Introduction

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.