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


Its core functionalities include:

  • Support running training and inference workloads on Kubernetes (Tensorflow, Pytorch. Mars etc.)in a single unified controller.
  • Automatically tunes the best container-level configurations before an ML model is deployed as inference services. - Morphling
  • Model lineage and versioning to track the history of a model natively in CRD: when the model is trained using which data and which image, each version of the model, which version is running etc.
  • Enables storing and versioning a model leveraging container images. Each model version is stored as its own image and can later be served with Serving framework.

KubeDL is a CNCF sandbox project.