3 repository-uri
Awesome GitHub RepositoriesGPU Resource Optimization
Dynamically allocates GPU nodes across multiple virtual clusters to maximize hardware utilization.
Distinct from Black-Box Maximizers: None of the candidates describe Kubernetes-level GPU node allocation for multi-tenant density.
Explore 3 awesome GitHub repositories matching devops & infrastructure · GPU Resource Optimization. Refine with filters or upvote what's useful.
Awesome GPU Resource Optimization GitHub Repositories
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- loft-sh/vcluster
loft-sh/vcluster
11,186Vezi pe GitHubvcluster 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
Dynamically allocates GPU nodes across hundreds of isolated virtual clusters to maximize hardware utilization.
Gocloud-nativehelmk3s - clearml/clearml
clearml/clearml
6,740Vezi pe GitHubClearML 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
Increases hardware capacity through workload scheduling and fractional GPU management.
Python - vllm-project/aibrix
vllm-project/aibrix
4,882Vezi pe GitHubAibrix is an inference orchestrator designed for scaling, routing, and managing the deployment of large language models across distributed vLLM clusters. It serves as a centralized gateway for load balancing and routing traffic to specific model replicas and versions. The system manages resource efficiency through a GPU cluster autoscaler that adjusts compute instance counts based on real-time request volume. It further optimizes operations by mixing different accelerator types within a single cluster and utilizing a model adapter orchestrator to deploy lightweight parameter adapters on share
Optimizes operational costs by mixing different accelerator types and monitoring hardware health within a single cluster.
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