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jamesstringer90 avatar

jamesstringer90/Easy-GPU-PVArchived

0
View on GitHub↗
5,586 stars·550 forks·PowerShell·13 views

Easy GPU PV

Easy-GPU-PV is an administrative toolset for verifying hardware compatibility and automating the deployment of partitioned graphics acceleration across virtualized Windows environments. It functions as a resource orchestrator and manager for provisioning virtual machines with partitioned graphics processing units.

The project focuses on Windows GPU partitioning, enabling a single physical graphics card to share hardware acceleration across multiple virtualized systems. It achieves this by automating the configuration of host hardware and software to allow virtualized environments to access graphics processing units.

The system manages the end-to-end provisioning process, covering hardware compatibility verification, virtual machine graphics setup, and the synchronization of drivers between host and guest systems. It utilizes registry-driven partitioning and scripted system automation to handle resource allocation and prerequisite validation.

Features

  • Windows GPU Partitioning - Divides a single physical graphics card to share hardware acceleration across multiple virtual machines on Windows.
  • Graphics-Accelerated VM Provisioning - Creates and configures virtualized systems with the necessary drivers and settings for hardware-accelerated graphics.
  • GPU Resource Orchestrators - Functions as a system to dynamically manage, partition, and allocate GPU hardware across virtualized workloads.
  • Automated Provisioning Tools - Provides tools to automate the creation and configuration of virtual machine instances from scratch.
  • Driver Binary Synchronization - Synchronizes graphics driver binaries from the host system to virtualized environments to ensure hardware compatibility.
  • GPU Resource Virtualization - Implements technologies for partitioning physical GPU hardware into multiple virtual devices for shared use.
  • Virtualization Management - Provides an administrative layer for managing the partitioning of physical GPU hardware into virtual slices.
  • Virtualized Driver Synchronization - Synchronizes and updates graphics drivers across host and guest systems to maintain hardware compatibility.
  • Hardware Partitioning Systems - Implements mechanisms for deploying software instances across GPU hardware partitions managed by a hypervisor.
  • Registry-Based GPU Partitioning - Configures graphics resource allocation by manipulating low-level Windows registry entries.
  • Tooling Prerequisite Validations - Validates that system hardware capabilities and software versions meet the requirements for virtualization.
  • System Configuration Scripts - Uses PowerShell scripts and registry edits to configure Windows features for hardware virtualization.
  • Hardware Compatibility Checkers - Verifies system hardware and software against requirements for GPU-accelerated virtualization.

Star history

Star history chart for jamesstringer90/easy-gpu-pvStar history chart for jamesstringer90/easy-gpu-pv

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does jamesstringer90/easy-gpu-pv do?

Easy-GPU-PV is an administrative toolset for verifying hardware compatibility and automating the deployment of partitioned graphics acceleration across virtualized Windows environments. It functions as a resource orchestrator and manager for provisioning virtual machines with partitioned graphics processing units.

What are the main features of jamesstringer90/easy-gpu-pv?

The main features of jamesstringer90/easy-gpu-pv are: Windows GPU Partitioning, Graphics-Accelerated VM Provisioning, GPU Resource Orchestrators, Automated Provisioning Tools, Driver Binary Synchronization, GPU Resource Virtualization, Virtualization Management, Virtualized Driver Synchronization.

Which projects share features with jamesstringer90/easy-gpu-pv?

Projects with overlapping indexed features include: project-hami/hami — HAMi is a hardware orchestration and virtualization system designed to manage accelerators within Kubernetes. It… justsenger/exhyperv — ExHyperV is a suite of administrative tools designed for managing advanced Hyper-V configurations, specifically… clearml/clearml — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial… fedml-ai/fedml — FedML is a distributed machine learning training library, federated learning framework, and GPU workload orchestrator.… cortexlabs/cortex — Cortex is a Kubernetes-based machine learning infrastructure platform designed for deploying, scaling, and managing… apache/cloudstack — CloudStack is an infrastructure-as-a-service orchestration engine designed to automate the deployment and lifecycle of…

Projects sharing features with Easy GPU PV

These projects share indexed features with Easy GPU PV. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • project-hami/hamiProject-HAMi avatar

    Project-HAMi/HAMi

    3,028View on GitHub↗

    HAMi is a hardware orchestration and virtualization system designed to manage accelerators within Kubernetes. It functions as a device plugin that partitions physical hardware into isolated virtual slices, enabling multiple containers to share a single device through enforced memory limits and compute quotas. The project provides a virtualization manager and a heterogeneous compute scheduler that distributes tasks across diverse accelerator types. It uses packing and topology policies to optimize workload placement and allows for specific hardware targeting using unique device identifiers. T

    Goascendcambriconcncf
    View on GitHub↗3,028
  • justsenger/exhypervJustsenger avatar

    Justsenger/ExHyperV

    4,285View on GitHub↗

    ExHyperV is a suite of administrative tools designed for managing advanced Hyper-V configurations, specifically focusing on GPU partitioning, device passthrough, and virtual network switches. It provides a graphical interface to configure virtual machine resources and optimize hypervisor settings. The project is distinguished by its ability to share physical graphics card resources across multiple virtual machines using paravirtualization and partitioning. It also provides specialized utilities for assigning PCIe devices and USB peripherals directly to guest machines for exclusive access. Th

    C#
    View on GitHub↗4,285
  • clearml/clearmlclearml avatar

    clearml/clearml

    6,740View on GitHub↗

    ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts. The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and

    Python
    View on GitHub↗6,740
  • cortexlabs/cortexcortexlabs avatar

    cortexlabs/cortex

    8,013View on GitHub↗

    Cortex is a Kubernetes-based machine learning infrastructure platform designed for deploying, scaling, and managing models and workloads. It functions as a serverless inference engine and GPU cluster orchestrator, providing the tools necessary to execute real-time, asynchronous, and batch model predictions. The platform utilizes declarative infrastructure-as-code for provisioning model clusters and environments. It optimizes operational costs by elastically scaling CPU and GPU resources through the use of spot instances. The system covers a broad set of operational capabilities, including wo

    Goinfrastructuremachine-learning
    View on GitHub↗8,013
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