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6 repository-uri

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

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • loft-sh/vclusterAvatar loft-sh

    loft-sh/vcluster

    11,186Vezi pe 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
    Vezi pe GitHub↗11,186
  • wasmedge/wasmedgeAvatar WasmEdge

    WasmEdge/WasmEdge

    10,665Vezi pe 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
    Vezi pe GitHub↗10,665
  • nvidia/isaac-gr00tAvatar NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Vezi pe GitHub↗

    Provides virtualization and containerization for managing GPU workloads.

    Jupyter Notebook
    Vezi pe GitHub↗6,222
  • gpustack/gpustackAvatar gpustack

    gpustack/gpustack

    5,173Vezi pe GitHub↗

    gpustack este o platformă de gestionare a clusterelor GPU și un orchestrator de inferență LLM. Acesta funcționează ca un sistem centralizat pentru pooling-ul și orchestrarea unităților de procesare grafică pe servere locale și medii cloud, servind drept manager de calcul eterogen pentru diverse configurații hardware și software. Sistemul oferă un gateway securizat de implementare a modelelor AI care servește modelele ca servicii scalabile folosind autentificarea bazată pe chei. Include un scheduler de resurse GPU care echilibrează sarcinile de lucru pe acceleratoare și coordonează mai multe motoare de inferență pentru a mapa modele AI specifice pe hardware compatibil. Platforma acoperă orchestrarea cuprinzătoare a clusterelor, inclusiv recuperarea automată în caz de eșec, monitorizarea resurselor în timp real și scalarea inferenței distribuite. Încorporează optimizarea performanței prin cuantizare și decodare speculativă pentru a maximiza throughput-ul și a reduce latența. Configurațiile sistemului și starea clusterului sunt menținute prin persistența stării într-o bază de date relațională externă.

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

    Python
    Vezi pe GitHub↗5,173
  • jamesturland/jimsgarageAvatar JamesTurland

    JamesTurland/JimsGarage

    4,439Vezi pe 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
    Vezi pe GitHub↗4,439
  • checkpoint-restore/criuAvatar checkpoint-restore

    checkpoint-restore/criu

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

Explorează sub-etichetele

  • 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.