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Run your deep learning workloads on Kubernetes more easily and efficiently.

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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


Publications

Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving. ACM Socc 2021

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Run your deep learning workloads on Kubernetes more easily and efficiently.

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