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6 مستودعات

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

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • loft-sh/vclusterالصورة الرمزية لـ loft-sh

    loft-sh/vcluster

    11,186عرض على 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
    عرض على GitHub↗11,186
  • wasmedge/wasmedgeالصورة الرمزية لـ WasmEdge

    WasmEdge/WasmEdge

    10,665عرض على 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
    عرض على GitHub↗10,665
  • nvidia/isaac-gr00tالصورة الرمزية لـ NVIDIA

    NVIDIA/Isaac-GR00T

    6,222عرض على GitHub↗

    Provides virtualization and containerization for managing GPU workloads.

    Jupyter Notebook
    عرض على GitHub↗6,222
  • gpustack/gpustackالصورة الرمزية لـ gpustack

    gpustack/gpustack

    5,173عرض على GitHub↗

    gpustack هي منصة إدارة مجموعات GPU ومنسق استدلال LLM. تعمل كنظام مركزي لتجميع وتنسيق وحدات معالجة الرسومات عبر الخوادم المحلية وبيئات السحابة، وتعمل كمدير حوسبة غير متجانس لتكوينات الأجهزة والبرامج المتنوعة. يوفر النظام بوابة نشر نموذج ذكاء اصطناعي آمنة تخدم النماذج كخدمات قابلة للتوسع باستخدام مصادقة قائمة على المفاتيح. يتضمن مجدول موارد GPU يوازن أعباء العمل عبر المسرعات وينسق محركات استدلال متعددة لرسم خرائط نماذج ذكاء اصطناعي محددة لأجهزة متوافقة. تغطي المنصة تنسيق المجموعات الشامل، بما في ذلك استعادة الفشل الآلي، ومراقبة الموارد في الوقت الفعلي، وتوسيع نطاق الاستدلال الموزع. يدمج تحسين الأداء من خلال التكميم وفك التشفير التخميني لزيادة الإنتاجية وتقليل زمن الوصول. يتم الحفاظ على تكوينات النظام وحالة المجموعة عبر استمرارية حالة قاعدة بيانات علائقية خارجية.

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

    Python
    عرض على GitHub↗5,173
  • jamesturland/jimsgarageالصورة الرمزية لـ JamesTurland

    JamesTurland/JimsGarage

    4,439عرض على 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
    عرض على GitHub↗4,439
  • checkpoint-restore/criuالصورة الرمزية لـ checkpoint-restore

    checkpoint-restore/criu

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

استكشف الوسوم الفرعية

  • 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.