Kubedl Save
Run your deep learning workloads on Kubernetes more easily and efficiently.
Project README
KubeDL enables deep learning workloads to run on Kubernetes more easily and efficiently.
KubeDL is a CNCF sandbox project.
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
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([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 2021link
License
Open Source Agenda is not affiliated with "Kubedl" Project. README Source: kubedl-io/kubedl
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