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
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
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
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
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 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.
Open-source alternatives to jamesstringer90/easy-gpu-pv 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…