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run-ai

run-ai• Pinned

run-ai: An open-source AI workload and GPU orchestration platform for managing shared AI infrastructure on Kubernetes.

What problem it solves

Organizations sharing GPU infrastructure need a consistent way to run development, training, and inference workloads across Kubernetes environments. They also need centralized scheduling, resource governance, workload orchestration, and visibility across teams and clusters.

Key features

  • AI-aware GPU scheduling — Schedules GPU workloads with fair-share, preemption, bin-packing, priorities, and topology-aware placement.

    Source: README

  • Dynamic GPU allocation — Shares GPU resources among workloads through fractional allocation and dynamic use of available capacity.

    Source: README

  • Multi-cluster management — Centrally manages workloads, resources, policies, and configuration across connected Kubernetes clusters.

    Source: README

  • Workload management and governance — Manages workspaces, training, distributed training, and inference while applying quotas, organizational policies, and role-based access controls.

    Source: README

  • Multiple interfaces and observability — Provides web UI, CLI, and REST API access alongside visibility into clusters, nodes, GPUs, workloads, resource utilization, and platform activity.

    Source: README

Quick start

Copied verbatim from the project's README.

./local-dev.sh                                  # full build + cluster + deploy
./local-dev.sh --skip-build --skip-cluster      # re-deploy only
./local-dev.sh --teardown                       # tear the environment down

Who it may suit

Inference: This may suit platform engineers, Kubernetes administrators, and AI infrastructure teams that need shared GPU scheduling, workload governance, and centralized multi-cluster operations.

Topics to discuss

  • How should multi-cluster GPU policies be modeled for hybrid Kubernetes environments?

  • Which workload types and orchestration integrations should receive priority next?

  • How can teams evaluate GPU sharing and quota policies without disrupting existing workloads?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/dsx-ai-factory/run-ai#readme

ReadmeX opened this community for a trending project; it is not run by the project. Maintainers can claim it from the community page.

github.comhttps://github.com/dsx-ai-factory/run-ai

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