Awesome
KubeDL enables deep learning workloads to run on Kubernetes more easily and efficiently.
KubeDL is a CNCF sandbox project.
<div align="left"> <img src="https://v6d.io/_static/cncf-color.svg" width="400" title=""> </div> <br/>Features
- Support training and inferences workloads (Tensorflow, Pytorch. 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
- 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=""> </div> <br/>Getting Involved
| Platform | Purpose | Estimated Response Time |
|---|---|---|
| DingTalk | For discussions about development and questions about usage. | < 1 day |
| Github Issues | For reporting bugs and filing feature requests. | < 2 days |
| E-Mail(cncf-kubedl-maintainers@lists.cncf.io) | 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 2021link
License
