkpoint-restore/criu

    3,697Voir sur GitHub↗

    CRIU is a Linux process checkpointing tool and state manager used to freeze running applications and save their memory and state to disk for later restoration. It functions as a container migration engine and an OCI checkpoint image converter, allowing the live state of running containers to be transferred between different hosts. The project distinguishes itself through its ability to persist network connectivity, acting as a TCP connection state persister that saves and reconstructs network socket states to maintain active communication after a restart. It further enables the distribution o

    Captures and restores the state of applications using GPU acceleration to enable failure recovery and workload migration.

    Cblcrcheckpointcontainer
    Voir sur GitHub↗3,697
  1. Home
  2. Operating Systems & Systems Programming
  3. GPU Resource Virtualization
  4. GPU Workload Virtualization and Containerization

Explorer les sous-tags

  • GPU Workload CheckpointingCapturing and restoring the execution state of GPU-accelerated applications for migration or recovery. **Distinct from GPU Workload Virtualization and Containerization:** Distinct from general GPU virtualization; focuses specifically on saving and restoring the runtime state (checkpointing).
  • Kernel-Level Hardware IsolationProvides secure isolation for hardware-accelerated workloads using kernel primitives to avoid hypervisor overhead. **Distinct from GPU Workload Virtualization and Containerization:** Focuses on using kernel-level security for high-performance direct hardware access, whereas general GPU Virtualization often involves hypervisors.
  • LLM Inference OrchestrationOrchestrating large language model inference tasks on GPU hardware within a Kubernetes cluster. **Distinct from GPU Workload Virtualization and Containerization:** Distinct from general GPU Workload Virtualization and Containerization: focuses specifically on orchestrating LLM inference tasks, not general GPU workload management.