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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Run your deep learning workloads on Kubernetes more easily and efficiently.
| Date | Stars |
|---|---|
| 2026-07-31 | 531 |
| 2026-08-02 | 531 |
| 2026-08-06 | 531 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://www.apache.org/licenses/LICENSE-2.0.html)
[](https://github.com/kubedl-io}/kubedl}/actions)
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fkubedl-io%2Fkubedl?ref=badge_shield)
[](https://bestpractices.coreinfrastructure.org/projects/5072)
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<img src="https://user-images.githubusercontent.com/3662775/134578512-a9f29d92-b2e2-4fc4-b7b5-333926c738ab.png" width="400" title="">
</div> <br/>
KubeDL enables deep learning workloads to run on Kubernetes more easily and efficiently.
KubeDL is a [CNCF sandbox](https://www.cncf.io/sandbox-projects/) project.
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<img src="https://v6d.io/_static/cncf-color.svg" width="400" title="">
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## Features
- Support training and inferences workloads (Tensorflow, Pytorch. [Mars](https://github.com/mars-project/mars) etc.)in a single unified controller. Features include advanced scheduling, acceleration using cache, metadata persistentcy, file sync, enable service discovery for training in host network etc.
- Automatically tunes the best configurations for ML model deployment. - [Morphling Github](https://github.com/alibaba/morphling)
- Package and deploy ML Model in container and track the model lineage natively with Kubernentes CRD.
Check the website: https://kubedl.io
<div align="center">
<img src="docs/img/kubedl.png" width="700" title="">
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## Getting Involved
| Platform | Purpose | Estimated Response Time |
|-------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-------------------------|
| [DingTalk](https://github.com/kubedl-io/kubedl/blob/master/docs/img/kubedl-dingtalk.png ) | For discussions about development and questions about usage. | < 1 day |
| [Github Issues](https://github.com/kubedl-io/kubedl/issues) | For reporting bugs and filing feature requests. | < 2 days |
| E-Mail([email protected]) | For discussing specific topics or ask for help from community members/maintainers. | < 3 days |
## Publications
Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving. ACM Socc 2021[link](https://dl.acm.org/doi/10.1145/3472883.3486987)
## License
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fkubedl-io%2Fkubedl?ref=badge_large)
Excerpt of 3,151 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c2b5c7e8b78f8c8d, llm:Repository topics and description: topics include deep-learning, inference, kubernetes, machine-learning, model, scheduling; description: 'Run your deep learning workloads on Kubernetes more easily and efficiently.' README: supports training and inference workloads, unified controller, advanced scheduling, model packaging/deployment with Kubernetes CRDs.
matched fp:c2b5c7e8b78f8c8d, llm:Repository topics and description: topics include deep-learning, inference, kubernetes, machine-learning, model, scheduling; description: 'Run your deep learning workloads on Kubernetes more easily and efficiently.' README: supports training and inference workloads, unified controller, advanced scheduling, model packaging/deployment with Kubernetes CRDs.
matched fp:c2b5c7e8b78f8c8d, llm:Repository topics and description: topics include deep-learning, inference, kubernetes, machine-learning, model, scheduling; description: 'Run your deep learning workloads on Kubernetes more easily and efficiently.' README: supports training and inference workloads, unified controller, advanced scheduling, model packaging/deployment with Kubernetes CRDs.
matched fp:c2b5c7e8b78f8c8d, llm:Repository topics and description: topics include deep-learning, inference, kubernetes, machine-learning, model, scheduling; description: 'Run your deep learning workloads on Kubernetes more easily and efficiently.' README: supports training and inference workloads, unified controller, advanced scheduling, model packaging/deployment with Kubernetes CRDs.
