kuberay-project/kuberay
A toolkit to run Ray applications on Kubernetes
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 42 days ago, with 281 contributors on GitHub.
Gained 7% more stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a Kubernetes operator to deploy and manage Ray applications efficiently on Kubernetes clusters.
- Simplifies lifecycle management of Ray clusters with autoscaling, fault tolerance, and zero-downtime upgrades.
- Use for running distributed machine learning workloads with automated cluster creation and job submission.
- Use for serving large-scale AI models with high availability and seamless Ray Serve deployment upgrades.
- Use for integrating Ray workloads into Kubernetes ecosystems with observability and queuing system support.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
KubeRay is a powerful, open-source Kubernetes operator that simplifies the deployment and management of Ray applications on Kubernetes. It offers several key components:
KubeRay core: This is the official, fully-maintained component of KubeRay that provides three custom resource definitions, RayCluster, RayJob, and RayService. These resources are designed to help you run a wide range of workloads with ease.
KubeRay ecosystem: Some optional components.
common workflows when deploying Ray on Kubernetes. If you aren’t familiar with Kubernetes, this plugin simplifies running Ray on Kubernetes. See kubectl-plugin for more details.
by some organizations to back user interfaces for KubeRay resource management. See KubeRay APIServer V2 for more details.
While it is not yet production-ready, we welcome your feedback. See KubeRay Dashboard for more details.
From September 2023, all user-facing KubeRay documentation will be hosted on the Ray documentation. The KubeRay repository only contains documentation related to the development and maintenance of KubeRay.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
901+81
Open issues
740+29
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| onnx/onnx | 21.6K | — | 63 | 31 days ago |
| mlc-ai/web-llm | 19.3K | — | 59 | 16 days ago |
| lukas-blecher/LaTeX-OCR | 16.6K | — | 18 | — |
| milesial/Pytorch-UNet | 11.7K | — | 18 | — |
| SharpAI/DeepCamera | 3.1K | — | 31 | 227 days ago |
| kuberay-project/kuberay | 2.8K | — | 63 | 42 days ago |
| Category median | 16.6K | — | 31 | — |
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