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KubeDL enables deep learning workloads to run on Kubernetes more easily and efficiently.


  • 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.

KubeDL is a CNCF sandbox project.